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Free Download Udemy - Data Preprocessing for Machine Learning and Data Analysis Published: 3/2025 Created by: Muhtar Qong MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 28 Lectures ( 8h 19m ) | Size: 4.35 GB A Comprehensive Guide for AI & Machine Learning Developers and Data Scientists What you'll learn Understand the importance of high-quality data in AI & machine learning. Apply data cleaning techniques to handle missing and poor-quality data. Perform feature selection, scaling, and transformation for better model performance. Work with categorical, numerical, text-based, and image features effectively. Identify correlations and use visualization techniques to gain insights. Implement Prin[beeep]l Component Analysis (PCA) for dimensionality reduction. Properly split datasets for training, testing, and cross-validation. Build automated data preprocessing pipelines using custom transformers. Visualize data using weighted scatter plots and shapefiles. Understand and process image and geographic datasets for AI & machine learning applications. Gain experience with traditional structured datasets, image datasets, and geographic datasets, providing a broader perspective on data used in AI & ML projects. Enhance your resume with in-demand data science skills, including statistical analysis, Python with NumPy, pandas, Matplotlib and advanced statistical analysis. Learn and apply useful data preprocessing techniques using Scikit-learn, pandas, NumPy, and Matplotlib. Requirements There are no special Requirements for this course. If you have beginner to intermediate-level Python experience, that is enough to follow along and understand the concepts. This course follows a classic classroom-style approach, where we first cover the theoretical foundations before moving on to hands-on coding sessions. This structured format makes the course easy to understand for learners at all levels. Description This course includes 29 downloadable files, including one PDF file containing the entire course summary (91 pages) and 28 Python code files attached to their corresponding lectures.If we understand a concept well theoretically, only then can we apply it effectively for our purposes. Therefore, this course is structured in a classic "classroom-style" approach. First, we dedicate sufficient time to explaining the theoretical foundations of each topic, including why we use a particular technique, where it is applicable, and its advantages.After establishing a solid theoretical understanding, we move on to the coding session, where we explain the example code line by line. This course includes numerous Python-based coding examples, and for some topics, we provide multiple examples to reinforce understanding. These examples are adaptable, meaning you can modify them slightly to fit your specific projects.Data preprocessing is a crucial step in AI and machine learning, directly affecting model performance, accuracy, and efficiency. Since raw data is often messy and unstructured, preprocessing ensures clean, optimized datasets for better predictions.This hands-on course covers essential techniques, including handling missing values, scaling, encoding categorical data, feature engineering, and dimensionality reduction (PCA). We will also explore data visualization with geographic information, weighted scatter plots, and shapefiles, particularly useful for geospatial AI applications.Beyond traditional structured datasets, this course includes image and geographic datasets, giving learners a broader perspective on real-world AI projects.By the end, you'll be able to build automated data preprocessing pipelines and prepare datasets efficiently for machine learning and deep learning applications.Ideal for ML engineers, data scientists, AI developers, and researchers, this course equips you with practical skills and best practices for high-quality, well-processed datasets that enhance model performance. You can download the entire course summary PDF from the final lecture (Lecture 28) Who this course is for Aspiring AI & Machine Learning Developers who want to master data preprocessing. Data Scientists & Analysts looking to improve model accuracy and efficiency. AI & ML Engineers working with real-world datasets, including geographic and image data. Students & Researchers interested in learning advanced data preparation techniques. 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Free Download Building Recommendation Engine With Machine Learning & Rag Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.72 GB | Duration: 3h 21m Learn how to build product, movie, music recommendation engines using Tensorflow, Keras, Surprise, SVD, and RAG What you'll learn Learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, RAG implementation in recommendation system Learn how to build product recommendation engine using Tensorflow and Keras Learn how to build movie recommendation engine using Surprise Learn how to build music recommendation engine using retrieval augmented generation Learn how to build product recommendation engine using TFIDF Vectorizer and Cosine Similarity Learn how to build search based recommendation engine using RAG Learn how recommendation engines work. This section cover, data collection, preprocessing, feature selection, model training, model evaluation, and deployment Learn how to perform feature selection for product recommendation engine Learn how to perform feature selection for movie recommendation engine Learn how to build and train collaborative filtering model Learn how to load RAG model and create Facebook AI Similarity Search index Learn how to build user interface for recommendation engine using Gradio and Streamlit Learn how to test and deploy recommendation engine on Hugging Face Learn how to download dataset using Kaggle Hub API Requirements No previous experience in machine learning is required Basic knowledge in Python Description Welcome to Building Recommendation Engine with Machine Learning & RAG course. This is a comprehensive project based course where you will learn how to build intelligent recommendation systems using Tensorflow, Surprise and Retrieval Augmented Generation. This course is a perfect combination between Python and machine learning, making it an ideal opportunity to level up your programming skills while improving your technical knowledge in software development. In the introduction session, you will learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, and also learn how retrieval augmented generation can be used to improve your recommendation system. Then, in the next section, you will learn step by step how a recommendation engine works. This section covers data collection, data preprocessing, feature selection, model selection, model training, model evaluation, deployment, monitoring, and maintenance. Afterward, you will also learn how to find and download datasets from Kaggle, it is a platform that offers many high quality datasets from various industries. Once everything is ready, we will start the project. Firstly, we are going to build a product recommendation engine using TensorFlow, it will have the capability of suggesting relevant products to users based on their browsing and purchase history. This recommendation engine will be able to analyze user behavior, extract meaningful patterns, and generate personalized product recommendations in real time. By implementing this system, businesses can enhance customer engagement, increase conversion rates, and optimize the shopping experience through intelligent suggestions. In the next section, we are going to build a movie recommendation engine using Surprise, which will help users discover films they might enjoy based on their past ratings and preferences. This recommendation engine will utilize collaborative filtering techniques to find similarities between users and movies, delivering highly personalized recommendations. With this approach, we can improve content discovery, keep users engaged, and drive higher retention rates for streaming platforms. Following that, we are also going to build a music recommendation engine using Retrieval Augmented Generation that is able to provide dynamic and context aware song recommendations. This recommendation engine will be able to enhance traditional recommendation methods by incorporating real-time external knowledge, improving the accuracy and diversity of song suggestions. Lastly, at the end of the course, we will conduct testing to evaluate the performance of our recommendation engines. After ensuring optimal model performance, we will deploy the recommendation system to Hugging Face Space, where users can select a few initial movies as input, allowing the model to process real-time data and generate personalized recommendations based on learned patterns and similarities.Before getting into the course, we need to ask this question to ourselves, why should we build a recommendation engine using machine learning? Well, here is my answer, by leveraging machine learning, businesses can offer smarter, more personalized recommendations that keep customers engaged, increase sales, and improve loyalty. Meanwhile, from users perspective, they can benefit from a seamless experience, where they receive valuable recommendations effortlessly, saving time and effort in finding what suits their needs.Below are things that you can expect to learn from this course:Learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, and RAG implementation in recommendation systemLearn how recommendation engines work. This section cover, data collection, preprocessing, feature selection, model training, model evaluation, and deploymentLearn how to download dataset using Kaggle Hub APILearn how to perform feature selection for product recommendation engineLearn how to build product recommendation engine using Tensorflow and KerasLearn how to build product recommendation engine using TFIDF Vectorizer and Cosine SimilarityLearn how to perform feature selection for movie recommendation engineLearn how to build movie recommendation engine using SurpriseLearn how to build and train collaborative filtering modelLearn how to build music recommendation engine using retrieval augmented generationLearn how to load RAG model and create Facebook AI Similarity Search indexLearn how to build search based recommendation engine using RAGLearn how to build user interface for recommendation engine using Gradio and StreamlitLearn how to test and deploy recommendation engine on Hugging Face Software engineers who are interested in building recommendation engine using machine learning,Data scientists who are interested in transforming customer data into relevant product recommendations Homepage: https://www.udemy.com/course/building-recommendation-engine-with-machine-learning-rag/ [b]AusFile[/b] https://ausfile.com/6km5cel8vplo/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part1.rar.html https://ausfile.com/2aecmc10y8cf/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part2.rar.html Rapidgator https://rg.to/file/bbd14b547dd77a01b4e7e7b0dfbf60a4/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part1.rar.html https://rg.to/file/d07abbcf7256d5cf42f80a1b783b185d/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part2.rar.html Fikper Free Download https://fikper.com/4knwY1Q6Pl/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part1.rar.html https://fikper.com/arYoJYvJuL/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part2.rar.html No Password - Links are Interchangeable

Free Download Udemy - Machine Learning & Deep Learning Masterclass In One Semester Last updated: 2/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 16.94 GB | Duration: 46h 49m Practical Oriented Explanations by solving more than 80 projects with NumPy, Scikit-learn, Pandas, Matplotlib, PyTorch. What you'll learn Theory, Maths and Implementation of machine learning and deep learning algorithms. Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, and Random Forest Build Artificial Neural Networks and use them for Regression and Classification Problems Using GPU with Neural Networks and Deep Learning Models. Convolutional Neural Networks Transfer Learning Recurrent Neural Networks and LSTM Time series forecasting and classification. Autoencoders Generative Adversarial Networks (GANs) Python from scratch Numpy, Matplotlib, Seaborn, Pandas, Pytorch, Scikit-learn and other python libraries. More than 80 projects solved with Machine Learning and Deep Learning models Requirements Some Programming Knowledge is preferable but not necessary Gmail account ( For Google Colab ) Description IntroductionIntroduction of the CourseIntroduction to Machine Learning and Deep LearningIntroduction to Google ColabPython Crash CourseData PreprocessingSupervised Machine LearningRegression AnalysisLogistic RegressionK-Nearest Neighbor (KNN)Bayes Theorem and Naive Bayes ClassifierSupport Vector Machine (SVM)Decision TreesRandom ForestBoosting Methods in Machine LearningIntroduction to Neural Networks and Deep LearningActivation FunctionsLoss FunctionsBack PropagationNeural Networks for Regression AnalysisNeural Networks for ClassificationDropout Regularization and Batch NormalizationConvolutional Neural Network (CNN)Recurrent Neural Network (RNN)AutoencodersGenerative Adversarial Network (GAN)Unsupervised Machine LearningK-Means ClusteringHierarchical ClusteringDensity Based Spatial Clustering Of Applications With Noise (DBSCAN)Gaussian Mixture Model (GMM) ClusteringPrin[beeep]l Component Analysis (PCA)What you'll learnTheory, Maths and Implementation of machine learning and deep learning algorithms.Regression Analysis.Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.Build Artificial Neural Networks and use them for Regression and Classification Problems.Using GPU with Deep Learning Models.Convolutional Neural NetworksTransfer LearningRecurrent Neural NetworksTime series forecasting and classification.AutoencodersGenerative Adversarial NetworksPython from scratchNumpy, Matplotlib, seaborn, Pandas, Pytorch, scikit-learn and other python libraries.More than 80 projects solved with Machine Learning and Deep Learning models. 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Free Download Udemy - Master Machine Learning & AI with Python Published: 3/2025 Created by: Paul Carlo Tordecilla MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 62 Lectures ( 5h 4m ) | Size: 2.46 GB Building Intelligent Systems from the Ground Up What you'll learn Understand the theory behind machine learning algorithms, including supervised, unsupervised, and reinforcement learning. Learn data preprocessing, feature engineering, and visualization methods to prepare data for modeling. Gain hands-on experience building and evaluating models for regression, classification, clustering, and recommendation systems using Python. Explore deep learning, neural networks, generative models, and advanced topics like meta-learning, federated learning, and graph neural networks through real-wo Discover how to deploy machine learning models, optimize performance with distributed computing, and integrate AI solutions into applications. Requirements Familiarity with Python programming, including data types, control structures, and functions. A basic understanding of algebra, calculus, and statistics to grasp algorithmic concepts. Prior exposure to simple ML concepts or courses can be beneficial, though not mandatory for beginners. Working knowledge of libraries like NumPy and Pandas for data manipulation and analysis. A proactive attitude toward solving problems, experimenting with code, and building projects. Description Embark on a transformative journey into the world of Machine Learning and Artificial Intelligence with our comprehensive online course. Designed for beginners and intermediate learners alike, this course bridges theory and practice, enabling you to master key concepts, techniques, and tools that drive today's intelligent systems. Whether you're aiming to launch a career in data science, build innovative projects, or simply expand your technical prowess, this course provides the robust foundation and hands-on experience you need.What you'll learnIntroduction to Machine LearningWhat is Machine Learning?Understand the definition, historical evolution, and transformative impact of machine learning in various industries.Types of Machine Learning:Dive deep into supervised, unsupervised, and reinforcement learning with real-world applications.Applications & Tools:Explore practical use cases across industries and get acquainted with the Python ecosystem and essential libraries like NumPy, Pandas, and Scikit-Learn.Data PreprocessingUnderstanding Data:Learn to distinguish between structured and unstructured data, and use visualization techniques to explore datasets.Data Cleaning & Feature Engineering:Master techniques for handling missing data, encoding categorical variables, feature scaling, and engineering new features.Data Splitting:Get hands-on experience with training/testing splits and cross-validation to ensure robust model performance.Regression TechniquesStart with Simple Linear Regression and progress to Multiple Linear, Polynomial Regression, and more advanced methods like Support Vector Regression, Decision Tree, and Random Forest Regression.Learn how to tackle issues like multicollinearity, overfitting, and implement these models using Python.Classification TechniquesFoundational Algorithms:Gain insights into Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) for both binary and multiclass problems.Advanced Methods:Understand Naive Bayes, Decision Trees, and ensemble methods such as Random Forests and boosting algorithms like AdaBoost, GBM, and XGBoost.Deep Dive into XGBoost:Learn the introduction to XGBoost and explore its advanced concepts, making it a powerful tool for your classification tasks.Clustering TechniquesExplore unsupervised learning with K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models.Understand how to determine optimal cluster numbers and interpret dendrograms for meaningful insights.Association Rule LearningApriori & Eclat Algorithms:Learn how to mine frequent itemsets and derive association rules to uncover hidden patterns in data.Natural Language Processing (NLP)Text Processing Fundamentals:Delve into tokenization, stopword removal, stemming, and lemmatization.Vectorization Techniques:Build models using Bag of Words and TF-IDF, and explore sentiment analysis to interpret textual data.Deep LearningNeural Networks & Training:Understand the architecture, training processes (forward and backpropagation), and optimization techniques of neural networks.Specialized Networks:Learn about Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) including LSTM for sequence modeling.Hands-On with Keras & TensorFlow:Build, evaluate, and tune models using industry-standard frameworks.Why Enroll?Comprehensive Curriculum:Our course is meticulously structured to take you from foundational concepts to advanced machine learning techniques, ensuring a holistic understanding of the field.Hands-On Learning:With practical labs and real-world projects, you'll not only learn the theory but also gain the experience needed to implement your ideas effectively.Expert Guidance:Learn from seasoned professionals who bring real industry experience and cutting-edge insights into every lesson.Career Advancement:Gain in-demand skills that are highly valued in tech, finance, healthcare, and beyond, positioning you for success in a rapidly evolving job market.Community & Support:Join a vibrant community of learners and experts, engage in discussions, receive feedback, and collaborate on projects to accelerate your learning journey.Enroll Now!Don't miss this opportunity to transform your career with advanced skills in Machine Learning and AI. Whether you're aspiring to build intelligent systems, analyze complex data, or innovate in your current role, this course is your gateway to success. Secure your spot today and start building the future!Ready to revolutionize your learning journey? Enroll now and become a leader in the era of AI! Who this course is for Individuals looking to start a career in data science and machine learning with a solid practical foundation. Developers who want to expand their skill set to include AI and machine learning technologies. University students or researchers interested in applying ML concepts to academic projects or research problems. Professionals from various fields seeking to transition into roles that focus on data analytics and machine learning. Anyone passionate about technology, eager to build real-world AI projects and deepen their understanding of advanced ML techniques. 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Free Download Udemy - Advanced Machine Learning Methods and Techniques Published: 3/2025 Created by: Henrik Johansson MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English + subtitle | Duration: 18 Lectures ( 11h 15m ) | Size: 4.86 GB Learn Advanced Machine Learning Methods and Techniques for Data Analysis, Data Science, and Machine Learning What you'll learn Knowledge about Advanced Machine Learning methods, techniques, theory, best practices, and tasks Deep hands-on knowledge of Advanced Machine Learning and know how to handle Machine Learning tasks with confidence Advanced ensemble models such as the XGBoost models for prediction and classification Detailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, and Supervised Learning Hands-on knowledge of Scikit-learn, Matplotlib, Seaborn, and some other Python libraries Advanced knowledge of A.I. prediction/classification models and automatic model creation Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources And much more. Requirements The four ways of counting (+-*/) Some Experience with Data Science, or Data Analysis, or Machine Learning Python and preferably Pandas knowledge Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended Access to a computer with an internet connection 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 Advanced Machine Learning Methods and Techniques!Machine Learning is expanding and developing on a massive and global scale. Everywhere in society, there is a movement to implement and use Machine Learning Methods and Techniques to develop and optimize all aspects of our lives, businesses, societies, governments, and states.This course will teach you a useful selection of Advanced Machine Learning methods and techniques, which will give you an excellent foundation for Machine Learning jobs and studies. This course has exclusive content that will teach you many new things about Machine Learning methods and techniques.This is a two-in-one master class video course which will teach you to advanced Regression, Prediction, and Classification.You will learn advanced Regression, Regression analysis, Prediction and supervised learning. This course will teach you to use advanced feedforward neural networks and Decision tree regression ensemble models such as the XGBoost regression model.You will learn advanced Classification and supervised learning. You will learn to use advanced feedforward neural networks and Decision tree classifier ensembles such as the XGBoost Classifier model.You will learnKnowledge about Advanced Machine Learning methods, techniques, theory, best practices, and tasksDeep hands-on knowledge of Advanced Machine Learning and know how to handle Machine Learning tasks with confidenceAdvanced ensemble models such as the XGBoost models for prediction and classificationDetailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, and Supervised LearningHands-on knowledge of Scikit-learn, Matplotlib, Seaborn, and some other Python librariesAdvanced knowledge of A.I. prediction/classification models and automatic model creationCloud 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 lifeAnd much more.This course includesan easy-to-follow guide for using the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). You may learn to use Cloud Computing resources in this coursean easy-to-follow optional guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install a Python Data Science environment useful for this course or for any Machine Learning or coding taska large collection of unique content, and this course will teach you many new things that only can be learned from this course on UdemyA compact course structure built on a proven and professional framework for learning.This course is an excellent way to learn advanced Regression, Prediction, and Classification! These are the most important and useful tools for modeling, AI, and forecasting.Is this course for you?This course is an excellent choice forAnyone who wants to learn Advanced Machine Learning Methods and TechniquesAnyone who wants to study at the University level and want to learn Advanced Machine Learning skills that they will have use for in their entire career!This 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 Advanced Regression, Prediction, and classification.Course RequirementsThe four ways of counting (+-*/)Some Experience with Data Science, Data Analysis, or Machine LearningPython and preferably Pandas knowledgeEveryday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedAccess to a computer with an internet connectionThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedEnroll now to receive 10+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course! Who this course is for Anyone who wants to learn Advanced Machine Learning Methods and Techniques Anyone who wants to study at the University level and want to learn Advanced Machine Learning skills that they will have use for in their entire career! 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Free Download Udemy - Mathematics For Machine Learning by Daniel Yoo Published: 3/2025 Created by: Daniel Yoo MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 40 Lectures ( 9h 3m ) | Size: 3.9 GB The math you will need for your machine learning journey. What you'll learn People who want to learn the mathematics that drives machine learning models. Students who are not sure about data science as a career and want to give it a serious try without paying college level tuition Data scientists who want a refresher in mathematics. Students who want to have a solid foundation in mathematics to proceed to more advanced machine learning models. Product managers who want to know how data scientists and machine learning engineers think. Machine Learning Engineers, who know how to deploy models, but want to know what is actually going underneath the hood of these models. Requirements No programming or math experience necessary, foundational concepts are developed from scratch. Description This course provides a comprehensive foundation in the mathematical concepts essential for understanding and implementing machine learning algorithms from first principles. Students will explore Linear Algebra, covering vectors, matrices, eigenvalues, and singular value decomposition-critical for data representation and transformations. Multivariable Calculus will focus on gradients, Jacobians, and Hessians, which are fundamental to optimization techniques used in training models.The course also introduces Probability and Statistics, covering key topics such as random variables, probability distributions, expectation, variance, and fundamental statistical inference techniques. Optimization methods, including gradient descent and related algorithms, will be explored to understand how machine learning models learn from data. Additionally, students will develop problem-solving skills by working through mathematical proofs and derivations that underpin these techniques.Throughout the course, students will gain hands-on experience with NumPy and S[beeep], leveraging these powerful Python libraries to implement mathematical concepts programmatically. Rather than applying models to real-world datasets, the focus will be on understanding and building the mathematical foundations necessary for machine learning. By the end of the course, students will have the necessary mathematical and computational tools to derive and implement machine learning techniques from scratch, preparing them for deeper study in artificial intelligence and data science, as well as advanced mathematical modeling. Who this course is for Anybody who wants to understand the mathematics behind machine learning models. Students, who are not sure if data science is a viable career for them. Homepage: https://www.udemy.com/course/mathematics-for-machine-learning-o/ Rapidgator https://rg.to/file/0140294f45eaa49a507a33440d1792ab/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part2.rar.html https://rg.to/file/0412c1971050a07388c5ca434af90557/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part5.rar.html https://rg.to/file/60bad2cf528362906c1461b5774bebbe/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part4.rar.html https://rg.to/file/629ee60aece12d43886de3d09aac995f/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part3.rar.html https://rg.to/file/672b832e589cd7b97975f067ee9a61d5/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part1.rar.html Fikper Free Download https://fikper.com/ADCEhDRpnG/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part5.rar.html https://fikper.com/VoZ7UxJ58F/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part3.rar.html https://fikper.com/hEhqtFtn55/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part4.rar.html https://fikper.com/mee5t4T8Zv/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part1.rar.html https://fikper.com/n9XtIZX60u/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part2.rar.html No Password - Links are Interchangeable

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Free Download Linkedin - Google Cloud Professional Machine Learning Engineer Cert Prep Released: 03/2025 Duration: 7h 7m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 0.97 GB Level: Advanced | Genre: eLearning | Language: English Earning the Google Cloud Professional Machine Learning Engineer certification confirms that you're able to build, evaluate, productionize, and optimize AI solutions by using Google Cloud capabilities and knowledge of conventional ML approaches. In this course, Noah Gift prepares you for the certification, starting with an Overview of the exam-including the format of the exam, the time it should take, and how and where you can take the exam. Noah then dives into the six sections of the exam, covering what you need to know about: architecting low-code ML solutions; collaborating within and across teams to manage data and models; scaling prototypes into ML models; serving and scaling models; automating and orchestrating ML pipelines; and monitoring ML solutions. Homepage: https://www.linkedin.com/learning/google-cloud-professional-machine-learning-engineer-cert-prep Fileaxa https://fileaxa.com/hikqq0ho5pqj/kcbjk.Linkedin..Google.Cloud.Professional.Machine.Learning.Engineer.Cert.Prep.part1.rar https://fileaxa.com/ywqfd4db70i1/kcbjk.Linkedin..Google.Cloud.Professional.Machine.Learning.Engineer.Cert.Prep.part2.rar TakeFile https://takefile.link/dyn502tk339x/kcbjk.Linkedin..Google.Cloud.Professional.Machine.Learning.Engineer.Cert.Prep.part1.rar.html https://takefile.link/3f1xrh6g9yor/kcbjk.Linkedin..Google.Cloud.Professional.Machine.Learning.Engineer.Cert.Prep.part2.rar.html Rapidgator https://rg.to/file/607ada13b5f5579c892c701b747513fd/kcbjk.Linkedin..Google.Cloud.Professional.Machine.Learning.Engineer.Cert.Prep.part1.rar.html https://rg.to/file/564142897bad672ba5e7ba31ca5d9588/kcbjk.Linkedin..Google.Cloud.Professional.Machine.Learning.Engineer.Cert.Prep.part2.rar.html Fikper Free Download https://fikper.com/gzQyM5nLcP/kcbjk.Linkedin..Google.Cloud.Professional.Machine.Learning.Engineer.Cert.Prep.part1.rar.html https://fikper.com/StW9VHWvtl/kcbjk.Linkedin..Google.Cloud.Professional.Machine.Learning.Engineer.Cert.Prep.part2.rar.html No Password - Links are Interchangeable

Free Download Build a Full-Stack Machine Learning Web App In Production Published: 3/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 3h 4m | Size: 2.35 GB Build an AI document search web app with Flask and deploy it to production What you'll learn Become a Full-Stack AI/ML Engineer Build complex Flask web applications and websites Train BERT-like Deep Learning Models and deploy as an API Design distributed computing workloads with Celery and Redis Gain proficiency with Databases using PostgreSQL and SQLAlchemy Deploy websites to production with Railway Enhance your job portfolio, freelance work or even start your own SaaS Requirements A computer running Windows, OSX or Linux with at least 8GB of RAM Basic understanding of HTML, CSS and JavaScript Basic understanding of computer science and AI Description Build a Full-Stack ML Web App: From Model to ProductionAre you ready to become a highly-paid Machine Learning Engineer in today's AI revolution?Hi, I'm Dylan P., and as a Lead Machine Learning Engineer with over 5 years of experience at major tech companies, I've watched ML Engineering become the hottest job in tech. Why? Because companies desperately need professionals who can both build AI models AND deploy them to production.But here's the problem: Most courses either teach you theoretical ML modeling without real-world application, or web development without any ML integration. Neither prepares you for what companies actually need.That's why I've created this comprehensive course that bridges the gap and teaches you to build production-ready ML applications from start to finish.What makes this course different?Unlike tutorials that show you toy examples with disclaimers like "you wouldn't do this in production..." I'll show you the REAL way professionals build and deploy ML systems. The techniques in this course are battle-tested from my years building production ML systems:Use industry best practices and tools like Docker, Databases, Caching, Distributed Computing, Unit / Integration TestingSystem design that allows your app to scale up to thousands of users without breakingUtilize cutting-edge models from traditional ML to state-of-the-art Transformers and LLMsDeliver measurable business impact while optimizing cost and performance"This course provides exactly what I needed - not just theory, but practical implementation that translates directly to my work projects." - James WongHere's What you'll learn by taking my course:Full-Stack Development: Create both the front end and backend with Flask, Docker, and RedisML System Design: How to design an AI web app that can scale effectively Natural Language Processing: Train a BERT language model from scratch using PyTorch, Hugging Face, WandbProduction-Grade APIs: Turn an AI model into high performance APIs with FastAPIDatabase Integration: Connect your app with production databases with PostgreSQLDeployment Mastery: Take your application live using RailwayThe best part? By the end of this course, you'll have a complete, impressive project for your portfolio that demonstrates exactly the skills employers are desperately seeking.Who is this course for?Software engineers looking to transition into the lucrative field of ML engineeringData scientists who want to level up by learning deployment and production skillsCS students or mid career switchers who want to build up their portfolioFreelance Consultants/Entrepreneurs keen in creating their own ML-powered applications or SaaS products"I was stuck in data science theory for years. After this course, I finally know how to build end-to-end ML systems that actually solve real problems." - Emery LinCourse StructureEach chapter follows a hands-on approach:Learn: Clear slides introducing new concepts and technologiesWatch: Video walkthroughs of actual code implementationBuild: Hands-on coding to construct your applicationVisualize: See your results in actionChallenge: Chapter exercises to cement your understandingInvest in Your Future The skills taught in this course regularly command $120,000-$180,000+ salaries in the industry. As AI continues transforming every sector, these skills will only become more valuable.Don't waste months piecing together fragmented tutorials or building projects that don't reflect real-world Requirements. Join me, and in just a few weeks, you'll have mastered the complete skillset needed to thrive as a modern ML Engineer.Ready to become the ML Engineer companies are looking to hire? 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Free Download Machine Learning and AI in Cybersecurity by Chuck Easttom Released 3/2025 By Chuck Easttom MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 3h 8m | Size: 648 MB Course Outline Machine Learning and AI for Cybersecurity: Introduction Learning objectives 1.1 Current Status of Machine Learning for Cyber Security 1.2 Basics of Machine Learning 1.3 Data Mining Basics Learning objectives 2.1 Defensive Uses of Machine Learning 2.2 Offensive Uses of Machine Learning Learning objectives 3.1 TensorFlow Basics 3.2 More with TensorFlow 3.3 TensorFlow Issues 3.4 Neural Networks with TensorFlow Learning objectives 4.1 What Are Large Language Models? 4.2 ChatGPT and Alternatives 4.3 Deep Fakes Learning objectives 5.1 Defining Cyber Warfare 5.2 Weaponized Malware Learning objectives 6.1 Neural Network Variations 6.2 Clustering Algorithms Machine Learning and AI for Cybersecurity: Summary Rapidgator https://rg.to/file/c8999d2da084e68825c13c9fe454f8da/ypguk.Machine.Learning.and.AI.in.Cybersecurity.rar.html Fikper Free Download https://fikper.com/3u9bEfWuXN/ypguk.Machine.Learning.and.AI.in.Cybersecurity.rar.html No Password - Links are Interchangeable

Free Download The Complete Azure Machine Learning Course - 2025 Edition Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.24 GB | Duration: 4h 19m Master Machine Learning with Azure ML Studio - Build, Train & Deploy AI Models Using No-Code & Python. What you'll learn Learn about supervised, unsupervised, and reinforcement learning, key concepts like training data, models, predictions, and real-world applications. Navigate and utilize Azure ML Studio's tools, including Designer, Notebooks, Automated ML, and Model Management. Load, clean, transform, and engineer features using Azure ML Studio to optimize model performance. Use Azure ML Studio's visual interface and custom Python scripts to create, train, and evaluate machine learning models. Apply hyperparameter tuning, cross-validation, and automated ML techniques to enhance model accuracy and efficiency. Learn different model deployment strategies, including real-time inference, batch inference, and Edge deployments using Azure Kubernetes Service (AKS) and Azure Create reusable machine learning workflows using Azure ML Pipelines for training, evaluation, and deployment automation. Set up CI/CD pipelines, automate model retraining, monitor model drift, and ensure security and compliance with Azure DevOps. Work with GPT, DALL·E, Stable Diffusion, and Codex, fine-tune AI models, and apply responsible AI principles for fairness and transparency. Work through multiple demos, labs, and real-world projects to gain practical experience in Azure Machine Learning. Requirements Familiarity with Python syntax, data types, and simple programming concepts will be helpful but is not mandatory. Some awareness of cloud services, particularly Microsoft Azure, will be useful but not required. Concepts like averages, probability, and basic algebra will help in understanding machine learning models, but the course will explain these as needed. You'll need an Azure account to access Azure Machine Learning Studio and complete hands-on exercises. Since Azure ML Studio is cloud-based, you'll need a stable internet connection. The course runs entirely in Azure Machine Learning Studio, so no local installations are needed. If you don't have an Azure account, you can sign up for a free tier to access cloud-based ML tools. Description Machine learning is revolutionizing industries by enabling data-driven decision-making and automation. However, implementing machine learning models can be complex, requiring infrastructure setup, data processing, and model deployment. Microsoft Azure Machine Learning Studio simplifies this process by providing a cloud-based platform to build, train, and deploy machine learning models efficiently. This course is designed to help learners master Azure ML Studio through a structured, hands-on approach.This course covers the entire machine learning lifecycle, from understanding key concepts to deploying models in production environments. Learners will explore:Types of Machine Learning - Supervised, unsupervised, and reinforcement learning.Real-world applications in healthcare, finance, cybersecurity, and retail.Challenges in Machine Learning - Overfitting, data quality, interpretability, and scalability.Hands-on with Azure ML StudioThrough practical demonstrations, learners will:Navigate the Azure Machine Learning Studio interface and set up a workspace.Manage datasets, experiments, and models in a cloud-based environment.Preprocess data - Handle missing values, perform feature engineering, and split datasets for training.Use data transformation techniques - Standardization, normalization, one-hot encoding, and PCA.Building & Training Machine Learning ModelsLearners will explore different machine learning algorithms and techniques, including:Regression, classification, and clustering models in Azure ML Studio.Feature selection and hyperparameter tuning for better model performance.AutoML (Automated Machine Learning) for optimizing models with minimal effort.Ensemble learning methods such as Random Forests, Gradient Boosting, and Neural Networks.Model Deployment & OptimizationOnce models are trained, learners will dive into model deployment strategies:Real-time inference vs. batch inference using Azure Kubernetes Service (AKS) and Azure Functions.Security best practices - Role-Based Access Control (RBAC), compliance, and encryption. Monitoring model drift - Implementing tracking tools to detect performance degradation over time.Automating Machine Learning WorkflowsThis course includes Azure ML Pipelines to automate machine learning processes: Building end-to-end pipelines - Automate data ingestion, model training, and evaluation.Using custom Python scripts in ML pipelines.Monitoring and managing pipeline execution for scalability and efficiency.MLOps & CI/CD for Machine LearningLearners will gain practical knowledge of MLOps and CI/CD for ML models using:Azure DevOps & GitHub Actions for model versioning and retraining automation.CI/CD pipelines for seamless ML model updates.Techniques for model lifecycle management - Deployment, monitoring, and rollback strategies.Exploring Generative AI with Azure MLThis course also introduces Generative AI: Working with Azure OpenAI Services - GPT, DALL·E, and Codex. Fine-tuning AI models for domain-specific applications. Ethical AI considerations - Bias detection, explainability, and responsible AI practices. Overview Section 1: Introduction to Machine Learning and Azure Lecture 1 Definition and Overview of machine learning (ML) Lecture 2 Types of machine learning Supervised, Unsupervised, Reinforcement Learning. Lecture 3 Key concepts Training data, features, labels, models, predictions Lecture 4 Real-world applications of ML in industries such as healthcare, finance, and r Lecture 5 Challenges in machine learning Overfitting, underfitting, data quality, and in Lecture 6 Introduction to Azure ML Studio and its capabilities for building, training, a Lecture 7 Overview of the Azure Machine Learning workspace Datasets, experiments, models Lecture 8 Key components Designer, Notebooks, Automated ML, and Model Management Lecture 9 Key features Visual interface, AutoML, integration with Azure services (Data F Lecture 10 Scalability and flexibility with Azure Compute and storage options Lecture 11 Collaboration and sharing Team-based development and version control Lecture 12 Benefits Faster experimentation, model deployment, and continuous learning Lecture 13 Creating an Azure account Lecture 14 Exploring Azure Cloud Interface and Services Part-1 Lecture 15 Exploring Azure Cloud Interface and Services Part-2 Lecture 16 Exploring Azure Cloud Interface and Services Part-3 Lecture 17 Creating Azure ML Studio Lecture 18 Exploring key features and benefits of Azure ML Studio Lecture 19 Overview of resource management Workspaces, compute resources, and storage acc Lecture 20 Connecting to data sources and Azure services. Section 2: Data Basics and Preprocessing Lecture 21 Importing datasets from various sources local files, Azure Blob Storage, SQL d Lecture 22 Exploring dataset statistics and visualizing data distribution Lecture 23 Understanding data types (numerical, categorical, text, image) Lecture 24 DEMO Loading a dataset and exploring basic statistics in Azure ML Studio Lecture 25 Identifying and handling missing data ( Null, Nan Values ) Lecture 26 Outlier detection and treatment strategies Lecture 27 Removing duplicates and irrelevant issues Lecture 28 Correcting data types and formatting issues Lecture 29 DEMO - Cleaning a dataset by handling missing values and outliers in ML Studio Lecture 30 Exploring ML Studio Designer and Setting up an Experiment If you're new to ML and want a structured, hands-on introduction using Azure Machine Learning Studio, this course will provide step-by-step guidance.,If you have some knowledge of ML but want to scale your models using Azure's cloud-based ML tools, this course will help you learn model training, deployment, and automation.,If you work with data and want to transition into machine learning and AI, this course will teach you how to build, optimize, and deploy ML models efficiently in Azure ML Studio.,you're an Azure user, cloud engineer, or solutions architect, this course will teach you how to integrate Azure ML with cloud-based services for AI-driven solutions.,If you're a software developer or Python programmer looking to automate machine learning workflows and deploy AI solutions, this course will provide the skills you need.,If you're interested in MLOps, CI/CD for ML models, and automated retraining, this course covers end-to-end model lifecycle management in Azure ML.,If you work in healthcare, finance, retail, cybersecurity, or any data-driven industry, this course will show you how machine learning can solve real-world business problems. 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Free Download Udemy - Mathematics For Machine Learning And Llms Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.81 GB | Duration: 15h 28m How is math used in AI What you'll learn Machine Learning mathematics linear algebra, statistics, probability and calculus for machine learning How algorithms works How algorithms are parametrizided Requirements Basic notions of machine learning Description Machine Learning is one of the hottest technologies of our time! If you are new to ML and want to become a Data Scientist, you need to understand the mathematics behind ML algorithms. There is no way around it. It is an intrinsic part of the role of a Data Scientist and any recruiter or experienced professional will attest to that. The enthusiast who is interested in learning more about the magic behind Machine Learning algorithms currently faces a daunting set of prerequisites: Programming, Large Scale Data Analysis, mathematical structures associated with models and knowledge of the application itself. A common complaint of mathematics students around the world is that the topics covered seem to have little relevance to practical problems. But that is not the case with Machine Learning.This course is not designed to make you a Mathematician, but it does provide a practical approach to working with data and focuses on the key mathematical concepts that you will encounter in machine learning studies. It is designed to fill in the gaps for students who have missed these key concepts as part of their formal education, or who need to catch up after a long break from studying mathematics.Upon completing the course, students will be equipped to understand and apply mathematical concepts to analyze and develop machine learning models, including Large Language Models. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 The Learning Diagram Lecture 3 Python Section 2: Types of Learning Lecture 4 Supervised Learnimg Lecture 5 Unsupervised Learning Lecture 6 Reinforcement Learning Lecture 7 When to Use and Not to Use ML Lecture 8 How to chose ML Algorithms Section 3: Data Preparation Lecture 9 Preeliminar Analysis Lecture 10 The Target Variable Lecture 11 Missing Data Lecture 12 Log Transformation - Homocedasticity Lecture 13 Outliers and Anomaly Detection Lecture 14 Data Transformation Lecture 15 Data Transformation (cont.) Section 4: Statistics in the Context off ML Lecture 16 Significant Differences Lecture 17 Descriptive and Inferential Statistics Section 5: Descriptive Statistics Lecture 18 Variables and Metrics Lecture 19 Correlation and Covariance Section 6: Probabilities for ML Lecture 20 Uncertainity Lecture 21 Frquentist versus Bayesian Probabilities Lecture 22 Random Variables and Sampling Lecture 23 Sampling Spaces Lecture 24 Basic Definitions of Probabilities Lecture 25 Axions, Theorems, Independence Lecture 26 Conditional Probability Lecture 27 Bayes Theorem and Naive Bayes Algorithm Lecture 28 Expectation, Chance and Likelihood Lecture 29 Maximum Likelihood Estimation (MLE) Lecture 30 Simulations Lecture 31 Monte Carlo Simulation, Markov Chainn Lecture 32 Probability Distributions Lecture 33 Families of Distributions Lecture 34 Normal Distribution Lecture 35 Tests for Normality Lecture 36 Exponential Distribution Lecture 37 Weibull Distribution and Survival Analysis Lecture 38 Binomial Distribution Lecture 39 Poisson Distribution Section 7: Statiscs Tests Lecture 40 Hypothesis Testing Lecture 41 The p- value Lecture 42 Critical Value, Significance, Confidence, CLT, LLN Lecture 43 Z and T Tests Lecture 44 Degrees of Freedom and F statistics Lecture 45 ANOVA Lecture 46 Chi Squared Test Lecture 47 Statistical Power Lecture 48 Robustness and Statistical Sufficiency Section 8: Time Series Lecture 49 Times Series Decommposition Lecture 50 Autoregressive Models Lecture 51 Arima Section 9: Linear ad Non Linear Models Lecture 52 Linear and Non Linear Models Section 10: Linear Algebra for ML Lecture 53 Introduction to Linear Algebra Lecture 54 Types of Matrices Lecture 55 Matrices Operations Lecture 56 Linear Transformations Lecture 57 Matrix Decomposition and Tensors Section 11: Calculus for ML Lecture 58 Functions Lecture 59 Limits Lecture 60 The Derivative Lecture 61 Calculating the Derivative Lecture 62 Maximum and Minimum Lecture 63 Analitical vs Numerical Solutions Lecture 64 Numerical and Analytic Solution Lecture 65 Gradient Descent Section 12: Distances, Similarities, knn and k means Lecture 66 Distance Measurements Lecture 67 Similarities Lecture 68 Knn and K means Lecture 69 Distances in Python Section 13: Training, Testing ,Validation Lecture 70 Training, Testin, Validation Lecture 71 Training, Testing, Validation (cont) Section 14: The Cost Function Lecture 72 The Cost Function Lecture 73 Cost Function for Regression and Classification Lecture 74 Minimazing the Cost Function with Gradient Descent Lecture 75 Batch annd Stochastic Gradient Descent Section 15: Bias and Variance Lecture 76 Bias and Variance Introduction Lecture 77 Complexity Lecture 78 Regularization Lecture 79 Regularization (Cont) Section 16: Parametric andd Non Parametric Algorithms Lecture 80 Parametric and Non Parametric Algorithms Section 17: Learning Curves Lecture 81 Learning Curves Lecture 82 Learning Curves in Python Section 18: Dimensionality Reduction Lecture 83 PCA and SCD Lecture 84 Eigenvectors and Eigenvalues Lecture 85 Dimensionality Reduction in Python Section 19: Entropy and Information Gain Lecture 86 Entropy and Information Gain Lecture 87 Entropy and Information Gain (cont) Section 20: Linear Regression Lecture 88 Linear Regression Lecture 89 Linear Regression (cont) Lecture 90 Polinomial Regression Section 21: Classification Lecture 91 Logistic Function Lecture 92 Generalized Linear Models (GLM) Lecture 93 Decision Boundaries Lecture 94 Confusion Matrix Lecture 95 ROC and AUC Lecture 96 Visualization of Class Distribution Lecture 97 Precision and Recall Section 22: Decision Trees Lecture 98 Introduction to Decision Trees Lecture 99 Gini Index Lecture 100 Hyperparameters Lecture 101 Decision Trees in Python Section 23: Suport Vector Machines Lecture 102 Introduction to SVMs Lecture 103 Introduction to SVMs (cont) Lecture 104 Mathematics of SVMs Lecture 105 SVM in Python Section 24: Ensemble Algorithms Lecture 106 Wisdom of the Crowds Lecture 107 Bagging and Random Forest Lecture 108 Adaboost, Gradient Boosting, XGBoosting Section 25: Natural Language Processing Lecture 109 Introduction to NLP Lecture 110 Tokenization and Embeddings Lecture 111 Weights and Representation Lecture 112 Sequences and Sentiment Analysis Section 26: Neural Networks Lecture 113 Mathematical Model of Artificial Neuron Lecture 114 Activation Functions Lecture 115 Activation Functions (cont) Lecture 116 Weights and Bias Parameters Lecture 117 Feedforward and Backpropagation Concepts Lecture 118 Feedforward Process Lecture 119 Backpropagation Process Lecture 120 Recurent Neural Networks (RNN) Lecture 121 Convolution Neural Networks (CNN) Lecture 122 Convolution Neural Networks (CNN) (cont) Lecture 123 Seq2Seq and Aplications of NN Section 27: Large Language Models Lecture 124 Generative vs Descriptive AI Lecture 125 LLMs Properties Section 28: Transformers Lecture 126 Introduction to Transformers Lecture 127 Training and Inference Lecture 128 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Free Download Udemy - Machine Learning Masterclass (2025) Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 8.73 GB | Duration: 26h 3m Combine Theory and Practice and become a Machine Learning Expert. Learn the basics of math and make real applications. What you'll learn Understand the fundamentals of Machine Learning and its real-world applications. Implement ML models using Python, TensorFlow, PyTorch, and Scikit-learn. Preprocess data, perform feature engineering, and optimize models effectively. Build, evaluate, and deploy ML models for classification, regression, and clustering. Requirements No prior knowledge of Machine Learning is required. The course covers everything from the basics. Basic Python programming knowledge is helpful but not mandatory. A Python introduction section is included. A computer with internet access and the ability to install Python-related libraries. Enthusiasm to learn and apply Machine Learning concepts in real-world scenarios. Description Master Machine Learning: A Complete Guide from Fundamentals to Advanced TechniquesMachine Learning (ML) is rapidly transforming industries, making it one of the most in-demand skills in the modern workforce. Whether you are a beginner looking to enter the field or an experienced professional seeking to deepen your understanding, this course offers a structured, in-depth approach to Machine Learning, covering both theoretical concepts and practical implementation.This course is designed to help you master Machine Learning step by step, providing a clear roadmap from fundamental concepts to advanced applications. We start with the basics, covering the foundations of ML, including data preprocessing, mathematical principles, and the core algorithms used in supervised and unsupervised learning. As the course progresses, we dive into more advanced topics, including deep learning, reinforcement learning, and explainable AI.What You Will LearnThe fundamental principles of Machine Learning, including its history, key concepts, and real-world applicationsEssential mathematical foundations, such as vectors, linear algebra, probability theory, optimization, and gradient descentHow to use Python and key libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch for building ML modelsData preprocessing techniques, including handling missing values, feature scaling, and feature engineeringSupervised learning algorithms, such as Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, and Naive BayesUnsupervised learning techniques, including Clustering (K-Means, Hierarchical, DBSCAN) and Dimensionality Reduction (PCA, LDA)How to measure model accuracy using various performance metrics, such as precision, recall, F1-score, ROC-AUC, and log lossTechniques for model selection and hyperparameter tuning, including Grid Search, Random Search, and Cross-ValidationRegularization methods such as Ridge, Lasso, and Elastic Net to prevent overfittingIntroduction to Neural Networks and Deep Learning, including architectures like CNNs, RNNs, LSTMs, GANs, and TransformersAdvanced topics such as Bayesian Inference, Markov Decision Processes, Monte Carlo Methods, and Reinforcement LearningThe principles of Explainable AI (XAI), including SHAP and LIME for model interpretabilityAn Overview of AutoML and MLOps for deploying and managing machine learning models in productionWhy Take This Course?This course stands out by offering a balanced mix of theory and hands-on coding. Many courses either focus too much on theoretical concepts without practical implementation or dive straight into coding without explaining the underlying principles. Here, we ensure that you understand both the "why" and the "how" behind each concept.Beginner-Friendly Yet Comprehensive: No prior ML experience required, but the course covers everything from the basics to advanced conceptsHands-On Approach: Practical coding exercises using real-world datasets to reinforce learningClear, Intuitive Explanations: Every concept is explained step by step with logical reasoningTaught by an Experienced Instructor: Guidance from a professional with expertise in Machine Learning, AI, and OptimizationBy the end of this course, you will have the knowledge and skills to confidently build, evaluate, and optimize machine learning models for various applications.If you are looking for a structured, well-organized course that takes you from the fundamentals to advanced topics, this is the right course for you. Enroll today and take the first step toward mastering Machine Learning. 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Free Download Udemy - Ai, Machine Learning, Statistics & Python Published: 2/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.33 GB | Duration: 5h 16m AI/ML : Overview, Statistics, Python, Machine learning, Methods, Use Cases in Telecom What you'll learn AI Basics Machine Learning Overview Types of Machine Learning Deep Learning Applications in Telecom Introduction to Statistics Overview of Python & its libraries Descriptive Statistics Central Tendency, Dispersion & Visualization (hands on - excel & python) Probability and Distributions Normal, Binomial & Poisson Distribution (hands on - excel & python) Inferential Statistics Hypothesis testing (t-tests) Introduction to Supervised Learning Linear Regression Hypothesis, Cost function, Gradient Descent, Regularization Logistic Regression Sigmoid Function, Decision Boundary, Anomaly detection Use cases in Telecom Requirements It is a course for everyone from beginner to expert level Description This course provides a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML) with a focus on applications in the telecommunications industry. Learners will begin with an Overview of AI/ML concepts, followed by a deep dive into essential statistical foundations and Python programming for data analysis. The course covers key machine learning techniques, including supervised and unsupervised learning, model evaluation, and optimization methods. Finally, real-world use cases in telecom, such as network optimization, fraud detection, and customer experience enhancement, will be explored. By the end of the course, parti[beeep]nts will have a strong foundation in AI/ML and its practical implementations.Course includes -AI BasicsMachine Learning OverviewTypes of Machine LearningDeep LearningApplications in TelecomIntroduction to Statistics ·Overview of Python & its libraries ·Descriptive StatisticsCentral Tendency, Dispersion & Visualization (hands on - excel & python)Probability and DistributionsNormal, Binomial & Poisson Distribution (hands on - excel & python)Inferential StatisticsHypothesis testing (t-tests)Confidence IntervalIntroduction to Supervised LearningLinear RegressionHypothesis, Cost function, Gradient Descent, RegularizationExample of telecom networkLogistic RegressionSigmoid Function, Decision Boundary, Anomaly detectionExample of telecom networkThroughout the course, parti[beeep]nts will engage in hands-on projects and case studies, applying AI/ML techniques to real telecom datasets. By the end of the program, learners will have a strong technical foundation in AI/ML, practical coding skills, and the ability to implement AI-driven solutions tailored to the telecommunications sector. Overview Section 1: Introduction to AI & ML Lecture 1 Introduction Lecture 2 AI & ML Basics Lecture 3 Machine Learning & its use cases Lecture 4 Deep Learning & its use cases Lecture 5 GenAI & its use cases Lecture 6 Types of Machine learning Lecture 7 Machine Learning in Telecom Section 2: Statistics & Python: Foundation of AI/ML Lecture 8 Introduction Lecture 9 Statistics Basics Lecture 10 Overview of Python Lecture 11 Loop function in Python Lecture 12 Conditional Statements & Visualization in Python Lecture 13 Descriptive Statistics : Central Tendency Lecture 14 Descriptive Statistics : Dispersion Lecture 15 Descriptive Statistics : Visualization Lecture 16 Data Distributions - Basics Lecture 17 Probability Distribution Lecture 18 Normal Distribution Lecture 19 Z - Score Lecture 20 Binomial Distribution Lecture 21 Poisson Distribution Lecture 22 Bayes' Theorem Lecture 23 Inferential Statistics Lecture 24 t - tests Section 3: Supervised Learning Lecture 25 Supervised Learning Overview Lecture 26 Linear Regression Lecture 27 Logistic Regression Overview Lecture 28 Logistic Regression : Decision Boundary Lecture 29 Logistic Regression : Cost Function Lecture 30 Logistic Regression : Gradient Descent Suitable for the engineers working in AI and IT/Telecom space or planning to get into technical domain of AI/ML and Telecom,Suitable for Managers working in telecom operators and planning to deploy or manage ML models in Telecom networks,Suitable for beginners who are interested to get into telecom domain and learn new technology such as AI/ML Homepage: https://www.udemy.com/course/ai-machine-learning-statistics-python/ DOWNLOAD NOW: Udemy - Ai, Machine Learning, Statistics & Python Rapidgator https://rg.to/file/267aac983a556e5b5796ba6b74860fa3/agmdu.Ai.Machine.Learning.Statistics..Python.part2.rar.html https://rg.to/file/3869d90c1d543a276fe60ff1a6759b2b/agmdu.Ai.Machine.Learning.Statistics..Python.part1.rar.html Fikper Free Download https://fikper.com/DhWaxyLVKX/agmdu.Ai.Machine.Learning.Statistics..Python.part1.rar.html https://fikper.com/hbPPqE3g1F/agmdu.Ai.Machine.Learning.Statistics..Python.part2.rar.html : No Password - Links are Interchangeable

Free Download Linear Regression Machine Learning Forecasts. Co2 Case Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.98 GB | Duration: 3h 0m Master Predictive Analytics in Pyrhon by Building Accurate CO2 Emission Forecasts with Linear Regression What you'll learn learn about linear regression learn machine learning learn neural net assess CO 2 performance Requirements No prerequisites Description Welcome to the comprehensive course, "Linear Regression Machine Learning Forecasts: CO₂ Case," designed to equip you with powerful forecasting skills using linear regression techniques. Throughout this course, you'll gain practical insights by forecasting CO₂ emissions up to the year 2050, utilizing historical emissions data. Our rigorous, clearly structured 10-step methodology ensures your forecasts are scientifically robust, statistically valid, and highly reliable, setting you apart in data-driven decision-making roles.The course is enriched with practical case studies covering multiple key regions, including India, China, the USA, the UK, France, the European Union, and the global average. By examining diverse economies, you'll master how regional differences and trends impact CO₂ emissions, enabling you to generate tailored, precise forecasts. Each forecasting exercise leverages real-world datasets and comprehensive statistical analyses, reinforcing your expertise and building confidence in applying linear regression techniques to environmental and economic scenarios.To guarantee the highest accuracy in your predictions, you'll learn to rigorously implement advanced statistical tests such as residual analysis, goodness-of-fit measures, and hypothesis testing. You'll discover how to validate your forecasts systematically, quantify uncertainties, and interpret results effectively. By the end of this course, you'll be adept at producing credible long-term CO₂ emission forecasts, capable of influencing policy, business strategies, and sustainable planning initiatives worldwide. engineers,ML practitioners,students,energy professionals Homepage: https://www.udemy.com/course/linear-regression-machine-learning-forecasts-co2-case/ Rapidgator https://rg.to/file/8026bc2ff39340fcaa3205eaace4be2a/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part1.rar.html https://rg.to/file/c3c8968c89959ea148046b05f46b920b/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part3.rar.html https://rg.to/file/f40e0ccf559f8efc7f9e3de2e8879183/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part2.rar.html Fikper Free Download https://fikper.com/P4tyOt0V6H/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part1.rar.html https://fikper.com/kxBr9HQPnT/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part2.rar.html https://fikper.com/vgT3lcKjC5/tdxmw.Linear.Regression.Machine.Learning.Forecasts..Co2.Case.part3.rar.html : No Password - Links are Interchangeable

Free Download Linkedin - Machine Learning Foundations Prototyping with Edge Impulse Released: 03/2025 Duration: 1h 9m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 186 MB Level: Beginner | Genre: eLearning | Language: English Explore the world of machine learning on edge devices with this hands-on course. Robert Gallup-a technologist, designer, and maker-guides you through basic machine learning concepts and workflow. Set up the necessary tools and hardware to develop a voice-driven prototype using the Arduino Nano 33 BLE Sense microcontroller. Discover how to use the Edge Impulse platform to acquire data, train a machine learning model, and generate code for your prototype. Upload and modify the code to complete your prototype using the Arduino IDE. Finally, explore practical challenges in deploying ethical machine learning on edge devices. By the end of this course, you'll be equipped to create your own intelligent prototypes, enhancing your technical portfolio and practical problem-solving abilities. Homepage: https://www.linkedin.com/learning/machine-learning-foundations-prototyping-with-edge-impulse DOWNLOAD NOW: Linkedin - Machine Learning Foundations Prototyping with Edge Impulse Fileaxa https://fileaxa.com/vo62ry2zjt8u/gkkty.Linkedin..Machine.Learning.Foundations.Prototyping.with.Edge.Impulse.rar TakeFile https://takefile.link/e18nbkwavhis/gkkty.Linkedin..Machine.Learning.Foundations.Prototyping.with.Edge.Impulse.rar.html Rapidgator https://rg.to/file/6f91af7078055258746853a3f1f72562/gkkty.Linkedin..Machine.Learning.Foundations.Prototyping.with.Edge.Impulse.rar.html Fikper Free Download https://fikper.com/Enl5vs2cBQ/gkkty.Linkedin..Machine.Learning.Foundations.Prototyping.with.Edge.Impulse.rar.html : No Password - Links are Interchangeable

Free Download Udemy - Machine Learning for Absolute Beginners (2025) Published: 3/2025 Created by: Rajita Yerramilli MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 5 Lectures ( 2h 27m ) | Size: 1.82 GB Using a Curiosity-Based Learning Method with Gen AI Prompts (Patent-Pending) What you'll learn Build a Linear Regression Model Build an Random Forest Model Build a KNN Model Learn some fundamental math around building models and forecasting Requirements Ability to understand English and use a browser Description This is course geared towards non-technical folks who want to understand use cases of Machine Learning (ML) models and build those models without any prerequisite technical skills. You will have a ready-made environment and you can get going within minutes. With the advent of Gen AI, you don't have to be an engineer to go build a cool app and make mind-blowing predictions that will accelerate your business. Dazzle your C-Suite with your newly acquired model-building skills that allow you to make predictions on your business dataThis course is also suitable for technical folks who have zero knowledge in Machine Learning. It's not suitable for Intermediate or Advanced ML professionals. You will learn various algorithms and also get up to speed on the math behind some of the models. Let your curiosity drive your learning. Let me show you how to transcend beyond the limits of the course to both broaden and deepen your learning. Don't hesitate, start today before someone else beats you to the finish line and gets that big fat bonus that will be waiting for you once you upskill.Machine learning is the present and he future. It's time to leave the old mindsets behind and dive into a whole world of wonder and bewilderment. Who this course is for Non-technical folks who are ML-curious and technical folks who want to kickstart their ML journey Homepage: https://www.udemy.com/course/ml-beginner/ DOWNLOAD NOW: Udemy - Machine Learning for Absolute Beginners (2025) Rapidgator https://rg.to/file/e3812534f6d1546ffc42c2e40a5b837c/fckgi.Machine.Learning.for.Absolute.Beginners.2025.part1.rar.html https://rg.to/file/97b389f0c613ee735b583d92ef6af211/fckgi.Machine.Learning.for.Absolute.Beginners.2025.part2.rar.html Fikper Free Download https://fikper.com/Cv4OOX6VKB/fckgi.Machine.Learning.for.Absolute.Beginners.2025.part1.rar.html https://fikper.com/inMe53jexs/fckgi.Machine.Learning.for.Absolute.Beginners.2025.part2.rar.html : No Password - Links are Interchangeable

Free Download Linkedin - Applied Machine Learning Ensemble Learning (2025) Released 02/2025 With Matt Harrison MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 28m 23s | Size: 208 MB Learn to use ensemble techniques like bagging, boosting, and stacking to improve your machine learning models. Course details Do you want to grow your skills as a machine learning practitioner, but don't know where to begin? You don't need any formal training in data science to start working toward your goal. In this course, instructor Matt Harrison guides you through the key concepts of ensemble learning. Explore different ensemble methods like bagging, boosting, and stacking and learn to implement them using popular Python libraries such as scikit-learn and XGBoost. By the end of this course, you'll be equipped with the skills you need to implement and optimize ensemble models in real-world machine learning tasks.This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time-all while using a tool that you'll likely encounter in the workplace. Check out "Using GitHub Codespaces" with this course to learn how to get started. Homepage: https://www.linkedin.com/learning/applied-machine-learning-ensemble-learning-25317548 DOWNLOAD NOW: Linkedin - Applied Machine Learning Ensemble Learning (2025) Fileaxa https://fileaxa.com/yf94nq45k7ol/dmxqt.Applied.Machine.Learning.Ensemble.Learning.2025.rar TakeFile https://takefile.link/ek9egc25bhz6/dmxqt.Applied.Machine.Learning.Ensemble.Learning.2025.rar.html Rapidgator https://rg.to/file/7f641bf9d0f4fe765651e15260e2775d/dmxqt.Applied.Machine.Learning.Ensemble.Learning.2025.rar.html Fikper Free Download https://fikper.com/CJWhNhYcJw/dmxqt.Applied.Machine.Learning.Ensemble.Learning.2025.rar.html : No Password - Links are Interchangeable

Free Download Udemy - Generative Ai And Machine Learning With Python Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 9.05 GB | Duration: 19h 30m Unlock the Power of Machine Learning and Generative AI What you'll learn Implement and evaluate machine learning models in Python. Apply dimensionality reduction and clustering techniques. Understand and explain core generative AI models. Build and train Artificial Neural Networks (ANNs) and Multi-Layer Perceptrons (MLPs) using Keras. Requirements Basic Programming in Python Description Unlock the Power of Machine Learning and Generative AIThis comprehensive course provides a deep dive into the core concepts and practical applications of machine learning and generative AI. Starting with foundational principles like supervised, unsupervised, and reinforcement learning, you'll progress through data preprocessing, evaluation metrics, and essential algorithms like linear and logistic regression, decision trees, and random forests.Dive into unsupervised learning with K-means clustering and Prin[beeep]l Component Analysis (PCA), mastering dimensionality reduction. Transition to deep learning with Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Multi-Layer Perceptrons (MLPs) using Keras.Finally, explore the cutting edge of generative AI, including Transformer attention mechanisms, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Recurrent Neural Networks (RNNs), and Gated Recurrent Units (GRUs).Course Highlights:Practical Labs: Hands-on experience coding in Python, solidifying your understanding of key algorithms.Comprehensive Coverage: From fundamental machine learning to advanced generative AI techniques.Detailed Evaluation: Learn to assess model performance with various metrics and confusion matrices.Deep Learning Mastery: Implement and train neural networks using Keras.Generative AI Exploration: Demystify Transformers, GANs, VAEs, and RNNs.Regular Quizzes: Reinforce learning with quizzes after each module.This course is designed for anyone seeking a robust understanding of machine learning and generative AI, from beginners to those looking to expand their knowledge. 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Free Download NumPy, Pandas and Matplotlib A-Z for Machine Learning Last updated: 6/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 4.1 GB | Duration: 11h 43m Python NumPy, Pandas, and Matplotlib for Data Analysis, Data Science and Machine Learning. Pre-machine learning Analysis What you'll learn Go from absolute beginner to become a confident Python NumPy, Pandas and Matplotlib user Dare to get the most out of Python NumPy, Pandas and Matplotlib Go deeper to understand complex topics in Python NumPy, Pandas and data visualisation Learn Python NumPy, Pandas and Matplotlib through several exercises and solutions Acquire the required Python NumPy, Pandas and Matplotlib knowledge you need to excel in Data Science, Machine Learning, Ai and Deep Learning Be trained by expert Requirements Just a little knowledge of Python Description Welcome to NumPy, Pandas and Matplotlib A-Z™: for Machine Learning NumPy is a leading scientific computing library in Python while Pandas is for data manipulation and analysis. Also, learn to use Matplotlib for data visualization. Whether you are trying to go into Data Science, dive into machine learning, or deep learning, NumPy and Pandas are the top Modules in Python you should understand to make the journey smooth for you. In this course, we are going to start from the basics of Python NumPy and Pandas to the advanced NumPy and Pandas. This course will give you a solid understanding of NumPy, Pandas, and their functions. At the end of the course, you should be able to write complex arrays for real-life projects, manipulate and analyze real-world data using Pandas. WHO IS THIS COURSE FOR? √ This course is for you if you want to learn NumPy, Pandas, and Matplotlib for the first time or get a deeper knowledge of NumPy and Pandas to increase your productivity with deep and Machine learning. √ This course is for you if you are coming from other programming languages and want to learn Python NumPy and Pandas fast and know it really well. √ This course is for you if you are tired of NumPy, Pandas, and Matplotlib courses that are too brief, too simple, or too complicated. √ This course is for you if you want to build real-world applications using NumPy or Panda and visualize them with Matplotlib. √ This course is for you if you have to get the prerequisite knowledge to understanding Data Science and Machine Learning using NumPy and Pandas. √ This course is for you if you want to master the in-and-out of NumPy, Pandas, and data visualization. √ This course is for you if you want to learn NumPy and Pandas by doing exciting real-life challenges that will distinguish you from the crowd. √ This course is for you if plan to pass an interview soon. Who this course is for All levels of students Rapidgator https://rg.to/file/068122ac43a610f940afa3f56db2fd70/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part2.rar.html https://rg.to/file/18fdba0a0338377958f8a10aca19b855/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part4.rar.html https://rg.to/file/7778b169df9ca53db485e4e6e946423d/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part1.rar.html https://rg.to/file/86f48e0f1a8a75b7be7e605aa59bce60/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part5.rar.html https://rg.to/file/fbc97bf058a54f3b9bcadff2ee53a35f/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part3.rar.html Fikper Free Download https://fikper.com/1wfb4A62ep/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part2.rar.html https://fikper.com/LcIkEB9KG9/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part4.rar.html https://fikper.com/Y9hyFkhhwQ/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part3.rar.html https://fikper.com/ij5G40yBVT/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part5.rar.html https://fikper.com/yPxkHLme8w/amhpj.NumPy.Pandas.and.Matplotlib.AZ.for.Machine.Learning.part1.rar.html : No Password - Links are Interchangeable

Free Download Full Stack Machine Learning - Django REST Framework, React Published: 2/2025 Created by: Rathan Kumar MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 182 Lectures ( 18h 33m ) | Size: 8.88 GB Learn to Build full-fledged Stock Prediction Portal using Python, Django REST Framework, React.js and Machine Learning What you'll learn REST API Development Backend Development with Django and Frontend with React JS Machine Learning with Neural Networks Deep Learning with LSTM Models Data Analysis, Data Manipulation and Data Visualization How to decide which type of machine learning to use for specific problems. Where deep learning comes in and how neural networks work. Why a neural network is the best choice for this specific stock prediction use case. Integration of Machine Learning Models with Web Applications Requirements Basic knowledge of Python & Django Basic Knowledge of HTML, CSS and JavaScript Description Not just another course, this is a hands-on program where you'll build a complete, stock prediction portal using Django REST Framework, React.js, and Machine Learning. Course Flow:First, you'll learn the fundamentals of Django REST Framework, including what REST APIs are and how to create them. If you're already familiar with Django REST Framework, you can skip this section.Next, we'll dive into the fundamentals of React.js to build the front-end of our application.After that, we'll connect Django REST Framework with React.js to build the portal. This will include implementing a user authentication system and other essential features needed for a functional application.Once the portal structure is ready, it's time to dive into machine learning. This course is not a Machine Learning Bootcamp, so it won't cover every ML concept in detail. Instead, it takes a practical approach focused on building a stock prediction portal as a real-world use case.Machine Learning Section:The basics of machine learning and its different types.How to choose the right ML approach for a specific problem.When and why to use deep learning and how neural networks work.Why a neural network is the best choice for this stock prediction use case.You'll build an LSTM model in Jupyter Notebook to analyze stock price data and make predictions. Once the model is ready, you'll create an API to integrate it with the portal and display the results.This course gives you the full experience of building a real-world stock prediction portal-a full-stack project combining Django REST Framework, React.js, and machine learning.Additional Skills You'll Learn:Data manipulation using Pandas and NumPy.Data visualization using Matplotlib.By the end of this course, you'll have built a complete project while gaining hands-on experience in both web development and machine learning.Important Disclaimer: This prediction model should NOT be implemented in real stock market trading. It is developed purely for educational purposes to help you understand the principles of machine learning and stock market data. Relying on this model for actual investments can lead to significant financial risks. Who this course is for Beginner programmers who want to learn how to build web applications using Python, Django & React Developers with experience in other programming languages who want to transition to Machine Learning Students who are interested in pursuing a career in full stack machine learning development Anyone who wants to improve their knowledge of Django and build upon their existing Python skills Individuals who have some experience with Django but want to level up their skills by building advanced custom projects. 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