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Free Download Transform Classroom Training to E-Learning with Articulate 360 Released 10/2024 With David Anderson MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 5h 15m 6s | Size: 1.34 GB Learn practical techniques for moving from classroom training to elearning while replicating a similar level of interaction and audience engagement with Articulate 360. Course details With traditional classroom training, students benefit from an instructor in the room providing context and feedback that guides them through the learning experience. However, elearning is asynchronous, meaning you won't have the same facilitated group interaction or discussion. So, when you need to transform classroom training into elearning, how do you maintain the richness of an instructor-led experience without an instructor? In this course, learn what you need to know about transforming instructor-led training activities into attention-grabbing interactions that motivate students to lean forward and touch the screen. Instructor David Anderson shows you how to keep viewers engaged with custom designs, multimedia features, and quizzes. Learn how to convert your materials-static content, classroom activities, and PowerPoint content-into elearning materials. Homepage https://www.linkedin.com/learning/transform-classroom-training-to-e-learning-with-articulate-360 Screenshot Rapidgator https://rg.to/file/636a68bead8c0fefeef4b88f849297f4/rurjn.Transform.Classroom.Training.to.ELearning.with.Articulate.360.part1.rar.html https://rg.to/file/b6361cf52b44e83184f8a73192879845/rurjn.Transform.Classroom.Training.to.ELearning.with.Articulate.360.part2.rar.html Fikper Free Download https://fikper.com/FvfgHkfi9L/rurjn.Transform.Classroom.Training.to.ELearning.with.Articulate.360.part2.rar.html https://fikper.com/UvIP3W0T5s/rurjn.Transform.Classroom.Training.to.ELearning.with.Articulate.360.part1.rar.html No Password - Links are Interchangeable

Free Download Tools To Shape A Secure Learning Environment Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.00 GB | Duration: 0h 41m Marching towards Safety and Security What you'll learn Physical Security Measures Emotional and Social Safety Digital Security and Cyber Safety Health and Wellness Requirements A priority and an initiative towards learning. Description Shaping a secure learning environment is critical for several key reasons, each of which directly impacts both student well-being and academic outcomes:1. Student Well-being and Mental HealthEmotional Safety: Students need to feel emotionally secure to engage in learning fully. A safe learning environment minimizes fear of bullying, discrimination, and other emotional stressors, which allows students to focus on their studies.Mental Health Support: Many students face challenges like anxiety, depression, or trauma. A secure environment provides them with the resources, such as counselors or peer support systems, to address these challenges and thrive in school.2. Improved Academic PerformanceFocus on Learning: When students feel safe-both physically and emotionally-they are better able to concentrate on academics. A sense of security helps reduce distractions, enabling them to engage more fully with the curriculum.Higher Attendance and Engagement: A secure environment fosters better attendance rates and active parti[beeep]tion. Students are more likely to come to school and engage in class activities when they feel safe and supported.3. Prevention of Bullying and ViolenceReduction of Bullying: A secure learning environment helps prevent bullying by establishing a culture of respect, inclusivity, and clear behavioral expectations. Anti-bullying policies and support systems make it easier to address issues before they escalate.Physical Safety: Implementing physical safety measures, such as controlled access and emergency protocols, ensures that students are protected from external threats or internal violence, contributing to an overall sense of security.4. Support for Diverse Learning NeedsInclusive Environment: Students with different backgrounds, abilities, and learning styles need a safe and inclusive environment to thrive. Schools that prioritize security and inclusivity reduce the risk of students feeling marginalized or unsupported.Focus on Equity: A secure environment allows for equitable access to learning, ensuring that students with special educational needs, language barriers, or disabilities receive the support they need to succeed.5. Fostering Positive BehaviorSocial and Emotional Development: A secure environment encourages positive social interactions and emotional development. By teaching conflict resolution, empathy, and communication skills, students learn how to manage their emotions and build healthy relationships.Discipline through Positive Reinforcement: Secure environments often emphasize positive reinforcement over punitive measures, promoting good behavior and reducing disciplinary issues.6. Parental Trust and EngagementBuilding Confidence: Parents need to trust that their children are in a safe environment. When schools invest in security measures, mental health resources, and open communication, parents are more likely to feel confident in the school's ability to care for their children.Increased Collaboration: A secure environment fosters stronger relationships between parents and the school. Engaged parents are more likely to parti[beeep]te in school activities, support learning at home, and collaborate with teachers on their child's development.7. Preparation for the FutureLife Skills Development: Students in secure environments learn essential life skills, such as teamwork, problem-solving, and emotional regulation, which are critical for success in the real world.Leadership and Responsibility: Secure environments often encourage students to take on leadership roles, parti[beeep]te in peer mentoring, or contribute to decision-making processes, helping them develop a sense of responsibility and agency.8. Prevention of Cyber ThreatsDigital Safety: As students increasingly use technology for learning, ensuring their safety online is vital. Cyberbullying, inappropriate content, and data privacy concerns can all impact a student's sense of security. Schools need to adopt tools to protect students in digital spaces.Technology Education: Teaching students about cybersecurity, responsible use of technology, and online privacy can help them become more aware of potential threats and how to navigate the digital world safely.9. Building a Positive School CultureSense of Belonging: A secure environment fosters a sense of community and belonging among students, teachers, and staff. This sense of unity enhances school pride and creates a positive learning atmosphere where everyone feels valued.Cultural Sensitivity and Diversity: In today's diverse world, schools must create an environment that respects cultural differences. By promoting inclusivity and sensitivity, schools can prevent discrimination and encourage understanding.10. Compliance with Legal and Ethical StandardsRegulatory Compliance: Schools are legally obligated to provide a safe environment for students. By implementing safety protocols, bullying prevention strategies, and mental health support, schools not only fulfill legal requirements but also meet ethical obligations to protect their students.Duty of Care: Educators and administrators have a moral responsibility to ensure that students are safe and secure. This duty of care extends to both physical safety and emotional well-being.Conclusion:A secure learning environment is essential because it lays the foundation for effective teaching and learning. It ensures that students feel safe, valued, and supported, allowing them to thrive academically, socially, and emotionally. Investing in security measures, mental health support, and positive behavior systems benefits not only students but also teachers, parents, and the broader community, creating a school culture that promotes success and well-being for all. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Physical Security Measures Lecture 3 Emotional and Social Safety Lecture 4 Digital Security and Cyber Safety Lecture 5 Health and Wellness Lecture 6 Anti-Bullying Initiatives Lecture 7 Comprehensive Guide: Digital Security and Cyber Safety Tools for Schools Lecture 8 Conclusion Heads of Schools/ Educators/ Teachers/ Parents Screenshot Homepage https://www.udemy.com/course/tools-to-shape-a-secure-learning-environment/ Rapidgator https://rg.to/file/06959697b4173baf93bd78434154e09b/fmxct.Tools.To.Shape.A.Secure.Learning.Environment.part1.rar.html https://rg.to/file/703d8d727460dffe6f6b667546a8ad38/fmxct.Tools.To.Shape.A.Secure.Learning.Environment.part2.rar.html Fikper Free Download https://fikper.com/a2LPfXa9LP/fmxct.Tools.To.Shape.A.Secure.Learning.Environment.part2.rar.html https://fikper.com/ubV7FKwgbO/fmxct.Tools.To.Shape.A.Secure.Learning.Environment.part1.rar.html No Password - Links are Interchangeable

Free Download Professional Certificate in Machine Learning by Academy of Computing & Artificial Intelligence Last updated 8/2023 Created by Academy of Computing & Artificial Intelligence MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 196 Lectures ( 24h 0m ) | Size: 10.4 GB Learn all the skills to become a Data Scientist & Build 500+ Artificial Intelligence Projects with source What you'll learn Machine Learning -[A -Z] Comprehensive Training with Step by step guidance Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, SVM, Random Forest) Unsupervised Learning - Clustering, K-Means clustering Data Pre-processing - Data Preprocessing is that step in which the data gets transformed, or Encoded Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices, Deep Convolutional Generative Adversarial Networks (DCGAN) Java Programming For Data Scientists Python Programming Basics For Data Science Algorithm Analysis For Data Scientists Requirements Computer & Internet Connection Description Academy of Computing & Artificial Intelligence proudly presents you the course "Professional Certificate in Data Mining & Machine Learning".mIt all started when the expert team of The Academy of Computing & Artificial Intelligence[ACAI] (PhD, PhD Candidates, Senior Lecturers , Consultants , Researchers) and Industry Experts . hiring managers were having a discussion on the most highly paid jobs & skills in the IT/Computer Science / Engineering / Data Science sector in 2023. To make the course more interactive, we have also provided a live code demonstration where we explain to you how we could apply each concept/principle[Step by step guidance]. Each & every step is clearly explained.[Guided Tutorials]"While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides."Course Learning OutcomesTo provide a solid awareness of Supervised & Unsupervised learning coming under Machine LearningExplain the appropriate usage of Machine Learning techniques.To build appropriate neural models from using state-of-the-art python framework.To build neural models from scratch, following step-by-step instructions. To build end - to - end effective solutions to resolve real-world problems To critically review and select the most appropriate machine learning solutionspython programming is also inclusive. RequirementsA computer with internet connectionPassion & commitment At the end of the Course you will gain the following # Learn to Build 500+ Projects with source code# Strong knowledge of Fundamentals in Machine Learning# Apply for the Dream job in Data Science # Gain knowledge for your University ProjectSetting up the Environment for Python Machine LearningUnderstanding Data With Statistics & Data Pre-processing Data Pre-processing - Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate SelectionData Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..Artificial Neural Networks with Python, KERASKERAS Tutorial - Developing an Artificial Neural Network in Python -Step by StepDeep Learning -Handwritten Digits Recognition[Step by Step][Complete Project ]Naive Bayes Classifier with Python[Lecture & Demo]Linear regressionLogistic regressionIntroduction to clustering[K - Means Clustering ]K - Means ClusteringWhat if you have questions?we offer full support, answering any questions you have.There's no risk !Who this course is for:Anyone who is interested of Data Mining & Machine Learning Who this course is for Anyone who wish to start a career in Machine Learning Homepage https://www.udemy.com/course/professional-certificate-in-machine-learning/ Screenshot Rapidgator https://rg.to/file/44338ac9b43e2e3fbffb95fb848e1f7e/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part04.rar.html https://rg.to/file/4c677d82833ded2969facf2d47edf70b/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part11.rar.html https://rg.to/file/59dc6ecf75505bdda6ccb4e62dbe769d/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part01.rar.html https://rg.to/file/74b12b1a170477995af3289ad06a1bc4/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part03.rar.html https://rg.to/file/778c772d54aec66fb567e80ed3455b7a/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part06.rar.html https://rg.to/file/9db26f85502fcad90ef9580955a96894/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part10.rar.html https://rg.to/file/a32b2645c18149c60b7b64f40e72af3c/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part09.rar.html https://rg.to/file/a3364ad14c53e88b134955c253be321a/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part08.rar.html https://rg.to/file/b6ef64873e577f6d9f655e27c3e08c14/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part05.rar.html https://rg.to/file/be5742808ab0e336f4d737fc7553a5b8/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part07.rar.html https://rg.to/file/f5801d8aa8573cf7bc9e078d51866b78/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part02.rar.html Fikper Free Download https://fikper.com/28F1kUfxBZ/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part07.rar.html https://fikper.com/48Fq85TagM/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part06.rar.html https://fikper.com/E8VXryBL2Q/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part11.rar.html https://fikper.com/FucbYSw7nc/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part05.rar.html https://fikper.com/LO4XigTEML/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part02.rar.html https://fikper.com/MAabHB1zCF/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part03.rar.html https://fikper.com/NaNOJc6Axf/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part04.rar.html https://fikper.com/cHtSRd0cRw/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part08.rar.html https://fikper.com/hBKcV4Vo4l/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part10.rar.html https://fikper.com/mB9UfHJjCd/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part01.rar.html https://fikper.com/qSmfZVfAlT/vouxy.Professional.Certificate.in.Machine.Learning.by.Academy.of.Computing..Artificial.Intelligence.part09.rar.html No Password - Links are Interchangeable

Free Download Master Simplified Supervised Machine Learning™ Published 10/2024 Created by Dr. F.A.K. Noble MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 30 Lectures ( 14h 22m ) | Size: 7.6 GB A Beginner-to-Advanced Deep MasterClass with Real Life Project Application What you'll learn Introduction to Machine Learning: Understand the basics and core concepts of machine learning. Machine Learning - Reinforcement Learning: Learn how agents make decisions by interacting with their environment. Introduction to Supervised Learning: Explore how models are trained on labeled data to make predictions. Machine Learning Model Training and Evaluation: Learn techniques for training models and evaluating their performance. Machine Learning Linear Regression: Master how to predict continuous outcomes using linear regression. Machine Learning - Evaluating Model Fit: Learn how to assess model accuracy and fit for regression tasks. Application of Machine Learning - Supervised Learning: Apply supervised learning techniques to solve practical problems. Introduction to Multiple Linear Regression: Understand how multiple predictors influence outcomes in regression models. Multiple Linear Regression - Evaluating Model Performance: Learn how to assess and optimize multiple linear regression models. Machine Learning Application - Multiple Linear Regression: Apply multiple linear regression to real-world datasets. Machine Learning Logistic Regression: Learn how to perform classification tasks using logistic regression. Machine Learning Feature Engineering - Logistic Regression: Master techniques to improve logistic regression with feature engineering. Machine Learning Application - Logistic Regression: Apply logistic regression to practical classification problems. Machine Learning Decision Trees: Learn how decision trees split data to make predictive decisions. Machine Learning - Evaluating Decision Trees Performance: Discover how to assess the accuracy and reliability of decision trees. Machine Learning Application - Decision Trees: Apply decision tree algorithms to real-world datasets. Machine Learning Random Forests: Understand how random forests combine multiple decision trees for robust predictions. Master Machine Learning Hyperparameter Tuning: Learn advanced techniques for optimizing model performance through hyperparameter tuning. Machine Learning Decision Trees Random Forest: Explore how random forests enhance decision tree performance. Master Machine Learning - Support Vector Machines (SVM): Learn how SVMs are used for classification by maximizing margin separation. Master Machine Learning - Kernel Functions in Support Vector Machines (SVM): Understand how kernel functions improve SVM classification of non-linear data. Machine Learning Application - Support Vector Machines (SVM): Apply SVM algorithms to classify complex datasets. Machine Learning K-Nearest Neighbor (KNN) Algorithm: Learn how KNN uses neighbors to classify data points. Machine Learning Preprocessing for KNN Algorithm: Master data preprocessing techniques to improve KNN performance. Machine Learning Application - KNN Algorithm: Apply the KNN algorithm to solve classification problems. Machine Learning Gradient Boosting Algorithm: Learn how gradient boosting improves prediction accuracy through iterative training. Master Hyperparameter Tuning in Machine Learning: Learn to fine-tune model hyperparameters for maximum performance. Machine Learning Application of Gradient Boosting: Apply gradient boosting to enhance model accuracy in real-world scenarios. Machine Learning Model Evaluation Metrics: Understand key metrics like accuracy and F1-score for evaluating machine learning models. Machine Learning ROC Curve and AUC Explained: Learn to interpret ROC curves and AUC for assessing classification models. Requirements Anyone can learn this class it is very simple. Description Supervised Machine Learning: Mastering Predictive ModelsThis course provides a deep dive into the fundamental concepts and techniques of supervised machine learning. You will learn how to build, train, and evaluate predictive models to solve real-world problems.Introduction to Machine Learning: Explore the principles of machine learning and its applications.Reinforcement Learning: Understand the role of reinforcement learning and its distinction from supervised learning.Introduction to Supervised Learning: Gain insights into how models are trained using labeled data.Model Training and Evaluation: Learn the process of model training, including performance evaluation techniques.Regression Models and Performance OptimizationLinear Regression: Discover how linear regression is used to model continuous outcomes.Evaluating Model Fit: Master techniques to evaluate and refine regression models for better performance.Multiple Linear Regression: Dive into modeling with multiple variables, extending linear regression capabilities.Logistic Regression: Understand classification tasks using logistic regression, with a focus on feature engineering and model interpretation.Advanced Decision-Making AlgorithmsDecision Trees: Learn how decision trees create intuitive, tree-like structures for classification and regression tasks.Evaluating Decision Tree Performance: Explore methods to evaluate decision trees for accuracy and generalization.Random Forests: Understand ensemble learning through random forests and how they improve model robustness.Advanced Techniques and Hyperparameter TuningSupport Vector Machines (SVM): Learn how SVMs optimize classification tasks, including the use of kernel functions for non-linear data.K-Nearest Neighbor (KNN) Algorithm: Explore the KNN algorithm and its preprocessing requirements for optimal performance.Gradient Boosting: Master this powerful ensemble technique that iteratively improves model accuracy.Hyperparameter Tuning: Discover advanced strategies to tune hyperparameters for improved model performance.Model Evaluation and MetricsModel Evaluation Metrics: Grasp key metrics such as accuracy, precision, recall, and F1-score for model evaluation.ROC Curve and AUC Explained: Learn how to use ROC curves and AUC scores to evaluate classification model performance. Who this course is for Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert. 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Free Download Master Cluster Analysis And Unsupervised Learning [2024] Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.10 GB | Duration: 5h 27m Learn to Master Cluster Analysis and Unsupervised Learning for Data Science, Data Analysis, and Machine Learning[2024] What you'll learn Master Cluster Analysis and Unsupervised Learning both in theory and practice Master simple and advanced Cluster Analysis models Use K-means Cluster Analysis, DBSCAN, Hierarchical Cluster models, Prin[beeep]l Component Analysis, and more. Evaluate Cluster Analysis models using many different tools Learn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated Simulations Gain Understanding of concepts such as truth, predicted truth or model-based conditional truth Use effective advanced graphical tools to judge models' performance Use the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and Python Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources Requirements Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended Access to a computer with an internet connection Some Python skill is necessary and some experience with the Pandas library is recommended The course only uses costless software Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included Description Welcome to the course Master Cluster Analysis and Unsupervised Learning!Cluster Analysis and Unsupervised learning are one of the most important and defining tasks within machine learning and data science. Cluster Analysis and Unsupervised learning are one of the main methods for data scientists, analysts, A.I., and machine intelligences to create new insights, information or knowledge from data.This course is a practical and exciting hands-on master class video course about mastering Cluster Analysis and Unsupervised Learning.You will be taught to master some of the most useful and powerful Cluster Analysis and unsupervised learning techniques available...You will learn to:Master Cluster Analysis and Unsupervised Learning both in theory and practiceMaster simple and advanced Cluster Analysis modelsUse K-means Cluster Analysis, DBSCAN, Hierarchical Cluster models, Prin[beeep]l Component Analysis, and more.Evaluate Cluster Analysis models using many different toolsLearn advanced Unsupervised and Supervised Learning theory and be introduced to auto-updated SimulationsGain Understanding of concepts such as truth, predicted truth or model-based conditional truthUse effective advanced graphical tools to judge models' performanceUse the Scikit-learn libraries for Cluster Analysis and Unsupervised Learning, supported by Matplotlib, Seaborn, Pandas, and PythonCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resourcesOption: To use the Anaconda Distribution (for Windows, Mac, Linux)Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages - golden nuggets to improve your quality of work life.And much more.This course is an excellent way to learn to master Cluster Analysis and Unsupervised Learning!Cluster Analysis and Unsupervised Learning are considered exploratory types of data analysis and are useful for discovering new information and knowledge. Unsupervised Learning and Cluster Analysis are often viewed as one of the few ways for artificial intelligences and machine intelligences to create new knowledge or data information without human assistance or supervision, so-called supervised learning.This course provides you with the option to use Cloud Computing with the Anaconda Cloud Notebook and to learn to use Cloud Computing resources, or you may use any Python capable environment of your choice.This course is designed for everyone who wants tolearn to Master Cluster Analysis and Unsupervised LearningRequirements:Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedAccess to a computer with an internet connectionSome Python skill is necessary and some experience with the Pandas library is recommendedThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedThis course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Cluster Analysis, and Unsupervised Learning.Enroll now to receive 5+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course! Overview Section 1: Introduction Lecture 1 Overview and Introduction Lecture 2 Setup of the Anaconda Cloud Notebook Lecture 3 Download and installation of the Anaconda Distribution (optional) Lecture 4 The Conda Package Management System (optional) Section 2: Master Cluster Analysis and Unsupervised Learning Lecture 5 Overview Lecture 6 K-Means Cluster Analysis Lecture 7 Auto-updated K-Means Cluster Analysis, introduction and simulation Lecture 8 Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Lecture 9 Four Hierarchical Clustering algorithms Lecture 10 Prin[beeep]l Component Analysis (PCA) Everyone who wants to learn to Master Cluster Analysis and Unsupervised Learning Screenshot Homepage https://www.udemy.com/course/master-cluster-analysis-and-unsupervised-learning/ Rapidgator https://rg.to/file/6e4b156e83fe25ce89da3bfa50e9c74c/xnrri.Master.Cluster.Analysis.And.Unsupervised.Learning.2024.part4.rar.html https://rg.to/file/bdd5db11f2996929ce4187d299349afb/xnrri.Master.Cluster.Analysis.And.Unsupervised.Learning.2024.part1.rar.html https://rg.to/file/d1b3e06274b14f8fa6455e4aed9a5c2c/xnrri.Master.Cluster.Analysis.And.Unsupervised.Learning.2024.part2.rar.html https://rg.to/file/f137b7d0a354c6b0bebd11186165faf4/xnrri.Master.Cluster.Analysis.And.Unsupervised.Learning.2024.part3.rar.html Fikper Free Download https://fikper.com/NgBbfPmt9Z/xnrri.Master.Cluster.Analysis.And.Unsupervised.Learning.2024.part3.rar.html https://fikper.com/OnyJxxkK9z/xnrri.Master.Cluster.Analysis.And.Unsupervised.Learning.2024.part4.rar.html https://fikper.com/lgmo7Ch4Yd/xnrri.Master.Cluster.Analysis.And.Unsupervised.Learning.2024.part1.rar.html https://fikper.com/n84rLXCGLg/xnrri.Master.Cluster.Analysis.And.Unsupervised.Learning.2024.part2.rar.html No Password - Links are Interchangeable

Free Download Machine Learning For Beginners - Sentiment Analyzer Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.99 GB | Duration: 3h 25m Sentiment Analyzer Project - TF & IDF What you'll learn Analyze and Interpret Sentiment: Extract and quantify emotions, sentiments, and opinions from text data using various sentiment analysis techniques. 2. Master Sentiment Analysis Tools: Learn to work with popular libraries (NLTK, spaCy, TensorFlow) and tools (TextBlob, VaderSentiment) for sentiment analysis. Develop NLP Skills: Understand Natural Language Processing (NLP) fundamentals, text preprocessing, and machine learning approaches for sentiment classification. Apply Sentiment Analysis in Real-World Scenarios: Confidently apply sentiment analysis techniques to real-world applications, such as customer feedback analysis Requirements Python programming knowledge is needed to pursue this course Description Sentiment Analysis: Extracting Insights from TextUnlock the power of emotions in text data with Sentiment Analysis. This comprehensive course teaches you to extract, analyze, and quantify sentiments, opinions, and emotions from various text sources.Key Topics:- Fundamentals of Natural Language Processing (NLP)- Sentiment Analysis techniques (rule-based, machine learning, deep learning)- Text preprocessing and feature extraction- Sentiment classification and visualization- Handling sarcasm, irony, and figurative language- Real-world applications (social media, customer feedback, product reviews)Learning Outcomes:- Analyze and interpret sentiments from text data- Master sentiment analysis tools and libraries (NLTK, spaCy, TensorFlow)- Develop NLP skills for text preprocessing and machine learning- Apply sentiment analysis in real-world scenariosTarget Audience:- Data scientists and analysts- NLP enthusiasts- Marketing and customer service professionals- Researchers and academicsChallenges:1. Handling sarcasm, irony, and figurative language2. Dealing with noisy or incomplete data3. Maintaining accuracy across domains4. Handling multilingual text data5. Integrating with existing systemsBy working on a Sentiment Analysis project, you'll gain hands-on experience with NLP, machine learning, and data analysis, while extracting valuable insights from text data.Prerequisites: Basic Python programming skillsJoin this course to unlock valuable insights from text data and drive informed decisions.Thank You and Keep Learning!! Overview Section 1: Introduction Lecture 1 Introduction - What this course is all about Section 2: Sentriment Analyzer Lecture 2 Understand the Project and code for sentiment analyzer Lecture 3 Understand the use of libraries - python Section 3: Understand the thing behind the scene Lecture 4 Understand the term frequency Lecture 5 Understand the DF and IDF Lecture 6 How TF and IDF works under the hood Lecture 7 How CountVectorizer works Lecture 8 Baye's Theorem and It's use in real life Lecture 9 Use Baye's theorem to identify spam mails Lecture 10 Baye's theorem and sentiment analysis Lecture 11 Significance of Training and Test data Lecture 12 fit_transform and transform methods Lecture 13 Save your model and use it in client code Lecture 14 Model with multiple features Lecture 15 Accuracy report Lecture 16 Run your project having multiple features Lecture 17 Important Docs and Artefacts This course is for the beginners in machine learning who want to learn basics without having prior knowledge of Maths Screenshot Homepage https://www.udemy.com/course/machine-learning-for-beginners-sentiment-analyzer/ Rapidgator https://rg.to/file/5046b08d39731e4f284eb4f019f31ca4/eacov.Machine.Learning.For.Beginners..Sentiment.Analyzer.part2.rar.html https://rg.to/file/6e99152c63950ad729ce4db2116aa28e/eacov.Machine.Learning.For.Beginners..Sentiment.Analyzer.part1.rar.html https://rg.to/file/e8418795f160ba5d7eab87c092b20900/eacov.Machine.Learning.For.Beginners..Sentiment.Analyzer.part3.rar.html Fikper Free Download https://fikper.com/12XjrvF2AU/eacov.Machine.Learning.For.Beginners..Sentiment.Analyzer.part1.rar.html https://fikper.com/RZO6wJxiy0/eacov.Machine.Learning.For.Beginners..Sentiment.Analyzer.part3.rar.html https://fikper.com/h8iwZYQ7Lc/eacov.Machine.Learning.For.Beginners..Sentiment.Analyzer.part2.rar.html No Password - Links are Interchangeable

Free Download Linkedin - Learning Wrike Released 10/2024 With David Rivers MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 20m 22s | Size: 191 MB Get up and running quickly with Wrike to manage projects, parti[beeep]nts, tasks, and workflow efficiently. Course details Managing projects and tasks can often be difficult and time-consuming, but luckily there are now tools specifically geared toward project management. In this course, learn how to use Wrike, a powerful project management and team productivity tool that can help you visualize, organize, and oversee project workflow effectively. Join instructor David Rivers as he guides you through the building of a complete project in Wrike, highlighting what its capabilities are and how it can help you manage projects easier and deliver successful outcomes. Homepage https://www.linkedin.com/learning/learning-wrike Welcome to Rapidgator https://rg.to/file/adf9d5ef496906cd844f6224a240a11f/vogmy.Learning.Wrike.rar.html Fikper Free Download https://fikper.com/Or64r8hETC/vogmy.Learning.Wrike.rar.html No Password - Links are Interchangeable

Free Download Linkedin - Learning JDBC (2024) Released 10/2024 With Frank P Moley III MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 2h 3m | Size: 273 MB Learn the skills to effectively integrate and manage data from relational databases like PostgreSQL, Oracle, MySQL, and SQL Server into Java applications using the JDBC API. Course details Whether developers want to build mobile device apps for Android, web-based, or desktop-based applications with the core Java SDK from Oracle, they must contend with the fact that many dynamic applications need to integrate data from a relational database. In this course, Frank Moley helps you get up to speed with the Java Database Connectivity (JDBC) API, showing how to use it to read and manage data from relational databases such as Postgres, Oracle Database, MySQL, and SQL Server in applications programmed with Java. Frank begins by going over key JDBC terminology, the basics of configuring a PostgreSQL database, and how to create the course project. He then provides detailed instructions on how to select and update data, work with transactions, handle exceptions, and more. Homepage https://www.linkedin.com/learning/learning-jdbc-24697410 Screenshot Rapidgator https://rg.to/file/ab94eabdfb5f29623448d452e04cdd6c/vgbqc.Learning.JDBC.2024.rar.html Fikper Free Download https://fikper.com/yAhKDImMhk/vgbqc.Learning.JDBC.2024.rar.html No Password - Links are Interchangeable

Free Download Linkedin - Learning Ansible (2024) Released 10/2024 With Greg Sowell MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 32s | Size: 114 MB Learn the basics of Ansible, the popular open-source tool that provides automation, configuration management, and orchestration all in one. Course details Ansible is a popular open-source tool that provides automation, configuration management, and orchestration all in one. In this course, instructor Greg Sowell introduces Ansible and explains the many reasons that system administrators and DevOps engineers choose to keep Ansible in their IT tool kit. Learn how to install Ansible in different environments and work with hosts, variables, inventories, and playbooks. Practice your new deployment and playbook writing skills with the exercise challenges at the end of each section. Along the way, Greg reviews various use cases and practical examples to highlight how Ansible can solve a variety of real-world problems more effectively and efficiently. Homepage https://www.linkedin.com/learning/learning-ansible-24687086 Screenshot Rapidgator https://rg.to/file/2395337a5812a5087432505b9422e532/xmfgu.Learning.Ansible.2024.rar.html Fikper Free Download https://fikper.com/dxOnKC4YOu/xmfgu.Learning.Ansible.2024.rar.html No Password - Links are Interchangeable

Free Download Learning how to use Canva for planner-makers Published 10/2024 Created by Schaquitta Peacock MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 7 Lectures ( 57m ) | Size: 1 GB Publishing with KDP What you'll learn Learn the Basics Bulding your planner Choosing your color scheme Working Amazon Kindle Direct Publishing Requirements Computer Description Here I will teach you how to use both Canva and KDP. This is a 1-hour course and you will be working on your planner, while also watching this course. Please make sure you make an account with Canva before starting this course because you will need it if you don't. However, if you already have a file that is great. There are a few quizzes in this course as well. You will be doing assignments to make sure you are learning. Overall, this course is sure to help you. Please come and enjoy your time taking my course. About me:My name is Schaquitta Peacock and I'm working on my on novel, but in the meantime, I make and sell planners on KDP. So, I decided to share my knowledge with you and teach you guys how to make and sale your very own planners. I'm from Mississippi and have always been a book-lover of all different types of books. I currently have some unreleased books in the works, that I can't wait to put out. So, please come and enjoy my teaching here on Udemy. While I'm also working on my degree in Elementary Education.Please enjoy and leave me a review telling me if it helps you or not. Who this course is for People wanting to sell and make planners Homepage https://www.udemy.com/course/learning-how-to-use-canva-for-planner-makers/ Screenshot Rapidgator https://rg.to/file/00103fcc9862054acdd4eb10008338bd/drull.Learning.how.to.use.Canva.for.plannermakers.part2.rar.html https://rg.to/file/a160fda682613cc1565d6fdd153d001b/drull.Learning.how.to.use.Canva.for.plannermakers.part1.rar.html Fikper Free Download https://fikper.com/D2aGtNvVXb/drull.Learning.how.to.use.Canva.for.plannermakers.part2.rar.html https://fikper.com/I5wKRLZPKZ/drull.Learning.how.to.use.Canva.for.plannermakers.part1.rar.html No Password - Links are Interchangeable

Free Download Learning about Disaster Recovery for Non-IT Auditors Published 10/2024 Created by Denise Cicchella MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 5 Lectures ( 1h 26m ) | Size: 1.2 GB Understand the Cyber Security and Cyber Threats in your Organization What you'll learn Understand how IT vulnerabilities can affect operations Learn contingency plans for IT systems going down Learning how to prioritize IT risk Understanding the role of alternate site management Requirements No programming experience needed. Students should have basic audit skills. Description In today's dynamic and interconnected business environment, organizations must be prepared to face unexpected disruptions that can impact their operations. In today's environment the risks of cyber attacks bringing down crucial networks is very high. Whether caused by natural disasters, cyberattacks, or system failures, the ability to maintain business continuity and swiftly recover from such events is critical to long-term success. This webinar is designed specifically for non-IT auditors seeking to understand the fundamentals of business continuity planning (BCP) and disaster recovery (DR) processes from an auditing perspective. Parti[beeep]nts will explore key concepts in BCP and DR, including risk assessments, business impact analyses, and the development of recovery strategies. The course will cover how auditors can evaluate the effectiveness of an organization's preparedness measures and identify gaps in their BCP/DR frameworks. Additionally, the session will provide insight into the role non-IT auditors can play in ensuring that business units outside of IT are adequately prepared to respond to crises and resume critical operations.This self-paced course offers auditors the tools and knowledge necessary to assess organizational resilience and ensure business continuity programs are in place and well-structured to mitigate risks. Whether you're new to auditing BCP/DR or looking to deepen your understanding, this webinar provides the essential framework for success. Who this course is for Non-IT Auditors Audit Management C-Suite CIOs Homepage https://www.udemy.com/course/learning-about-disaster-recovery-for-non-it-auditors/ Screenshot Rapidgator https://rg.to/file/d2bff56aa90b05bd98057fd1fbb3b638/hdxkb.Learning.about.Disaster.Recovery.for.NonIT.Auditors.part1.rar.html https://rg.to/file/e562ff205617268661d726027c1d80f8/hdxkb.Learning.about.Disaster.Recovery.for.NonIT.Auditors.part2.rar.html Fikper Free Download https://fikper.com/TQD2wa8tzM/hdxkb.Learning.about.Disaster.Recovery.for.NonIT.Auditors.part1.rar.html https://fikper.com/VB1p5tW0xP/hdxkb.Learning.about.Disaster.Recovery.for.NonIT.Auditors.part2.rar.html No Password - Links are Interchangeable

Free Download Learning Python by by Gaurav Sharma Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.56 GB | Duration: 2h 58m Your Journey Begins: Building Python Skills for Every Level What you'll learn Understand Python Fundamentals: Master Python syntax and core programming concepts, enabling students to write, run, and troubleshoot simple Python programs. File Handling and Data Manipulation: Learn to read and write files, parse HTML, JSON, and XML data, enhancing skills to manage various data formats effectively. Date and Time Management: Acquire techniques to handle and manipulate dates and times in Python, enabling effective management of time-sensitive information. Hands-On Coding Experience Requirements Here are the suggested prerequisites for the Python course: Basic Computer Skills: Familiarity with using a computer, including file management and web browsing. No Prior Programming Experience Required: The course is designed for beginners, so no previous programming knowledge is necessary. Desire to Learn: A willingness to engage with new concepts and practice coding regularly. Access to a Computer: Students should have access to a computer with internet connectivity to install Python and complete coding exercises. These requirements ensure that all students are well-prepared to start their Python learning journey. Description Dive into the world of programming with our exciting Python course, designed for everyone-from absolute beginners to seasoned developers! Python, renowned for its readability and versatility, is the ideal language to kickstart your coding journey. In this course, led by expert instructor Joe Marini, you'll gain a solid foundation in Python that opens doors to endless possibilities.Start by mastering the installation process and getting comfortable with Python's syntax. Joe guides you through creating and running your very first Python program, making complex concepts approachable and fun! As you progress, you'll learn to handle dates and times, read and write files effortlessly, and retrieve and parse web data in formats like HTML, JSON, and XML.What truly sets this course apart are the interactive Code Challenges powered by CoderPad. These engaging coding exercises provide real-time feedback, allowing you to apply your newfound knowledge immediately. With hands-on practice, you'll reinforce your skills and build confidence as you code.Join a vibrant community of learners and elevate your programming prowess today! Whether you dream of developing apps, automating tasks, or diving into data science, this Python course is your gateway to success. Don't miss out on this opportunity-enroll now and embark on an exciting coding adventure that could change your life! Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 What you should know Lecture 3 Exercise File Section 2: 1. Getting Started Lecture 4 Installing Python on Windows Lecture 5 Installing Python on Mac Lecture 6 Choosing an editor or IDE Lecture 7 How to run the Python examples Lecture 8 CoderPad Challenges Section 3: 2. Python Basics Lecture 9 Building Hello World Lecture 10 Variables and expressions Lecture 11 Python functions Lecture 12 Conditional structures Lecture 13 Loops Lecture 14 Classes Lecture 15 Importing and using modules Lecture 16 Using exceptions Section 4: 3. Working with Files Lecture 17 Reading and writing files Lecture 18 Working with OS path utilities Lecture 19 Using filesystem shell methods Section 5: 4. Using Dates and Times Lecture 20 The date, time, and datetime classes Lecture 21 Formatting time output Lecture 22 Using timedelta objects Lecture 23 Working with calendars Section 6: 5. Internet Data Formats Lecture 24 Fetching Internet data Lecture 25 Working with JSON data Lecture 26 Parsing and processing HTML Lecture 27 Manipulating XML Section 7: Conclusion Lecture 28 Where to go next This course is for: Beginner Programmers: Individuals new to coding who want to learn Python from the ground up. Experienced Developers: Those looking to expand their skill set by adding Python to their programming toolkit. Data Enthusiasts: Individuals interested in data manipulation, web scraping, and working with APIs. Students and Professionals: Anyone seeking to enhance their career prospects with Python skills for various fields like data science, web development, and automation. This course caters to a diverse audience, making Python accessible to all who are eager to learn. 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Free Download Learning Path Become A Digital Advertising Specialist Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 7.83 GB | Duration: 18h 27m Master Every Aspect of Digital Advertising, from Targeting to Conversion, and Take Your Marketing to the Next Level What you'll learn Master TikTok Ads Master Snapchat Ads Master Meta Ads Master Google Ads Master LinkedIn Ads Master X Ads Master Reddit Ads Master Pinterest Ads Master Quora Ads Master YouTube Ads Master Instagram Ads Master Microsoft Ads How to create a Sale Page step by step Master Copywriting for Sales Pages and Ad Creatives Requirements No Experience Needed Just Internet and Ad Accounts on Advertising Platforms Description Mastering Digital Advertising: Comprehensive Step-by-Step Guide to All Major PlatformsAre you ready to transform your business with expert-level digital advertising skills? Whether you want to attract new customers, increase conversions, or grow your brand's presence online, this course will equip you with the tools and strategies to achieve those goals. Mastering Digital Advertising is your ultimate guide to running high-performing ad campaigns across the industry's leading platforms, from Meta Ads to Google, Snapchat, TikTok, YouTube, and beyond.With the digital landscape evolving rapidly, mastering the nuances of each advertising platform is crucial. In this comprehensive course, you'll learn how to strategically plan, set up, and optimize ad campaigns across 13 major platforms to maximize your ROI:Meta Ads (Facebook & Instagram): Learn advanced targeting techniques, custom audience creation, A/B testing, and optimization strategies to get the most from your Facebook and Instagram ad spend.Google Ads: Master search and display advertising to dominate search engines. Learn how to target high-intent keywords, structure successful PPC campaigns, and track your conversions with Google Analytics.Snapchat Ads: Engage younger audiences with creative vertical video ads that resonate. Understand how to use Snapchat's unique features to drive brand awareness and conversions.TikTok Ads: Tap into one of the fastest-growing platforms by creating engaging, viral-worthy content. Learn how to optimize TikTok ads for maximum reach and discover its algorithm's ins and outs.Microsoft Ads (Bing Ads): Reach an often-overlooked audience with lower competition. Learn the ins and outs of running search ads on Microsoft's network, where you can often achieve higher ROI due to less ad saturation.YouTube Ads: From in-stream ads to bumpers, you'll discover how to create captivating video content that grabs attention within seconds. Learn targeting, bidding strategies, and how to ensure your message sticks with your audience.LinkedIn Ads: Learn how to reach high-value business audiences through LinkedIn's precise targeting tools. Perfect for B2B marketers, this module will show you how to generate qualified leads and build brand authority.X Ads (formerly Twitter Ads): Leverage X Ads to promote your business, products, or services to an engaged audience. Learn how to use promoted tweets, trends, and advanced targeting for brand exposure and customer engagement.Quora Ads: Advertise where people seek answers. Learn how to create ads that align with specific topics and questions to capture highly relevant traffic and drive conversions.Reddit Ads: Tap into Reddit's unique community-driven ecosystem. Learn how to strategically advertise within niche subreddits and engage with audiences that are passionate about specific topics.Propeller Ads: Dive into performance-driven advertising with push notifications and pop-under ads. Learn how to reach a global audience through Propeller's network and optimize for cost-effective lead generation.Instagram Ads: Build high-converting ad campaigns using Instagram's visual-first platform. Learn to craft ads that inspire and engage users during their browsing sessions.Pinterest Ads: Capture the attention of users during moments of inspiration with Pinterest Ads. Perfect for e-commerce brands, you'll learn how to drive traffic and sales by leveraging Pinterest's shopping-focused platform.Imagine being able to navigate each of these platforms with confidence, running campaigns that consistently deliver strong results. This course offers more than just the basics-it's designed to make you a true expert in every platform, giving you the competitive edge. Not only will you learn the technical setup, but you'll also get insights into advanced strategies that separate successful advertisers from the rest.Beyond mastering ads, you'll gain critical skills in copywriting and sales page optimization:Copywriting That Converts: Discover how to write attention-grabbing headlines, create compelling ad copy, and craft irresistible calls-to-action. Learn the psychology behind persuasion and how to tailor your message to each platform.Sales Page Optimization: Your ads are only as effective as the landing pages they lead to. You'll learn how to design and optimize sales pages that align with your ad campaigns, driving conversions and maximizing revenue. From layout design to persuasive content, we'll show you the best practices for a high-converting funnel.Don't wait-take control of your digital advertising success today! Whether you're just starting out or looking to refine your existing skills, this course is designed to turn you into a digital advertising powerhouse. Each module is packed with actionable strategies, real-world examples, and in-depth tutorials to guide you step-by-step.Enroll now and gain the expertise to run high-performing ad campaigns across all major platforms, drive consistent results, and turn your advertising efforts into powerful engines for growth! Overview Section 1: Introduction Lecture 1 About the Courses Lecture 2 About the Instructor Lecture 3 Glossary Section 2: Master Copywriting Techniques for Sales pages and Ad Copy Lecture 4 AIDA Copywriting Framework Lecture 5 PAS Copywriting Framework Lecture 6 BAB Copywriting Framework Lecture 7 ACCA Copywriting Framework Lecture 8 SOFT Copywriting Framework Lecture 9 The OATH Framework Lecture 10 ARM Copywriting Framework Section 3: Mastering High-Converting Sales Pages A Step-by-Step Guide Lecture 11 Introduction Lecture 12 Sales Pages Headline sub headline offer and hook Lecture 13 Sales Page Call To action Guarantees Lecture 14 Sales page Pop us and Offer Lecture 15 Converting Sales Pages A Step-by-Step Guide PDF Section 4: The Evolution and Future of Digital Advertising From Banner Ads to AI Lecture 16 The definition of Digital Advertising Lecture 17 The importance of Digital Advertising Lecture 18 The Advent of the Internet Lecture 19 The First banner ads Lecture 20 The Rise of Search Engine Advertising Lecture 21 Email Marketing Lecture 22 Social Media Advertising Lecture 23 Mobile and Video Ads Lecture 24 Programmatic Advertising Lecture 25 Data Analytics and Big Data Lecture 26 AI and Machine Learning Lecture 27 AR and VR Lecture 28 Regulations and Ethical Considerations Lecture 29 Ad Fraud and Transparency Lecture 30 Ethical Advertising Practices Lecture 31 Influencer Marketing Lecture 32 Native Ads Lecture 33 Personalized and Interactive ads Lecture 34 The Future of Digital Advertising Lecture 35 Changing Consumer Behavior Lecture 36 PDF: The Evolution and Future of Digital Advertising From Banner Ads to AI Section 5: The Ultimate Digital Advertising Blueprint From Market Research to Campaign Opt Lecture 37 Introduction Lecture 38 Market Research and Segmentation Lecture 39 Define Objectives and KPIs Lecture 40 Creative Strategy Lecture 41 Channel Specific Tactics Lecture 42 Implementation and Monitoring Lecture 43 Optimization and Scaling Lecture 44 Reporting and Evaluation Lecture 45 PDF: The Ultimate Digital Advertising Blueprint Section 6: Mastering Digital Advertising An Expert's Playbook for Strategy Metrics Lecture 46 Overview of Digital Ads Ecosystem Lecture 47 Digital ads Learning Stages or Phases Lecture 48 Key Metrics and Concepts Lecture 49 Testing and Optimization Techniques Lecture 50 Advanced Models and Analysis Lecture 51 Measuring Across Channels Lecture 52 Surveys and Experimental Methods Lecture 53 Tools and Techniques Lecture 54 Creating Creative ad copy and visuals Lecture 55 Additional Considerations Lecture 56 PDF: Mastering Digital Advertising An Expert's Playbook for Strategy Metrics Section 7: Mastering Customer-Centric Strategies A Comprehensive Guide to Segmentation Lecture 57 Introduction Lecture 58 Market Segmentation and Customer Profiling Lecture 59 Touchpoint Mapping and Psychographic Profiling Lecture 60 Buyer Feedback Loop and CRM Lecture 61 Customer Advocacy Program and Loyalty programs Lecture 62 Customer Co creation and customer onboarding Lecture 63 Customer Sentiment Analysis and Customer Journey Lecture 64 Customer Touchpoint optimization Lecture 65 Customer Communication strategies and Human Cent Lecture 66 PDF: Mastering Customer-Centric Strategies A Comprehensive Guide to Segmentation Section 8: Psychology of selling online Lecture 67 Introduction and Types of Consumers Lecture 68 Cognitive Biases Lecture 69 Designing for persuasion Lecture 70 Building Trust and Credibility Lecture 71 Social Proof and Pricing Strategies Lecture 72 PDF: Psychology of selling online Section 9: Master Meta Ads Lecture 73 Why Meta Ads Lecture 74 How To Get Started with Meta Ads Lecture 75 How to Avoid Account suspension Lecture 76 Introducing Meta Ads Dashboard part one Lecture 77 Introducing Meta Ads Dashboard part two Lecture 78 Introducing Meta Ads Dashboard part three Lecture 79 Introducing Meta Ads Dashboard part four Lecture 80 Meta Ads Custom Rules Lecture 81 Business Portfolio Add Ad accounts Lecture 82 Business Portfolio People Lecture 83 Business Portfolio Partners Lecture 84 Business Portfolio System Users Lecture 85 Business Portfolio Pages Lecture 86 Business Portfolio Assign ads accounts Lecture 87 Business Portfolio Business Assets Lecture 88 Business Portfolio Instagram and Whatsapp accou Lecture 89 Business Portfolio Catalogs Lecture 90 Business Portfolio Set up Conversions API and M Lecture 91 Business Portfolio Extra Features Lecture 92 Meta Ads Traffic Campaign Lecture 93 Meta Ads Sales Campaign Lecture 94 Meta Ada Awareness Campaign Lecture 95 Meta Ads Custom Audiences Lecture 96 Meta Ads Engagement Campaign Lecture 97 Meta Ads Payments Lecture 98 Meta Ads Ad Account Setting and Advertising Set Lecture 99 Meta Ads Business Support Home Lecture 100 Meta Ads Advertising Standards or Policy Lecture 101 Meta Ads Instant Forms, Ads Mockups and Page ad Lecture 102 Meta Ads Business Apps Lecture 103 Meta Ads Brand Collabs Manager Lecture 104 Meta Ads Experiments Lecture 105 Meta Ads Library Lecture 106 Meta Ads Certifications Lecture 107 Useful Recourses Section 10: Master YouTube Ads Lecture 108 Why YouTube Ads Lecture 109 Understanding YouTube ads Process Lecture 110 Create a Google Ads account Lecture 111 YouTube Ads Best Practices Lecture 112 One Time YouTube Channel Verification Lecture 113 YouTube Ads Upload Your Ads Video Lecture 114 YouTube Video View Campaign Lecture 115 YouTube Ads Custom Audience Lecture 116 YouTube Ads Video View Campaign Lecture 117 YouTube Ads Keywords Lecture 118 YouTube Ads Topics Placements and Ad Creation Lecture 119 Recourses Section 11: Master Snapchat ads Lecture 120 Why Snapchat ads Lecture 121 How to Get a BC or Business Center Lecture 122 Snapchat ads Terms of Use Lecture 123 How to Know if you ad account got suspended Lecture 124 How to share your public profile on Snapchat Ads Lecture 125 Promote Public Profiles Easily and get many followers Lecture 126 Getting Familiar with Snapchat Ads Dashboard Lecture 127 Getting Familiar with Business Dashboard 2 Lecture 128 Create an Ad Account Lecture 129 Creating Members Lecture 130 Payments and Creator Marketplace Lecture 131 Snapchat Developer Portal and Camera KiT Lecture 132 Snapchat Ad Audiences and Insights Lecture 133 Snapchat Ads Custom Reports Lecture 134 Snapchat Ads Campaigns Lab Lecture 135 Snapchat ads Creative library Lens Maker Lecture 136 Snapchat ads Catalog Sales Lecture 137 Snapchat Pixels and Events Manager Lecture 138 Snapchat Audiences Lecture 139 Snapchat Ad Simple Ad Creation Lecture 140 Snapchat ad creation in Full part 1 Lecture 141 Snapchat ad creation in Full part 2 Discover Lecture 142 Snapchat Ads Collection Ads Lecture 143 Before You Go Lecture 144 Resources Section 12: Master TikTok ads Lecture 145 Why TikTok ads Lecture 146 Video Editing tools for TikTok ads Lecture 147 How to Get a Business Center Lecture 148 Business Center and how to invite members Lecture 149 Let us help you get an ad account Lecture 150 Third Party Ad measurement tools Lecture 151 TikTok Advertising Policies Lecture 152 TikTok Ads Glossary and Terms Lecture 153 Understanding TikTok Business Center Lecture 154 Tiktok ads account access and user security Lecture 155 TikTok Dashboard and Top Ads Lecture 156 TikTok ads manager Analytics Lecture 157 TikTok ads manager Account Setup Lecture 158 TikTok Ads Automated Rules Lecture 159 TikTok ads Catalogs Lecture 160 TikTok ads Events and Pixel Lecture 161 TikTok leads connect to a CRM Lecture 162 Pangle Brand Safety Lecture 163 TikTok Ads Recommendation Center Lecture 164 Video Library or Video Creation Lecture 165 TikTok Ads Instant Page and Lead Gen Forms Lecture 166 TikTok Ads Video Editor Lecture 167 TikTok ads managing Comments Lecture 168 TikTok Custom Audiences Lecture 169 TikTok Campaigns Overview Lecture 170 TikTok ads Campaign Creation part 1 Lecture 171 TikTok ads Campaign Creation part 2 Lecture 172 TikTok ads Authorize a Post Lecture 173 TikTok Ads Reach Campaigns Lecture 174 TikTok ads Followers or Visit Profile Campaigns Lecture 175 TikTok ads Promote Shops Lecture 176 TikTok ads Lead Generation Lecture 177 TikTok ads Website Conversions Lecture 178 TikTok ads Create Ads using your phone Lecture 179 TikTok ads Review your ads on your phone Lecture 180 Become TikTok Media Buyer Certified Lecture 181 Resources Section 13: Master X Ads Lecture 182 Why X ads Lecture 183 Requirements to run ads on X Lecture 184 X Ads Glossary Lecture 185 Optimize your profile on X Lecture 186 Define your target audience Lecture 187 Getting Familiar with the Dashboard Lecture 188 X ads Editor Lecture 189 X ads Events Manager Lecture 190 X ads Custom Audiences Lecture 191 X Ads Shopping Products and Adding your apps Lecture 192 X ads Campaign Creation Lecture 193 X Campaign Creation Keywords and Interests Lecture 194 Campaign Creation Process Lecture 195 X Ads Account Access Lecture 196 Twitter Flight School X Academy Lecture 197 Resources Section 14: Master Google Ads Lecture 198 Why Google Ads Lecture 199 Create Google ads Account Lecture 200 Three Things you must learn about Lecture 201 Introduction to Google Ads Dashboard Lecture 202 Reactivate your Google Ads account Lecture 203 Exploring Admin Area Lecture 204 Google Ads Billing System Lecture 205 Google Ads Conversion Tracking Lecture 206 Google Ads Conversion Troubleshooting Lecture 207 Keywords Lecture 208 Google ads Campaign Overview Lecture 209 Campaign Creation Location and language Lecture 210 Google ads Campaign ads Audience Segments Lecture 211 Broad Match Recommendation and start and end Lecture 212 Google ads campaign keywords and headlines Lecture 213 Google ads Site Links Lecture 214 Google ads Callouts and Promotion Extensions Lecture 215 Google ads Budget Lecture 216 Google ads publish your ad Lecture 217 Google ads Certifications Lecture 218 Resources Section 15: Master Instagram Ads Lecture 219 Why Instagram Ads Lecture 220 Avoid Paying Services Fees Lecture 221 Instagram Ads Three way to promote your business Lecture 222 Instagram Ads Web Version Lecture 223 Instagram In app ads account set up Lecture 224 Instagram Ads Professional Account Lecture 225 Instagram Ads Promote your stories Lecture 226 Instagram In App Campaign Creation Section 16: Master LinkedIn Ads Lecture 227 Why LinkedIn Ads Lecture 228 How to To Access Business Manager Lecture 229 LinkedIn Free Credits and Coupons Lecture 230 LinkedIn Ads Audiences Lecture 231 LinkedIn Ads Brand Safety Lecture 232 LinkedIn Ads AB Test and Brand Lift Test Lecture 233 LinkedIn Ads Analyze Data Sources and Conversion Lecture 234 LinkedIn Ads Install Insight Tag Manager Lecture 235 LinkedIn Ads Managing Assets Lecture 236 LinkedIn Ads Account Access Lecture 237 LinkedIn Ads URL Parameters Lecture 238 LinkedIn Ads Website Traffic Campaign Lecture 239 LinkedIn Ads Reports Lecture 240 Become LinkedIn Certified Advertiser Lecture 241 Resources Section 17: Master Microsoft Ads Lecture 242 Why Microsoft Ads Lecture 243 Sign up For Microsoft Ads Lecture 244 Where your Ads show up Lecture 245 Install Universal Event Tracking Lecture 246 Microsoft Ads Conversion Set up Lecture 247 Campaign Creation part one Lecture 248 Campaign Creation part two Lecture 249 Campaign Creation Part Three Lecture 250 Search Campaign part one Lecture 251 Search Campaign Part Two Lecture 252 Mater Microsoft Ads Bidding Strategy Lecture 253 Microsoft Ads Dashboard Overview Lecture 254 Campaign Level changes Lecture 255 Microsoft Ads Asset Library Lecture 256 Microsoft Ads Reporting Tab Lecture 257 Microsoft Merchant Center Lecture 258 Become a Microsoft Certified Advertiser Lecture 259 Resources Section 18: Master Pinterest Ads Lecture 260 Why Pinterest ads Lecture 261 Pinterest ads Account Review Lecture 262 Pinterest Business Sign Up Lecture 263 Business Manager Adding Members Lecture 264 How to Access Business Center To Run Ads Lecture 265 Business Manager Partners Lecture 266 Business Manager Ad Accounts Lecture 267 Business Manager Profiles Lecture 268 Business Manager Asset Groups Lecture 269 Business Manager Custom Audiences Lecture 270 Business Manager Brand Safety Lecture 271 Business Manager Security Lecture 272 Pinterest Ads Analytics and Pinterest Trends Lecture 273 Pinterest Ads Reports and Custom Reports Lecture 274 Account Recommendation and Account History Lecture 275 Pinterest Ads Create a consideration campaign Lecture 276 Pinterest Exact, Broad, Negative Matches Lecture 277 Pinterest Media Buying Certification Lecture 278 Resources Section 19: Master Propeller Ads Lecture 279 Why Propeller ads Lecture 280 Best Use Case for Propeller Ads Lecture 281 Strategic methods to use when running ads on Pro Lecture 282 How to create a Propeller Account Lecture 283 Propeller ads Dashboard Lecture 284 Understand Propeller Ads Formats Lecture 285 Campaigns Creation Lecture 286 More Features to Campaigns Section 20: Master Quora Ads Lecture 287 Why Quora Ads Lecture 288 How to Access Quora Ads Lecture 289 Quora Ads Reports Lecture 290 Quora Ads Lead Generation forms Lecture 291 Quora Ads Custom Audiences Lecture 292 Quora Ads Install Pixel and Events Lecture 293 Quora Ads Campaign Set Up Lecture 294 Quora Resources Section 21: Master Reddit Ads Lecture 295 Why Reddit Ads Lecture 296 Reddit Ads Dashboard Lecture 297 How to create a campaign on Reddit Lecture 298 Install Reddit Pixel and Audiences Lecture 299 Reddit Audiences Lecture 300 Reddit Ads Custom Reports Lecture 301 Reddit Ads AI Copywriter Lecture 302 Reddit Ads Inventory Type Lecture 303 Reddit Ads Business Details Lecture 304 Reddit Ads Catalog Products Lecture 305 Resources Beginner to Advanced Users Screenshot Homepage https://www.udemy.com/course/learning-path-become-a-digital-advertising-specialist/ Rapidgator https://rg.to/file/279df7f1f256e89cafd34a635c8cb7ae/nibyr.Learning.Path.Become.A.Digital.Advertising.Specialist.part04.rar.html 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Free Download Learning Microsoft 365 Copilot and Business Chat Released 10/2024 With Nick Brazzi MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 29m 49s | Size: 101 MB Enable Microsoft's AI assistant inside of Word, Excel, Outlook, Teams, and PowerPoint through Microsoft 365 Copilot, a powerful add-on for Microsoft 365 Business and Enterprise users. Course details Discover how through Copilot, AI can help you create documents, summarize messages, and analyze data from natural language requests in Word, Excel, Outlook, Teams, and PowerPoint. Staff Instructor Nick Brazzi also introduces Microsoft 365 Business Chat, which allows you to ask questions and make requests using secure data from your calendar, messages, and files. Homepage https://www.linkedin.com/learning/learning-microsoft-365-copilot-and-business-chat Screenshot Rapidgator https://rg.to/file/eea91f17f66ecd6b9c5ee490a3b7ce0a/yhrir.Learning.Microsoft.365.Copilot.and.Business.Chat.rar.html Fikper Free Download https://fikper.com/IFu9R1cibm/yhrir.Learning.Microsoft.365.Copilot.and.Business.Chat.rar.html No Password - Links are Interchangeable

Free Download Learning Disability - Dyslexia Awareness Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 260.28 MB | Duration: 0h 33m Dyslexia | Learning Difficulties | Cognitive Approaches to Dyslexia | Legal Ethics and Considerations What you'll learn An introduction to Dyslexia and key characteristics of this Learning Disability Connections between Dyslexia and other Learning Disabilities Strategies to support learners with Dyslexia in the school environment Overview of cognitive approaches to aid those with Dyslexia Legal and ethical considerations when working with Dyslexia and Learning Disabilities How to create a supportive learning environment for individuals with Dyslexia Requirements There are no prerequisites for this course. It is open to anyone interested in learning more about Dyslexia and Learning Disabilities. Description In the Dyslexia Awareness course, students will gain a beginner-friendly and insightful introduction to Dyslexia as a Learning Disability and its significant impact on both learning processes and educational environments. This self-taught course, delivered through pre-recorded video modules, enables learners to start anytime and progress at their own pace, beginning with zero prior knowledge.Dyslexia is a prevalent Learning Disability that affects many students in various educational settings, often influencing reading, writing, and comprehension skills. Understanding Dyslexia goes beyond just recognizing the symptoms; it involves grasping how this Learning Disability shapes an individual's daily experiences, academic journey, and even social interactions. In this course, students will embark on an educational path designed to build both empathy and practical knowledge, fostering a supportive approach to Learning Disabilities like Dyslexia.The course begins with Module 1, which introduces Dyslexia as a unique Learning Disability and defines its primary characteristics, causes, and types. This module lays the groundwork for a broader understanding of how Dyslexia differentiates itself from other Learning Disabilities. Here, students will learn about the historical context of Dyslexia awareness and the shift in educational approaches over the years. By understanding its background and modern definitions, learners will start seeing Dyslexia through a compassionate, well-informed lens.Module 2 expands on the relationship between Dyslexia and other Learning Disabilities. This section provides essential information on how Dyslexia intersects with other learning challenges, including ADHD, dysgraphia, and dyscalculia. Understanding these intersections allows students to see the broader spectrum of Learning Disabilities and helps them appreciate the diverse needs of those who experience Dyslexia in combination with other conditions. This module emphasizes the importance of customized learning plans to support individuals with multiple Learning Disabilities effectively.In Module 3, the course delves into the specifics of how Dyslexia manifests within school settings and the challenges that students with Dyslexia face in traditional learning environments. This module is essential for parents, teachers, and educational professionals who want to build an accommodating atmosphere in classrooms and schools. We'll cover common signs of Dyslexia in school-aged children, how these signs impact learning, and practical interventions that teachers can use to support these students. By the end of this module, students will have a clear framework for making school a more inclusive space for those with Learning Disabilities like Dyslexia.Module 4 introduces students to cognitive approaches and modern strategies for supporting those with Dyslexia. Here, we explore the science behind Dyslexia and look at evidence-based interventions. From phonics-based programs to assistive technology, learners will discover methods proven to make a difference. This module is beneficial for anyone looking to go beyond surface-level understanding, diving into the cognitive mechanisms that create Learning Disabilities like Dyslexia and the ways modern approaches can transform the learning experience.Finally, Module 5 provides an overview of the legal ethics and considerations associated with Dyslexia and Learning Disabilities. Laws and educational policies have evolved to protect students with Learning Disabilities and ensure equal opportunities in schools and workplaces. In this module, learners will gain awareness of the rights of individuals with Dyslexia and how these rights impact educational and workplace settings. This knowledge is essential for anyone working with or advocating for those with Learning Disabilities.By the end of this comprehensive course, students will have a well-rounded understanding of Dyslexia as a Learning Disability and be better equipped to create a supportive learning environment. Overview Section 1: Introduction Lecture 1 Introduction to Dyslexia Lecture 2 Dyslexia and Learning Difficulties Lecture 3 Dyslexia in the School Lecture 4 Cognitive Approaches to Dyslexia Lecture 5 Legal Ethics and Considerations in Dyslexia Anyone new to the topic of Dyslexia and Learning Disabilities,Teachers and school staff looking to better support students with Dyslexia,Parents of children with Dyslexia who want to gain deeper insights into this Learning Disability,HR and workplace professionals aiming to understand Dyslexia from a learning support perspective,Educational and social work students interested in Learning Disabilities,Anyone interested in exploring methods to make learning accessible for individuals with Dyslexia Screenshot Homepage https://www.udemy.com/course/learning-disability-dyslexia-awareness/ Rapidgator https://rg.to/file/f733e45b438bcad6b3ad5c356f019562/fpfbl.Learning.Disability.Dyslexia.Awareness.rar.html Fikper Free Download https://fikper.com/jgbo7blaeC/fpfbl.Learning.Disability.Dyslexia.Awareness.rar.html No Password - Links are Interchangeable

Free Download Learning Apache Spark - Master Spark for Big Data Processing Published 10/2024 Created by VCloudMate Solutions MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 88 Lectures ( 7h 11m ) | Size: 2.8 GB Embark on a comprehensive journey to master Apache Spark from data manipulation to machine learning! What you'll learn Understand the fundamentals of Spark's architecture and its distributed computing capabilities Learn to write and optimize Spark SQL queries for efficient data processing Master the creation and manipulation of DataFrames, a core component of Spark Learn to read data from different file formats such as CSV and Parquet Develop skills in filtering, sorting, and aggregating data to extract meaningful insights Learn to process and analyze streaming data for real-time insights Explore the capabilities of Spark's MLlib for machine learning Learn to create and fine-tune models using pipelines and transformers for predictive analytics Requirements You should know how to write and run Python code Basic understanding of Python syntax and concepts is necessary Understanding SQL (Structured Query Language) is important You should know how to create and manage tables, transform data, and run queries Description Unlock the power of big data with Apache Spark!In this course, you'll learn how to use Apache Spark with Python to work with data.We'll start with the basics and move up to advanced projects and machine learning.Whether you're just starting or already know some Python, this course will teach you step-by-step how to process and analyze big data.What You'll Learn:Use PySpark's DataFrame: Learn to organize and work with data.Store Data Efficiently: Use formats like Parquet to store data quickly.Use SQL in PySpark: Work with data using SQL, just like with DataFrames.Connect PySpark with Python Tools: Dig deeper into data with Python's data tools.Machine Learning with PySpark's MLlib: Work on big projects using machine learning.Real-World Examples: Learn by doing with practical examples.Handle Large Data Sets: Understand how to manage big data easily.Solve Real-World Problems: Apply Spark to real-life data challenges.Build Confidence in PySpark: Get better at big data processing.Manage and Analyze Data: Gain skills for both work and personal projects.Prepare for Data Jobs: Build skills for jobs in tech, finance, and healthcare.By the end of this course, you'll have a solid foundation in Spark, ready to tackle real-world data challenges. Who this course is for IT professionals interested in big data and analytics Aspiring Data Scientists Aspiring Data Analysts Aspiring Machine Learning Engineers Business Analysts Software Engineers Students and Academics Researchers Anyone Interested in Big Data Homepage https://www.udemy.com/course/learning-apache-spark-master-spark-for-big-data-processing/ Rapidgator https://rg.to/file/990339f131570b5941433d1bfe0f31ec/oehgr.Learning.Apache.Spark..Master.Spark.for.Big.Data.Processing.part2.rar.html https://rg.to/file/a87002e26f49542e970bbe5eb6ec16a5/oehgr.Learning.Apache.Spark..Master.Spark.for.Big.Data.Processing.part1.rar.html https://rg.to/file/d9222ef1e87ef257c2cafbea2f97a28f/oehgr.Learning.Apache.Spark..Master.Spark.for.Big.Data.Processing.part3.rar.html Fikper Free Download https://fikper.com/ERnEEicX2m/oehgr.Learning.Apache.Spark..Master.Spark.for.Big.Data.Processing.part3.rar.html https://fikper.com/XuDk9MOl6q/oehgr.Learning.Apache.Spark..Master.Spark.for.Big.Data.Processing.part2.rar.html https://fikper.com/nRI1NYRzQY/oehgr.Learning.Apache.Spark..Master.Spark.for.Big.Data.Processing.part1.rar.html No Password - Links are Interchangeable

Free Download Java Programming Language Step-by-Step Learning Path Published 10/2024 Created by Click Learning MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 24 Lectures ( 3h 53m ) | Size: 1.6 GB Step-by-step guide to Java programming, helping you build solid coding skills through practical coding exercises. What you'll learn Introduction to Java Setting up the Java development environment Basic data types (Numbers, Strings, Arrays) Control flow (If-Else Statements, Loop for, Loop While) Interfaces and Abstract Classes Objects and Classes Encapsulation and Abstraction Methods and Classes Encapsulation and Abstraction JavaFX for Modern GUI Development Collections Framework File I/O Multithreading JDBC for Database Connectivity Spring Framework for Enterprise Applications Code Optimization and Performance Tuning Requirements No Prior Java Experience Required, Here you learn step by step. Description Unlock the power of Java with "Java Programming Language Step-by-Step Learning Path," a comprehensive course designed for beginners and aspiring developers who want to master Java programming. Whether you're completely new to coding or looking to solidify your Java skills, this course takes you through a structured, easy-to-follow journey, making learning both effective and engaging.Starting with the basics, you'll learn the fundamentals of Java, including variables, data types, loops, and functions. As you progress, you'll dive into more complex topics like object-oriented programming (OOP), exception handling, file I/O, and data structures. Each topic is broken down into bite-sized lessons, supported by real-world examples and hands-on projects to reinforce your learning.Course Outline:Java FundamentalsIntroduction to JavaSetting up the Java development environmentBasic data types (Numbers, Strings, Arrays)Operators and ExpressionsControl flow (If-Else Statements, Loop for, Loop While)Methods and ClassesObject-Oriented Programming (OOP) in JavaObjects and ClassesInheritance and PolymorphismEncapsulation and AbstractionException HandlingInterfaces and Abstract ClassesAdvanced Java ConceptsGenericsCollections FrameworkMultithreadingNetworkingFile I/OJava Libraries and FrameworksSwing for GUI DevelopmentJDBC for Database ConnectivityServlet and JSP for Web DevelopmentSpring Framework for Enterprise ApplicationsJavaFX for Modern GUI DevelopmentJava Project DevelopmentTesting and DebuggingCode Optimization and Performance TuningDeployment and MaintenanceBy the end of this course, you'll have a strong grasp of Java's core concepts and will be able to confidently write, debug, and deploy Java applications. You'll also build a portfolio of projects that demonstrate your skills, from simple programs to more advanced applications. Whether you want to pursue a career in software development or use Java for personal projects, this course gives you the tools you need to succeed.Enroll today and follow this step-by-step path to becoming a proficient Java programmer! Who this course is for Anyone interested in becoming a proficient Java Programming. Homepage https://www.udemy.com/course/java-programming-language-step-by-step-learning-path/ Screenshot Rapidgator https://rg.to/file/0cc6e5f422051285324d97c87e6aa1d6/uuyku.Java.Programming.Language.StepbyStep.Learning.Path.part1.rar.html https://rg.to/file/e901d03095be85911c45d03cc4b53ab0/uuyku.Java.Programming.Language.StepbyStep.Learning.Path.part2.rar.html Fikper Free Download https://fikper.com/UpEzNCrmNk/uuyku.Java.Programming.Language.StepbyStep.Learning.Path.part1.rar.html https://fikper.com/pl0D0mjJjb/uuyku.Java.Programming.Language.StepbyStep.Learning.Path.part2.rar.html No Password - Links are Interchangeable

Free Download Introduction to Machine Learning by Kevin Brand Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 5h 42m | Size: 3.54 GB A beginners guide to commonly used machine learning models and terminology What you'll learn Define the fundamental aspects of data pipelines that is necessary for machine learning Identify the potential pitfalls when building data pipelines Recognize the different types of machine learning models and explain their differences Discuss popular supervised machine learning models Understand popular unsupervised clustering algorithms Broadly define what neural networks are Know what some of the most popular neural network variants are and when to use them Utilize machine learning fundamentals to implement basic solutions to classification and regression problems Requirements Some programming experience will be beneficial for exercises and examples, but is not required. Description This course aims to provide students with a broad overview of the field of machine learning and will introduce some important terms and techniques which will enable them to follow a discussion on the topic. I will discuss the fundamental aspects of data pipelines and will point out what some of the common pitfalls are when preparing data for a machine learning project. I will also discuss what the different types of machine learning models are and how they differ from deep learning models.Broad overviews will be provided of some of the most popular supervised and unsupervised models and students will be introduced to some of the popular neural network variants. This will be followed by a few practical demonstrations which will show students how they can combine the discussed topics to create basic machine learning solutions.This course will not provide in-depth explanations regarding the mathematical underpinnings of these models, nor will it provide detailed discussions regarding how to implement machine learning models from scratch. Instead, the aim is to simplify and condense the subject matter to provide students with an easily digestible introduction to the field.Whether students are employers or employees, we believe it to be highly beneficial to have a basic understanding of what machine learning models are and what they are not --- especially as machine learning tools become increasingly common in many domains. Who this course is for Software engineers that want to be able to follow discussions about the machine learning pipeline Managers that are looking to incorporate machine learning into their business and want to better understand the intricacies of doing so Prospective students that want to establish whether machine learning is the right field for them This course is not intended for learners with prior machine learning knowledge This course is not intended for learners that wants to understand the mathematical foundations of machine learning Homepage https://www.udemy.com/course/introduction-to-machine-learning-f/ Screenshot Rapidgator https://rg.to/file/2c28cb792a2961877a95f1e30e23878e/ungdj.Introduction.to.Machine.Learning.by.Kevin.Brand.part2.rar.html https://rg.to/file/364e5fa824cda9de5e29b0fabcc9aa53/ungdj.Introduction.to.Machine.Learning.by.Kevin.Brand.part1.rar.html https://rg.to/file/97dfde1343a1651e4d9c618d9d56c279/ungdj.Introduction.to.Machine.Learning.by.Kevin.Brand.part4.rar.html https://rg.to/file/b551d3577b57591bd4c686da61a2ed3d/ungdj.Introduction.to.Machine.Learning.by.Kevin.Brand.part3.rar.html Fikper Free Download https://fikper.com/2FegpOKPV0/ungdj.Introduction.to.Machine.Learning.by.Kevin.Brand.part1.rar.html https://fikper.com/EH0lLiaTOU/ungdj.Introduction.to.Machine.Learning.by.Kevin.Brand.part2.rar.html https://fikper.com/myO1itAzcj/ungdj.Introduction.to.Machine.Learning.by.Kevin.Brand.part4.rar.html https://fikper.com/vH4e8VUQSa/ungdj.Introduction.to.Machine.Learning.by.Kevin.Brand.part3.rar.html No Password - Links are Interchangeable

Free Download Introduction To Machine Learning by Dr.Padmapriya G Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 836.56 MB | Duration: 2h 46m Machine Learning, Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Markov Models, HMM What you'll learn Explore the fundamental mathematical concepts of machine learning algorithms Apply linear machine learning model to perform regression and classification Utilize mixture models to group similar data items Develop macine learning models for time-series data prediction Design ensemble learning model using various machine learning algorithms Requirements No programming experience is need Description Course Description:Unlock the power of machine learning with this comprehensive course designed for beginners and intermediate learners. You will be guided through the essential concepts, algorithms, and techniques driving machine learning today, building a solid understanding of how machines learn from data and solve real-world problems. This course is designed to help you grasp the theoretical underpinnings of machine learning while applying your knowledge through solved problems, making complex concepts more accessible.What You'll Learn:Core Principles of Machine Learning: Gain a deep understanding of how systems learn from data to make intelligent decisions.Supervised Learning: Explore predictive modeling using algorithms like Linear Regression, and Support Vector Machines (SVM).Unsupervised Learning: Master clustering techniques like k-Means and Hierarchical Clustering to discover patterns in data.Regression and Classification: Learn how to model continuous outcomes (regression) and classify data into distinct categories (classification).Clustering: Group similar data points to uncover hidden structures within large datasets.Markov Models & Hidden Markov Models (HMMs): Understand probabilistic models that predict future states and learn how they are used to model sequences and temporal data. Through solved problems, you'll explore how these models work in practice, gaining insights into the theoretical foundation and practical application of HMMs in time-series data and sequential decision-making processes.Machine learning is transforming industries by enabling systems to learn and make intelligent decisions from data. This course will equip you with a strong foundation in machine learning, focusing on problem-solving and theoretical understanding without the need for hands-on implementation.Practical Application Through Solved Problems:This course includes solved problems to illustrate how each algorithm and technique works in practice. These examples will help you apply theoretical concepts to real-world situations, deepening your understanding and preparing you to solve similar problems in your professional or academic career.Through detailed explanations of algorithms, real-world examples, and step-by-step breakdowns of machine learning processes, you'll develop a solid grasp of the models and techniques used across various industries. This course is perfect for learners who want to master the core concepts of machine learning and engage with practical applications without diving into programming or technical implementation.Course Highlights:No Programming Required: Focus on understanding the theory behind machine learning algorithms and models.Solve Real-World Problems: Work through practical examples to understand how to apply machine learning techniques to everyday challenges.Evaluate Model Performance: Learn to assess, interpret, and refine machine learning models effectively.Build a Strong Conceptual Foundation: Prepare for future practical applications in machine learning or data-driven fields.Who Should Take This Course:Students and Professionals: Ideal for those seeking an in-depth introduction to machine learning theory.Enthusiasts with Basic Knowledge of Math and Programming: Perfect for those interested in machine learning concepts through solved problems and real-world examples. Overview Section 1: Introduction Lecture 1 Machine Learning What and Why? Lecture 2 Supervised and Unsupervised Learning Lecture 3 Polynomial Curve Fitting Lecture 4 Probability Theory - Introduction and Fundamental Rules Lecture 5 Probability - Bayes Rule and Independence and Conditional Independence Lecture 6 Probability - Random Variables and Density Function Lecture 7 Probability - Quantiles, Mean, Variance, Expectation and Covariance Section 2: Linear Models for Regression Lecture 8 Robust Linear Regression Lecture 9 Ridge Linear Regression Section 3: Mixture Models and EM Lecture 10 K- Means Clustering Lecture 11 K-Means Clustering Solved Problem Lecture 12 PCA Solved Problem Lecture 13 Hierarchical Clustering Section 4: Hidden Markov Models Lecture 14 Sequential Data and Markov Model Beginners for Machine learning Screenshot Homepage https://www.udemy.com/course/introduction-to-ml/ Rapidgator https://rg.to/file/9cc5f618079c81e745e0160f63837908/zgrua.Introduction.To.Machine.Learning.by.Dr.Padmapriya.G.rar.html Fikper Free Download https://fikper.com/53VHMcS850/zgrua.Introduction.To.Machine.Learning.by.Dr.Padmapriya.G.rar.html No Password - Links are Interchangeable

Free Download Introduction To Machine Learning Models (Ai) Testing Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.01 GB | Duration: 4h 54m From Scratch, Learn testing types and Strategies involved in all the phases of ML Models (AI) with real time examples What you'll learnIntroduction to Artificial Intelligence and Machine Learning Models Understanding Lifecycle of Machine Learning Models and their testing Scope Shift-Left Testing in the ML Engineering Phase such as OverFitting & UnderFitting Testing QA Functional Testing in the ML Validation Phase with 25 different Testing types & Strategies API Testing Scope for Machine Learning Models with ChatGPT Model example Responsible AI Testing for Machine Learning Models such as Bias, Fairness, Ethical, Privacy Testing etc Post-Deployment Testing Strategies for ML Models such as DataDrift & Concept Drift testing Continuous Tracking and Monitoring Activities for QA in Production RequirementsNone. All the concepts are taken care with Scratch explanation DescriptionThis course will introduce you to the World of Machine Learning Models Testing. As AI continues to revolutionize industries, many companies are developing their own ML models to enhance their business operations. However, testing these models presents unique challenges that differ from traditional software testing. Machine Learning Model testing requires a deeper understanding of both data quality and model behavior, as well as the algorithms that power them.This Course starts with explaining the fundamentals of the Artificial Intelligence & Machine Learning concepts and gets deep dive into testing concepts & Strategies for Machine Learning models with real time examples.Below is high level of Agenda of the tutorial:Introduction to Artificial IntelligenceOverview of Machine Learning Models and their LifecycleShift-Left Testing in the ML Engineering PhaseQA Functional Testing in the ML Validation PhaseAPI Testing Scope for Machine Learning ModelsResponsible AI Testing for ML ModelsPost-Deployment Testing Strategies for ML ModelsContinuous Tracking and Monitoring Activities for QA in ProductionBy the end of this course,you will gain expertise in testing Machine Learning Models at every stage of their lifecycle.Please Note:This course highlights specialized testing types and methodologies unique to Machine Learning Testing, with real-world examples.No specific programming language or code is involved in this tutorial. OverviewSection 1: Getting Started with Machine Learning Testing basics Lecture 1 Introduction and Agenda of the tutorial Lecture 2 Introduction to Artificial Intelligence Systems with examples Lecture 3 What is Machine Learning and how it is related to Artificial Intelligence family Lecture 4 Examples of commonly used Machine Learning Models and their usage Section 2: Shift Left Testing in ML Model Engineering phase (Supervised Learning) Lecture 5 Understand Machine Learning Model Life cycle stages with online/offline modes Lecture 6 How Machine Learning models works in nutshell -Learn terminologies used Lecture 7 Understand how OverFitting Testng & UnderFitting works with Trained data sets Lecture 8 Predicting House Prices (ML Model) Demo to show how internally Algorithms works Lecture 9 Revision on Supervised Learning Model Testing with Overfitting/UnderFitting ex Section 3: Unsupervised Learning Models Testing in Engineering Phase Lecture 10 Introduction to Unsupervised Learning in the ML models with example Lecture 11 Testing scope on Unsupervised Learning with Data point patterns&Cluster scores Lecture 12 Revision on Unsupervised Learning with cluster score analysis Section 4: Reinforcement Learning & Commonly used Frameworks and Algos in ML Models Lecture 13 Introduction to Reinforcement Learning in ML Model with examples Lecture 14 Algorithms and Frameworks commonly used in developing ML Models Section 5: Functional Testing for Machine Learning Models in Evaluation Phase Lecture 15 What are Validation Unseen Data sets and why it is required Lecture 16 Temperature Testing to fine tune the response predictions from ML Models Lecture 17 Prompts Testing with Zero Shot & Chain of thought Prompts test Lecture 18 Relevance stary Testing & Fantasy claims testing on ML Models Lecture 19 Repeatability Testing & Asking question in different phases to test Lecture 20 Style Transfer testing & Intent recognition testing on ML Models Lecture 21 What is Invariance Testing & BiDirectional testing for AI Models Section 6: Introduction to API Testing on Machine Learning Models Lecture 22 Create OpenAI Account to test ChatGPT API's of generating response Lecture 23 Download Postman tool to setup ChatGPT APIs environment for testing Lecture 24 PostBot plugin to generate automation scripts for API responses in Postman Section 7: Responsible AI Testing with examples on Machine Learning (AI) Models Lecture 25 Importance of Fairness testing on ML responses to check bias Lecture 26 Transparency testing and why it is necessary to stay ahead in AI competition Lecture 27 Data Privacy and Security testing on Machine Learning models Section 8: Post Deployment Testing Types with examples on Machine Learning Models Lecture 28 Importance of Integration & Latency testing on Production ML models Lecture 29 Importance of Data drift Testing & Concept Drift Testing in ML Models Lecture 30 Shadow Testing & A/B Testing to certify the latest version of ML into prod Section 9: Final words - Impact of Machine Learning Models in QA Space Lecture 31 How QA's can be critical resources for Machine Learning Model Life cycle Lecture 32 Bonus Lecture QA Testers,Software Engineers,Software Testers,Data Engineers,Developers,Test Managers Homepage https://www.udemy.com/course/machine-learning-models-ai-testing/ Rapidgator https://rg.to/file/0274d5b78f2c1dc209d45da2779adba2/ljrfw.Introduction.To.Machine.Learning.Models.Ai.Testing.part4.rar.html https://rg.to/file/0398e5d870f0349257e96e5572208a39/ljrfw.Introduction.To.Machine.Learning.Models.Ai.Testing.part1.rar.html https://rg.to/file/7248aaf3582e508835ebdf9a50a0ee13/ljrfw.Introduction.To.Machine.Learning.Models.Ai.Testing.part2.rar.html https://rg.to/file/cd83cdb364cc92fbdda0c244d064661d/ljrfw.Introduction.To.Machine.Learning.Models.Ai.Testing.part3.rar.html Fikper Free Download https://fikper.com/7YbU88WFvn/ljrfw.Introduction.To.Machine.Learning.Models.Ai.Testing.part1.rar.html https://fikper.com/Iwf11shZXn/ljrfw.Introduction.To.Machine.Learning.Models.Ai.Testing.part3.rar.html https://fikper.com/cF8XUqYGTE/ljrfw.Introduction.To.Machine.Learning.Models.Ai.Testing.part4.rar.html https://fikper.com/yhyAnByfdB/ljrfw.Introduction.To.Machine.Learning.Models.Ai.Testing.part2.rar.html No Password - Links are Interchangeable

Free Download Identifying Learning Opportunities Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 301.96 MB | Duration: 0h 33m Self-Assessment, Opportunity Identification What you'll learn Understanding Learning Opportunities Methods for Identifying Opportunities Evaluating Learning Opportunities Planning and Goal Setting Leveraging Resources Reflective Practices Case Studies and Practical Applications Requirements Basic Educational Background Interest in Personal and Professional Development Fundamental Research Skills Communication Skills Access to Technology Previous Experience or Coursework English Proficiency Description The course "Identifying Learning Opportunities" is designed to equip parti[beeep]nts with the skills and insights necessary to recognize and capitalize on various educational and developmental opportunities that arise in both professional and personal contexts. This comprehensive course delves into the nuances of what constitutes a learning opportunity and teaches parti[beeep]nts how to differentiate between formal, informal, and non-formal learning environments.Parti[beeep]nts will learn through a mix of theoretical frameworks and practical applications how to conduct effective self-assessments to understand their personal learning needs and objectives. They will also explore methods for scanning their environment to identify potential opportunities, using tools such as networking, industry research, and technological platforms.A significant focus of the course is on evaluating the relevance and value of opportunities in relation to one's career goals and personal growth plans. Parti[beeep]nts will be taught to set SMART goals and develop actionable plans to take advantage of the opportunities they identify.Furthermore, the course emphasizes the importance of reflective practices and encourages a mindset of lifelong learning. Through case studies, interactive sessions, and hands-on projects, parti[beeep]nts will apply what they've learned in real-world scenarios, ensuring they leave the course with the ability to continuously adapt and grow in an ever-changing world.This course is ideal for students, professionals at any stage of their career, and anyone interested in maximizing their potential through continuous learning and development. Overview Section 1: Identifying Learning Opportunities Lecture 1 Introduction to Identifying Learning Opportunities Lecture 2 The Imperative for Lifelong Learning in Leadership Lecture 3 Fostering a Learning-Oriented Organizational Culture Lecture 4 Strategies for Enhancing Leadership Learning Lecture 5 Key Characteristics of Effective Learners Lecture 6 Leveraging Technology and Innovation for Learning Lecture 7 Case Study: Sarah's Leadership Journey Section 2: Exploring Diverse Learning Opportunities and Brainstorming Essential Skills Lecture 8 Introduction to Diverse Learning Opportunities Lecture 9 Formal Education and Training Programs Lecture 10 Informal Learning Experiences and Self-directed Learning Lecture 11 Peer Learning and Collaboration Lecture 12 Action Learning and Experiential Learning Lecture 13 Feedback and Reflection Lecture 14 Case Study: John's Leadership Journey Section 3: Personalized Learning Roadmap and Skills Matrix Development Lecture 15 Introduction to Personalized Learning Roadmap Development Lecture 16 Individualized Skill Assessment Lecture 17 Goal Setting and Prioritisation Lecture 18 Exploring Diverse Learning Opportunities Lecture 19 Continuous Feedback and Reflection Lecture 20 Adaptation and Iteration Lecture 21 Case Study: Emily's Leadership Journey Section 4: Facilitating Learning Journey Mapping and Action Planning Lecture 22 Introduction to Learning Journey Mapping and Action Planning Lecture 23 Understanding Individual Learning Styles and Preferences Lecture 24 Clarifying Learning Objectives and Goals Lecture 25 Mapping Learning Opportunities and Resources Lecture 26 Creating Action Plans and Timelines Lecture 27 Building Accountability and Support Structures Lecture 28 Monitoring Progress and Adjusting Plans Lecture 29 Case Study: James's Leadership Journey Lecture 30 Celebrating Achievements and Reflecting on Learnings Section 5: Reflection, Analysis, and Promoting Discussion Lecture 31 Introduction to Reflection and Analysis in Learning Lecture 32 Encouraging Regular Self-Reflection Lecture 33 Conducting Objective Analysis of Performance Lecture 34 Fostering Open Dialogue and Exchange of Ideas Lecture 35 Facilitating Peer Learning and Knowledge Sharing Lecture 36 Championing Constructive Feedback and Reflection Sessions Lecture 37 Case Study: Implementing Effective Reflection and Discussion Strategies Students and Recent Graduates,Early Career Professionals,Career Changers,Entrepreneurs and Business Owners,Human Resources Professionals,Educators and Trainers,Lifelong Learners Homepage https://www.udemy.com/course/identifying-learning-opportunities/ Rapidgator https://rg.to/file/dc5929e2818e3a932804b0371047fea5/ixexe.Identifying.Learning.Opportunities.rar.html Fikper Free Download https://fikper.com/1uoc0IHhcR/ixexe.Identifying.Learning.Opportunities.rar.html No Password - Links are Interchangeable

Free Download Human-in-the-Loop Machine Learning, Video Edition by Rob Munro Released 7/2021 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 13h 42m | Size: 2.24 GB Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. About the Technology Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. About the Book Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows. What's Inside Identifying the right training and evaluation data Finding and managing people to annotate data Selecting annotation quality control strategies Designing interfaces to improve accuracy and efficiency About the Author Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford. Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past. Screenshot Rapidgator https://rg.to/file/9b76979c432dcf35907d96796b1dfe3d/ohbje.HumanintheLoop.Machine.Learning.Video.Edition.part2.rar.html https://rg.to/file/aa4dcfeffb6ada8db6fb09db551e92ed/ohbje.HumanintheLoop.Machine.Learning.Video.Edition.part3.rar.html https://rg.to/file/bd0b102825bc00fc19417f3cd7daee81/ohbje.HumanintheLoop.Machine.Learning.Video.Edition.part1.rar.html Fikper Free Download https://fikper.com/AvZEKNFwzn/ohbje.HumanintheLoop.Machine.Learning.Video.Edition.part2.rar.html https://fikper.com/MfQMKrU26n/ohbje.HumanintheLoop.Machine.Learning.Video.Edition.part1.rar.html https://fikper.com/PXFKnQvSVg/ohbje.HumanintheLoop.Machine.Learning.Video.Edition.part3.rar.html No Password - Links are Interchangeable

Free Download Hands-On Python Machine Learning with Real World Projects Published 10/2024 Created by Sayman Creative Institute MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 13 Lectures ( 4h 8m ) | Size: 1.3 GB Python Based Machine Learning Course with Practical Exercises and Case Studies What you'll learn Applications of machine learning Data manipulation and analysis Building a predictive model to forecast sales Essential Python libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn) Requirements No experience required Description Are you ready to unlock the power of machine learning with Python? This comprehensive course is designed to equip you with the essential skills to build predictive models that can solve real-world problems.From beginner to expert, we'll guide you through the entire machine learning process, starting with the fundamentals of Python programming. You'll learn how to:Prepare and clean data for analysisExplore different machine learning algorithms and their applicationsBuild and train predictive models using popular libraries like Scikit-learn and TensorFlowEvaluate model performance and refine your approachApply machine learning techniques to a variety of real-world problems, including:Regression: Predicting continuous values (e.g., house prices)Classification: Categorizing data (e.g., spam detection)Clustering: Grouping similar data points (e.g., customer segmentation)Neural networks and deep learning: Building complex models for tasks like image and natural language processingThroughout the course, you'll work on hands-on projects that will help you solidify your understanding and develop practical skills. We'll also provide you with real-world case studies to demonstrate how machine learning can be applied to solve business challenges.By the end of this course, you'll be able to:Confidently use Python for machine learning tasksBuild and deploy predictive models that drive business valueStay up-to-date with the latest trends in machine learning Who this course is for Anyone who want to learn machine learning with python Homepage https://www.udemy.com/course/hands-on-python-machine-learning-with-real-world-projects/ Screenshot Rapidgator https://rg.to/file/32da74c86f826757ddf9a9e8acf84aff/rawin.HandsOn.Python.Machine.Learning.with.Real.World.Projects.part1.rar.html https://rg.to/file/e5550f57731b04ada2c75af7b53f5670/rawin.HandsOn.Python.Machine.Learning.with.Real.World.Projects.part2.rar.html Fikper Free Download https://fikper.com/Sz5eylTxoL/rawin.HandsOn.Python.Machine.Learning.with.Real.World.Projects.part1.rar.html https://fikper.com/ZOEzRyN4Vt/rawin.HandsOn.Python.Machine.Learning.with.Real.World.Projects.part2.rar.html No Password - Links are Interchangeable

Free Download Gamifying Training to Improve Learning Outcomes Released 10/2024 With Molly Kilfoyle, Madecraft MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 37m 43s | Size: 96 MB Transform training into engaging experiences with gamification techniques. Course details Discover how to leverage gamification to transform your trainings into fun and impactful learning experiences. In this course, instructional designer and corporate trainer Molly Kilfoyle outlines four steps you can take to make training fun with gamification: build your understanding of gamification benefits and applications, integrate important game elements into your trainings, personalize the learning experience, and evaluate and optimize your trainings. Homepage https://www.linkedin.com/learning/gamifying-training-to-improve-learning-outcomes Screenshot Rapidgator https://rg.to/file/0bfb3e0e727f3e40bc2417fcfc816187/miacm.Gamifying.Training.to.Improve.Learning.Outcomes.rar.html Fikper Free Download https://fikper.com/ObhVaLQVRi/miacm.Gamifying.Training.to.Improve.Learning.Outcomes.rar.html No Password - Links are Interchangeable

Free Download Fundamentals Of Reinforcement Learning (2024) Last updated 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 4.01 GB | Duration: 10h 39m A systematic tour of foundational RL, from k-armed bandits to planning via Markov Decision Processes and TD learning What you'll learn Master core reinforcement learning concepts from k-armed bandits to advanced planning algorithms. Implement key RL algorithms including Monte Carlo, SARSA, and Q-learning in Python from scratch. Apply RL techniques to solve classic problems like Frozen Lake, Jack's Car Rental, Blackjack, and Cliff Walking. Develop a deep understanding of the mathematical foundations underlying modern RL approaches. Requirements Students should be comfortable with Python programming, including NumPy and Pandas. Basic understanding of probability concepts is beneficial (probability distributions, random variables, conditional and joint probabilities) While familiarity with other machine learning methods is helpful, it's not required. We'll build the necessary reinforcement learning concepts from the ground up. Section assignments are in pure python (rather than Jupyter Notebooks), and often span edits to multiple modules, so students should be setup with an editor (e.g. VS Code or PyCharm) Description Reinforcement learning is one of the most exciting branches of modern artificial intelligence.It came to the public consciousness largely because of a brilliant early breakthrough of DeepMind: in 2016, they utilised reinforcement learning to smash a benchmark thought to be decades away in artificial intelligence - they beat the world's greatest human grandmaster in the Chinese game of Go.This was so exceptional because the game tree for Go is so large - the number of possible moves is 1 with 200 zeros after it (or a "gargoogol"!). Compare this with chess, which has only 10^50 nodes in its tree.Chess was solved in 1997, when IBM's Deep Blue beat the world's best Gary Kasparov. Deep Blue was the ultimate example of the previous generation of AI - Good Old-fashioned AI or "GOFAI". A team of human grandmasters hard-coded opening strategies, piece and board valuations and end-game databases into a powerful computer which then crunched the numbers in a relatively brute-force way.DeepMind's approach was very different. Instead of humans hard-coding heuristics for how to play a good game of Go, they applied reinforcement learning so that their algorithms could - by playing themselves, and winning or losing millions of times - work out good strategies for themselves.The result was a game playing algorithm unbounded by the limitations of human knowledge. Go grandmasters to this day are studying its unique and creative moves in its series against Lee Sedol.Since then, DeepMind have shown how reinforcement learning can be practically applied to real life problems. A reinforcement learning agent controlling the cooling system for a Google data centre found strategies no human control engineer had thought of, such as to exploit winter temperatures to save heater use. Another of their agents applied to an experimental fusion reactor similarly found superhuman strategies for controlling the highly complex plasma in the reactor.So, reinforcement learning promises to help solve some of the grand problems of science and engineering, but it has a whole load of more immediately commercial applications too - from the A/B testing of products and website design, to the implementation of recommender systems to learn how to match up a company's customers with its products, to algorithmic trading, where the objective is to buy or sell stocks to maximise a profit.This course will explain the fundamentals of this most exciting branch of AI. You will get to grips with both the theory underpinning the algorithms, and get hands-on practise implementing them yourself in python.By the end of this course, you will have a fundamental grasp these algorithms. We'll focus on "tabular" methods using simple NumPy arrays rather than neural networks, as one often gets the greatest understanding of problems by paring them down to their simplest form and working through each step of an algorithm with pencil and paper.There is ample opportunity for that in this course, and each section is capped with a coding assignment where you will build the algorithms yourselfFrom there, the world is your oyster! Go solve driverless cars, make bajillions in a hedge fund, or save humanity by solving fusion power! Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Course overview Section 2: K-armed bandits Lecture 3 Introduction to k-armed bandits Lecture 4 Setting the scene Lecture 5 Initial concepts Lecture 6 Action value methods // Greedy Lecture 7 Action value methods // Epsilon-greedy Lecture 8 Action value methods // Efficient implementation Lecture 9 Non-stationary bandits Lecture 10 Optimistic initial values Lecture 11 Getting started with your first assignement: the 10-armed testbed Section 3: Markov Decision Processes (MDPs) Lecture 12 Introduction to MDPs Lecture 13 From bandits to MDPs // setting the scene Lecture 14 From bandits to MDPs // Frozen Lake walk-through Lecture 15 From bandits to MDPs // Real world examples Lecture 16 Goals, rewards, returns and episodes Lecture 17 Policies and value functions Lecture 18 Bellman equations // Expectation equation for v(s) Lecture 19 Bellman equations // Expectation equation for q(s, a) Lecture 20 Bellman equations // Optimality equations Lecture 21 Walk-through // Bellman expectation equation Lecture 22 Walk-through // Bellman optimality equation Lecture 23 Walk-through // Matrix inversion Lecture 24 MDP section summary Section 4: Dynamic Programming (DP) Lecture 25 Introduction to Dynamic Programming Lecture 26 Policy evaluation // introduction Lecture 27 Policy evaluation // walk-through Lecture 28 Policy improvement // introduction and proof Lecture 29 Policy improvement // walk-through Lecture 30 Policy iteration Lecture 31 Value iteration // introduction Lecture 32 Value iteration // walkthrough Section 5: Monte Carlo methods Lecture 33 Introduction to Monte Carlo methods Lecture 34 Setting the scene Lecture 35 Monte Carlo example // area of a pentagram Lecture 36 Prediction Lecture 37 Control - exploring starts Lecture 38 Control - on-policy Lecture 39 Control - off-policy // new concepts Lecture 40 Control - off-policy // implementation Lecture 41 Environment introduction // Blackjack Section 6: Temporal Difference (TD) methods Lecture 42 Introduction to TD methods Lecture 43 Setting the scene Lecture 44 Sarsa Lecture 45 Q-learning Lecture 46 Expected sarsa Section 7: Planning methods Lecture 47 Introduction to planning methods Lecture 48 Filling the unforgiving minute Lecture 49 Dyna-Q // introduction Lecture 50 Dyna-Q // walk-through Lecture 51 Planning with non-stationary environments: Dyna-Q+ Section 8: Congratulations and feedback Lecture 52 Congratulations! This course is ideal for AI enthusiasts, computer science students, and software engineers keen to dive into reinforcement learning. Perfect for those with some programming experience who want to understand and implement cutting-edge AI algorithms from the ground up. 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