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Street Machine Australia - December 2024 English | 148 pages | True PDF | 96.5 MB Street Machine is the country's biggest selling, most widely read and most respected modified car magazine. Combining great photography with accurate, expert coverage of the Aussie modified car scene and in-depth technical features, Street Machine celebrates Australia's passion for older cars, V8s and the lifestyle that surrounds them. https://fikper.com/gEnGBl0DLQ/ https://fileaxa.com/9g2rjdv1nyve https://ddownload.com/qbdjncg1sila https://rapidgator.net/file/69042cd3338e0ef98b0a220d4a7b8e24/ https://turbobit.net/d3pnpzbqwoat.html

Free Download Streamlit Deployer son app de Machine Learning sur le web Last updated 9/2024 Created by Pierre-louis Danieau MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: French + subtitle | Duration: 25 Lectures ( 4h 38m ) | Size: 1.74 GB Créez rapidement une superbe application web et déployez votre modèle d'IA dans le monde entier avec Python ! What you'll learn Savoir utiliser Streamlit Développer et déployer son application Data afin de partager ses modèles de Machine Learning sur le web Scrapper de la Data en temps réel grâce à une API (Yahoo Finance) Utilisation de Streamlit Cloud Créer des visuels attrayants avec les librairies interactives de Python Créer une interface utilisateur attractive (UI / UX) Structurer son programme Python pour du développement web Savoir optimiser une application Streamlit (Cache / Session / Form...) Utilisation de Git et Github Surpasser le Jupyter Notebook et donner vie à son projet Data Requirements Une connaissance élémentaire du language de programmation Python est requise pour mieux comprendre les concepts abordés dans cette formation. De simples connaissances suffisent. Aucune compétence en développement web et/ou en data engineering n'est nécessaire. L'ensemble des concepts sont abordés depuis le début. Aucune expérience dans le cloud n'est requise. Vous apprendrez tout ce qu'il est utile de savoir pour la partie déploiement / mise en production. Description Avez-vous déjà ressenti la frustration d'avoir développé un super modèle de Machine Learning sur votre Jupyter Notebook et de ne jamais pouvoir le confronter à une utilisation réelle ? C'est la proposition de valeur de Streamlit et de cette formation: Pouvoir déployer votre projet Data sur le web afin que le monde entier puisse l'utiliser grâce à votre propre application web !Ainsi, l'ensemble de vos projets Data vont prendre vie ! Vous allez ainsi pouvoir : Partagez votre superbe classificateur d'images afin que d'autres personnes puissent utiliser votre modèle en y téléchargeant leurs propres images.Déployez en temps réel le score de sentiment des derniers tweets d'Elon Musk avec du NLP.Ou encore réaliser des dashboards interactifs à destination de vos équipes en entreprise avec un système d'authentification pour restreindre l'accès à seulement quelques personnes.J'ai développé ce cours après que des dizaines de personnes m'aient contacté pour me demander comment j'avais fait pour développer une application web de réservation de trains en temps réel, utilisée par plus de 10 000 personnes. Car oui on peut utiliser streamlit pour tous types d'applications et non seulement des applications data / IA !Bref, des centaines de cas d'usage sont possibles avec streamlit !Ce qui est formidable dans tout ça, c'est qu'il suffit uniquement d'avoir des connaissances en Python.Et qu'aucune compétence en Développement web, en Data Engineering ou même en cloud n'est nécessaire.Ce cours est scindé en 2 parties : Une partie exercice où nous verrons l'ensemble des fondamentaux de Streamlit, depuis la connection à un système de base de donnée, en passant par la création de l'interface puis finalement la partie sur le déploiement dans le cloud !Une seconde partie destinée au projet de formation : Développement et mise en production d'une application de tracking et d'analyse des actions du S&P5O0 avec notamment la visualisation de l'évolution du cours des actions et le calcul d'indicateurs de performances. Les données seront requêtées via une API.Faites passer vos projets data à l'étape supérieure avec Streamlit !Bonne formation :) Who this course is for Des personnes s'intéressant à la Data et à Python mais qui sont frustrés de ne jamais pouvoir partager leurs modèles de Machine Learning autour d'eux ! Des Data Scientist en entreprise qui souhaitent partager leurs travaux de Machine Learning ou des dashboards en interne pour leurs collaborateurs. Une personne qui a une idée de projet d'application web et qui souhaite développer un MVP en quelques heures ! Tous bons Data Sientists ! Homepage https://www.udemy.com/course/streamlit-deployer-son-app-de-machine-learning-sur-le-web/ Screenshot Rapidgator https://rg.to/file/05c4fd25d34d31558952388d1adb14c8/fktew.Streamlit..Deployer.son.app.de.Machine.Learning.sur.le.web.part2.rar.html https://rg.to/file/56728b99d11983f74923ed3d0c6dfd5f/fktew.Streamlit..Deployer.son.app.de.Machine.Learning.sur.le.web.part1.rar.html Fikper Free Download https://fikper.com/N3Ko4FbJHc/fktew.Streamlit..Deployer.son.app.de.Machine.Learning.sur.le.web.part2.rar.html https://fikper.com/d2lF2Fw9Dl/fktew.Streamlit..Deployer.son.app.de.Machine.Learning.sur.le.web.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 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 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 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 Coursera - Mind and Machine Specialization Last updated 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 31 Lessons ( 17h 18m ) | Size: 4 GB What you'll learn Develop connections between our understanding of human minds and artificial minds in the field of cognitive science. Discover and synthesize research in various disciplines related to cognitive science (e.g. computer science, psychology, neuroscience). Connect cognitive science principles and ideas to your own understanding. This specialization examines the ways in which our current understanding of human thinking is both illuminated and challenged by the evolving techniques and ideas of artificial intelligence and computer science. Our collective understanding of "minds" - both biological and computational - has been revolutionized over the past half-century by themes originating in fields like cognitive psychology, machine learning, neuroscience, evolutionary psychology, and game theory, among others. This specialization focuses on both the larger "historical" arc of these changes as well as current research directions and controversies. Applied Learning Project By completing the Mind and Machine specialization, students will be able to: (1) demonstrate their understanding of topics and strategies through quizzes, and (2) discuss and debate different arguments and interpretations of philosophical issues in discussions and peer-reviewed activities. Homepage https://www.coursera.org/specializations/mind-machine Screenshot Rapidgator http://peeplink.in/68b280186cd9 Fikper Free Download https://fikper.com/REVKGY1J3J/psisk.Coursera..Mind.and.Machine.Specialization.part5.rar.html https://fikper.com/VESDhH0NPC/psisk.Coursera..Mind.and.Machine.Specialization.part2.rar.html https://fikper.com/ZguGI6tdBt/psisk.Coursera..Mind.and.Machine.Specialization.part1.rar.html https://fikper.com/gYrpBOqA9y/psisk.Coursera..Mind.and.Machine.Specialization.part4.rar.html https://fikper.com/usaCHIlwAO/psisk.Coursera..Mind.and.Machine.Specialization.part3.rar.html No Password - Links are Interchangeable

Free Download Astronomy Image Colorization using Machine Learning (GANs) Published 10/2024 Created by Spartificial Innovations MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 101 Lectures ( 13h 45m ) | Size: 7.31 GB Colorize Black & White Astronomical Images Using Python, PyTorch, and FastAPI What you'll learn Discover the fundamentals of Generative Adversarial Networks (GANs) and understand their architecture, loss functions, and optimization challenges. Generate galaxies using GANs by setting up and training a model from scratch with hands-on coding in Kaggle Notebooks. Dive deeper into Wasserstein GAN with Gradient Penalty (WGAN-GP), learning about the algorithm and its implementation for more stable training. Implement WGAN-GP to generate realistic galaxy images and compare generated images with real astronomical data. Master Image-to-Image Translation GANs (Pix2Pix) and explore how they can be used for transforming images in the context of astronomy. Colorize black-and-white astronomical images using UNET architecture, PyTorch, and advanced GAN models to recreate realistic, vivid space images. Get introduced to FastAPI and Streamlit, learn to build APIs and create a frontend for your machine learning models. Create and deploy your own Image Colorization App using FastAPI, bringing all your learning together in a real-world project. Requirements Basic knowledge of Python programming. Familiarity with machine learning concepts is recommended, but not mandatory. Enthusiasm to learn GANs, WGANs, and image processing techniques! Description Are you fascinated by the beauty of the universe but curious about how machine learning can be used to bring astronomical images to life? Welcome to Astronomy Image Colorization using Machine Learning (GANs), where you will dive deep into the world of Generative Adversarial Networks (GANs) and their applications in astronomical image processing.In this course, you will learn how to leverage machine learning techniques to generate galaxies and colorize black-and-white images from space. You will gain practical knowledge by building end-to-end projects, from understanding GANs to creating your own image colorization app using FastAPI and Streamlit.What You'll Learn:Module 1: Discover the fundamentals of Generative Adversarial Networks (GANs) and understand their architecture, loss functions, and optimization challenges.Module 2: Generate galaxies using GANs by setting up and training a model from scratch with hands-on coding in Kaggle Notebooks.Module 3: Dive deeper into Wasserstein GAN with Gradient Penalty (WGAN-GP), learning about the algorithm and its implementation for more stable training.Module 4: Implement WGAN-GP to generate realistic galaxy images and compare generated images with real astronomical data.Module 5: Master Image-to-Image Translation GANs (Pix2Pix) and explore how they can be used for transforming images in the context of astronomy.Module 6: Colorize black-and-white astronomical images using UNET architecture, PyTorch, and advanced GAN models to recreate realistic, vivid space images.Module 7: Get introduced to FastAPI and Streamlit, learn to build APIs and create a frontend for your machine learning models.Module 8: Create and deploy your own Image Colorization App using FastAPI, bringing all your learning together in a real-world project.Course Highlights:Real-world Astronomy Applications: Work with real astronomical data to train your models.Project-Based Learning: Build multiple projects, including a Galaxy Generation project and a colorization web app.Hands-on with GANs: Deep dive into the technical details of GANs, WGANs, and Pix2Pix with step-by-step coding exercises.PyTorch & FastAPI: Learn how to use PyTorch for model building and FastAPI to deploy your models in production.Who This Course is For:Data science enthusiasts interested in Generative Adversarial Networks (GANs).Machine learning engineers looking to enhance their skills in computer vision and image generation.Astronomy buffs who want to apply machine learning to space image processing.Developers interested in building real-world ML apps using FastAPI and Streamlit.Requirements:Basic knowledge of Python programming.Familiarity with machine learning concepts is recommended, but not mandatory.Enthusiasm to learn GANs, WGANs, and image processing techniques!FAQs Section:What tools and libraries will we use in this course?You'll use Python libraries like PyTorch for model building, FastAPI for backend development, and Streamlit for frontend interfaces. We'll also leverage Kaggle Notebooks for coding exercises.Do I need prior experience with GANs?No prior experience with GANs is necessary, but basic Python programming knowledge and a basic understanding of machine learning would be beneficial. Who this course is for Data science enthusiasts interested in Generative Adversarial Networks (GANs). Machine learning engineers looking to enhance their skills in computer vision and image generation. Astronomy buffs who want to apply machine learning to space image processing. Developers interested in building real-world ML apps using FastAPI and Streamlit. 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Free Download Ai Forex Trading Harnessing Machine Learning And Ai For Cur Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 684.71 MB | Duration: 0h 37m Master the Art of Trading with AI and Machine Learning Techniques What you'll learn Create and optimize AI trading bots to automate trading processes. Implement machine learning techniques to develop trading strategies. Analyze currency correlations and their implications for trading. Understand and apply basic concepts of Forex trading. Evaluate the effectiveness of their trading strategies using data analysis. Requirements No programming experience is needed for AI bot. Everything is in the course. Description Master the Art of Trading with AI and Machine Learning Techniques:Unlock the power of Artificial Intelligence in the world of Forex trading! This course is designed for traders of all levels, from beginners to experienced, who want to leverage AI and Machine Learning to enhance their trading strategies. You will learn how to analyze currency correlations, implement machine learning models, and utilize AI trading bots effectively. You will gain insights into key concepts like data pre-processing, feature selection, and algorithm selection tailored for forex trading. By the end of this course, you'll not only understand the principles of AI and machine learning but also be equipped to make informed trading decisions that can enhance your trading performance.Course Objectives:By the end of this course, parti[beeep]nts will be able to:- Understand and apply basic concepts of Forex trading.- Analyze currency correlations and their implications for trading.- Implement machine learning techniques to develop trading strategies.- Create and optimize AI trading bots to automate trading processes.- Evaluate the effectiveness of their trading strategies using data analysis.This course will guide you through the fundamentals of machine learning and its application in the currency markets. You'll learn how to develop your own AI trading bot, analyze market trends, and implement effective trading strategies using real-world data.What You Will Learn:- Analyze currency correlations to make informed trading decisions.- Utilize machine learning to enhance your trading strategies.- Create and optimize AI bots for automated trading.- Implement best practices for risk management in Forex trading.DisclaimerInformation contained in this video is for informational and educational purposes and should not be construed as investment, tax, legal or financial advise. Opinions expressed herein are not investment recommendations and are not meant to be relied upon in making investment decisions. this course is not acting in the capacity of a Licence or a Registered Financial Advisor. Information and opinions presented are not an investment research report and may only address certain aspects of the companies mentioned and should not be taken as a substitute for comprehensive investment analysis any analysis presented herein is limited in scope, and illustrative for educational purposes only and based on an incomplete set of information and has limitations to its accuracy the information provided is based upon material that was obtained from sources believed to be reliable but has not been independently verified. therefore, I cannot guarantee its accuracy. Any opinions or estimates constitute best judgement based upon the information available at the time of publication and are subjected to change without notice.Futures forex trading risk disclaimer:Trading futures and foreign exchange on margin carries a high level of risk and potential for losses greater than your initial capital invested. Please insure to carefully consider whether these types of investments are suitable for you based on your financial situation, investment objective, and level of knowledge/experience. Since the possibility of financial losses exist you should not invest money that you cannot afford to lose. Any opinions, price, analysis, or other information provided in this video is for educational purposes only and is not financial advise. Please consult the Commodity Future trading commission for more information on the agency and two view carious rules and regulation as set forth in the Commodity Exchange Act Dodd-Frank Act and CFTC Regulations found at Title 17 chapter I of the code of Federal Regulation (CFR).Government Required Risk Disclaimer and Discloser Statement:CFTC RULE 4.41 - HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT BEEN EXECUTED, THE RESULTS MAY HAVE UNDER-OR-OVER COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ADY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFIT OR LOSSES SIMILAR TO THOSE SHOWN. OUR COURSE(S), PRODUCTS AND SERVICES SHOULD BE USED AS LEARNING AIDS ONLY AND SHOULD NOT BE USED TO INVEST REAL MONEY. IF YOU DECIDE TO INVEST REAL MONEY, ALL TRADING DECISIONS SHOULD BE YOUR OWN.Trading performance displayed herein is hypothetical. Hypothetical performance results have many inherit limitation some of which are described below. No representation is been made that any account will or is likely to achieve profit or losses similar to those shown. Infact, there are frequently sharp differences between hypothetical performances results and the actual results subsequently achieved by an particular trading program. One of the limitations of hypothetical performance trading results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk and no hypothetical trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses are material points which can also adversely affect actual trading results there are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results and all of which adversely affect actual trading results.U.S. Government Required Disclaimer-Commodity Futures Trading Commission Futures and Options trading has large potential rewards, but also large potential risk. You must be aware of the risks and be willing to accept them in order to invest in the futures and options markets. Don't trade with money you can't afford to lose. This is neither a solicitation nor an offer to Buy/Sell futures or options. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed on this web site. The past performance of any trading system or methodology is not necessarily indicative of future results.Trade at your own risk the information provided here is of the nature of a general comment only and neither supports nor in the intends to be, specific trading advice. It has been prepared without regard to any particular person's investment objectives, financial situation and particular needs. Information should not be considered as an offer or enticement to buy, sell or trade.You should see appropriate advise from your broker, or licenced investment advisor, before taking any action. Past performance does not guarantee future results. Stimulated performance results contain inherit limitations. Unlike actual performance records the result may under or overcompensate for such factors such as lack of liquidity. No representation is being made that any account will or is likely to achieve profits or losses to those show.The risk of loss in trading can be substantial you should therefor carefully consider whether such trading is suitable for you and light of your financial condition.If you purchase or sell Equity, Futures, Currencies or Options you may sustain in a total loss of the initial margin funds and any additional funds that you deposit with you broker to establish or maintain your positions. If the market moves against your position, you may be called upon by your broker to deposit a substantial amount or additional margin funds, on short notice is in order to maintain your position. If you do not provide the required funds within the prescribe time your position may be liquidated at a loss, and you maybe liable for any resulting deflect in your accountUnder certain market conditions, you may find it difficult or impossible to liquidate a position. This can occur, for example when the market makes a "limit move". The placement of constituent orders by you such as a "stop loss" or "stop limit" order, will not necessarily limit your losses to the intend amounts, since market conditions may make it impossible to execute such orders. Overview Section 1: Introduction Introduction to Forex Trading Lecture 1 Importance of AI in Trading. Lecture 2 What is Forex Market & Types of Market Lecture 3 Important Currency Pairs & Brokers Lecture 4 Important Trading Session & Key concepts and Terminology Lecture 5 Important Websites Overview Section 2: Using Advance Concepts & Websites Lecture 6 Important Currency Pairs & Indices (TradingView) Lecture 7 Economic News & Influence on Market (Forex Factory) Lecture 8 Using Treasury Bonds & Yield (BarChart) Lecture 9 Treasury Bond, Treasury Yield & Dollar Relationship Lecture 10 Working of Currency Correlation Analyser Section 3: Currency Correlation Analyzer Lecture 11 Automated Currency Correlation Analyser Lecture 12 Method 1 Lecture 13 Method 2 Section 4: Machine Learning Lecture 14 Learn How to setup ML trades Section 5: Ai Integration Lecture 15 AI Bot Trading Beginner to intermediate Forex traders.,Individuals interested in Machine Learning and AI applications in Finance.,Anyone looking to Automate their Trading Strategies.,Beginners wondering how the markets are Interconnected Screenshot Homepage https://www.udemy.com/course/ai-forex-trading-harnessing-machine-learning-and-ai-for-cur/ Rapidgator https://rg.to/file/b8b44eb67fd6b5a3fcbbf290ade5df20/yepsx.Ai.Forex.Trading.Harnessing.Machine.Learning.And.Ai.For.Cur.rar.html Fikper Free Download https://fikper.com/DBVIt1JarT/yepsx.Ai.Forex.Trading.Harnessing.Machine.Learning.And.Ai.For.Cur.rar.html No Password - Links are Interchangeable

Free Download Adversarial Machine Learning With Csv And Image Data Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 682.80 MB | Duration: 1h 39m Mastering Adversarial Machine Learning: Insights into Attack Techniques, Defense Strategies, and Ethical Considerations What you'll learn Explain foundational adversarial ML concepts, including AI security challenges and historical evolution. Analyze different adversarial attack types and assess their impact on machine learning models. Develop and apply defensive techniques for CSV and image-based ML models to mitigate risks. Use generative adversarial networks (GANs) to craft adversarial examples and test model robustness. Explore ethical considerations in adversarial ML. Investigate emerging trends in adversarial machine learning, including quantum computing, edge computing, zero-shot learning, and reinforcement learning Requirements Basic understanding of machine learning concepts Proficiency in Python programming Experience with data handling (including CSV and image formats) Familiarity with cybersecurity principles Description This comprehensive course on Adversarial Machine Learning (AML) offers a deep dive into the complex world of AI security, teaching you the sophisticated techniques used for both attacking and defending machine learning models. Throughout this course, you will explore the critical aspects of adversarial attacks, including their types, evolution, and the methodologies used to craft them, with a special focus on CSV and image data.Starting with an introduction to the fundamental challenges in AI security, the course guides you through the various phases of setting up a robust adversarial testing environment. You will gain hands-on experience in simulating adversarial attacks on models trained with different data types and learn how to implement effective defenses to protect these models.The curriculum includes detailed practical sessions where you will craft evasion attacks, analyze the impact of these attacks on model performance, and apply cutting-edge defense mechanisms. The course also covers advanced topics such as the transferability of adversarial examples and the use of Generative Adversarial Networks (GANs) in AML practices.By the end of this course, you will not only understand the technical aspects of AML but also appreciate the ethical considerations in deploying these strategies. This course is ideal for cybersecurity professionals, data scientists, AI researchers, and anyone interested in enhancing the security and integrity of machine learning systems. Overview Section 1: Introduction to Adversarial Machine Learning Lecture 1 Overview of AI Security Challenges Lecture 2 Evolution and Impact of Adversarial Attacks Lecture 3 Setting Up the Environment for AML Practices Section 2: The Nature of Adversarial Attacks Lecture 4 Types and Techniques of Adversarial Attacks Lecture 5 Practical: Crafting Evasion Attacks on CSV File-Trained Models Lecture 6 Practical: Simulating Basic Adversarial Attacks on Image Models Section 3: Developing Defense Mechanisms Lecture 7 Overview of Defense Strategies against Adversarial Threats Lecture 8 Practical: Implementing Defenses for CSV File-Trained Models Lecture 9 Practical: Applying Defense Techniques to Image-Trained Models Section 4: Advanced Adversarial Techniques Lecture 10 Transferability of Adversarial Examples Lecture 11 Generative Adversarial Networks (GANs) in AML Lecture 12 Practical: Creating and Defending Against Transferable Adversarial Examples Lecture 13 Practical: GAN Code for Adversarial Example Generation Section 5: Case Studies and Ethical Considerations Lecture 14 Analyzing Real-World Adversarial Attacks in Different Industries Lecture 15 Ethical Considerations in the Deployment of AML Strategies Lecture 16 Practical: Analyzing a Real-World Case and Proposing a Defense Strategy Section 6: Emerging Trends and Future Directions in Adversarial Machine Learning Lecture 17 Adversarial Machine Learning in Quantum Computing Lecture 18 AI Robustness in Edge Computing and Resource-Constrained Environments Lecture 19 Adversarial Attacks and Defense in Zero-Shot Learning Lecture 20 Adversarial Attacks and Defense in Reinforcement Learning This Adversarial Machine Learning course is ideal for AI professionals, cybersecurity experts, data scientists, graduate/post graduate/doctoral/post-doctoral students in related fields, and tech enthusiasts with a foundation in machine learning and programming, who are interested in exploring the security challenges of AI systems. 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Free Download Mastering AI From Machine Learning to Automation Published 10/2024 Created by Ndiaga Fall MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 53 Lectures ( 6h 37m ) | Size: 4.5 GB AI, Machine Learning, Reinforcement Learning, Robotics, Automation What you'll learn: Automation Artificial intelligence Machine Learning Field Knowlegde Robotics Reinforcement Learning Requirements: No prior experience in Artificial Intelligence is required Description: Unlock the power of Artificial Intelligence (AI) and gain hands-on experience with cutting-edge technologies like Machine Learning, Reinforcement Learning, Robotics, Automation, and Natural Language Processing (NLP). This course is designed to take you from AI fundamentals to advanced applications, empowering you to build intelligent systems and solve real-world challenges.What You Will Learn:Artificial Intelligence Foundations: Understand the core principles of AI and how it's revolutionizing industries like healthcare, finance, and robotics.Machine Learning (ML): Dive into supervised and unsupervised learning, build predictive models, and master techniques like decision trees, neural networks, and deep learning.Reinforcement Learning (RL): Explore how intelligent agents make decisions through trial and error in dynamic environments, using algorithms that optimize long-term success.Robotics & Automation: Discover how AI-driven robots are transforming automation processes and how machine learning is enabling robots to interact with their surroundings in real time.Natural Language Processing (NLP): Learn to build models that understand, generate, and respond to human language, using techniques such as sentiment analysis, text generation, and speech recognition.Real-World Applications: Implement AI solutions for automation, decision-making, and optimization tasks in various industries, and explore the future potential of AI in robotics and beyond.Why Take This Course? Whether you're an aspiring data scientist, an AI enthusiast, or a professional looking to automate processes, this course offers a complete roadmap to mastering AI. By the end of the course, you will have the skills to build intelligent systems that can learn, adapt, and interact with the world around them.Join us today and step into the future of AI and robotics! Who this course is for: Beginners curious about Artificial Intelligence Homepage https://www.udemy.com/course/mastering-ai-from-machine-learning-to-automation/ Rapidgator https://rg.to/file/460a6233b268963abd3bd4a9fe60e766/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part1.rar.html https://rg.to/file/9463bab2d2169ebe876e3e0945e26942/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part2.rar.html https://rg.to/file/9c1df9307a52b94723c3e2d4d9931b76/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part3.rar.html https://rg.to/file/9f0b52b9071fcccc6f12ad9999a2ff5b/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part4.rar.html https://rg.to/file/af3cdb3c5c4b3a32b6e8a3f4c448eef5/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part5.rar.html Fikper Free Download https://fikper.com/JvlEWN1jIB/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part1.rar https://fikper.com/62BOmMn010/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part2.rar https://fikper.com/4oH3PqPVuv/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part3.rar https://fikper.com/Xtm7teNRik/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part4.rar https://fikper.com/v9QvlBDT49/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part5.rar No Password - Links are Interchangeable

Free Download MQL5 MACHINE LEARNING Linear Regression for Algo Trading Published 10/2024 Created by Latvian Trading Solutions MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 25 Lectures ( 3h 42m ) | Size: 2 GB A complete guide to developing linear regression based models for algorithmic trading in MQL5 What you'll learn: The concept of Linear Regression and its application in Algorithmic Trading How to Develop a Linear Regression model on a spread sheet How to code a Linear Regression model Indicator in MQL5 How to develop a Linear Regression Strategy and code an Expert advisor in MQL5 Requirements: Basics of MQL5 Description: Simple linear regression is a statistical method used to model the relationship between two variables: an independent variable (x) and a dependent variable (y). It assumes a linear relationship between the two variables and aims to find the best-fitting straight line that represents this relationship.The equation for a simple linear regression model is:y = ax + bWhere:y is the dependent variable (the variable we want to predict).x is the independent variable (the variable used to make predictions).a is the slope of the line, representing the rate of change of y with respect to x.b is the y-intercept, representing the value of y when x is zero.While simple linear regression is a statistical technique, it can also be considered as a machine learning algorithm. In machine learning, the goal is to build models that can learn from data and make predictions. Linear regression fits this framework because it learns the relationship between x and y from a given dataset and uses this learned relationship to make predictions for new data points. As neural networks learn the best non-linear relationships between data by finding the weights that best fit the data, linear regression aims to find the best values of a and b that best describe the linear relationship between variables.In this course, our aim is to build a linear regression model in mql5 that seeks to predict the closing prices of a currency pair given its specific bar index. We shall start by creating a linear regression model on a spread sheet to basically explain the calculations involved in creating a linear regression model. We shall then develop our linear regression model as an mql5 indicator by coding it using the mql5 programming language. After that, we shall develop our trading strategy as an mql5 expert advisor coded using the mql5 algorithmic trading language. We shall use the linear regression model we created as an indicator to analyze data and find patterns we can use to profit from the market. We shall base our trading logic on the fact that if price goes beyond one or two standard deviations from its predicted or expected price, it has to reverse and go back to its expected price. Hence our strategy will be a mean reversion type of strategy.For those that are still finding their way with MQL5, as long as you understand the basics of MQL5, this course is for you. We will patiently guide you through every step of the strategy development process and walk you through every line of code we shall craft. Hopefully, by the end of the course, you will have gained the necessary skills to code similar models and trading strategies and be able to appreciate how linear regression models can be an asset in developing your own trading ideas based on the ideas that shared in this course.So hit hard on that enroll button now and join me in this incredible journey of coding a linear regression model using the mql5 algorithmic trading language. Who this course is for: Anyone willing to learn about the applications of Linear Regression in Market analysis and timeseries forecasting Homepage https://www.udemy.com/course/mql5-machine-learning-linear-regression-for-algo-trading/ Rapidgator https://rg.to/file/630d36f1b4288747a471e52674d9d4dd/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part1.rar.html https://rg.to/file/7645de1e68ac2dcf4f2b70fa72700e70/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part2.rar.html https://rg.to/file/482c3aed89c0666e8b54135282d626d2/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part3.rar.html Fikper Free Download https://fikper.com/1HmmIr9mbW/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part1.rar https://fikper.com/R3AmHQhj0Z/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part2.rar https://fikper.com/4wDuaEE6tI/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part3.rar No Password - Links are Interchangeable

Free Download Solana Candy Machine Deployment & Minting Dapp Integration Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 793.08 MB | Duration: 0h 46m Comprehensive Guide to Launching NFTs and Building a Minting DApp on Solana What you'll learn Generate NFTs with image layers and generate metadata files for the Solana blockchain Create Config file for the Candy machine Create a Candy machine with Sugar and upload the NFTs Add Candy guards to the Candy machine and view the details Interact with Candy machine and fetch data Create a Solana NFT minting dapp and perform minting Generate Solana key pair and request airdrops Switch between Devnet and Mainnet Requirements You will need node version 16.16 or higher version to be installed to your environment (I used v16.16) I recommend to use Visual studio code as the editor Sugar v2.7.1 Use Solana CLI latest version A little knowledge in react js is good to have Description This guide provides a comprehensive, step-by-step approach to generating NFTs, deploying a Solana Candy Machine, and integrating it into a minting dApp. It's designed to help developers launch a full NFT project on the Solana blockchain, from asset creation to user-friendly minting.In the first part of the guide, you'll focus on generating NFTs and creating their metadata. You'll learn how to produce unique NFT images and associate them with metadata files, ensuring compatibility with the Solana NFT standard. By the end of this section, you'll have a complete collection of images and metadata ready for blockchain deployment.The second part covers the deployment of the Solana Candy Machine. You'll be guided through the process of uploading your NFT assets to the blockchain, configuring important parameters such as pricing and minting. This section also explains how to secure your Candy Machine by adding Candy Guards.In the final part of the guide, you'll build and integrate a minting dApp. You'll learn to handle user transactions, and provide real-time minting updates within a user-friendly interface. By the end of the guide, you'll have a fully functional minting dApp, allowing users to easily mint NFTs from your collection.This guide equips you with the knowledge to launch a secure, scalable NFT project on Solana, from asset generation to a live minting dApp. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Prerequisites Section 2: Generate Solana NFT images + Metadata Lecture 3 Generate Solana NFT images + Metadata Section 3: Create and Deploy Candy Machine Lecture 4 Create and Deploy Candy Machine Section 4: Create Minting Dapp Lecture 5 Fetch Data from Candy Machine Lecture 6 Code Mint Function Lecture 7 Display Minted NFT Section 5: Switching Devnet to Mainnet Lecture 8 Switching Devnet to Mainnet Lecture 9 Switching Devnet to Mainnet in Phantom Wallet Beginner Solana Developers curios about NFT minting Homepage https://www.udemy.com/course/solana-candy-machine-deployment-minting-dapp-integration/ Rapidgator https://rg.to/file/465a941086caf8a25dd46926750c6476/qwujj.Solana.Candy.Machine.Deployment..Minting.Dapp.Integration.rar.html Fikper Free Download https://fikper.com/j8Lzl3FM7k/qwujj.Solana.Candy.Machine.Deployment..Minting.Dapp.Integration.rar.html No Password - Links are Interchangeable

Free Download Mastering Machine Learning From Basics To Breakthroughs Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 918.11 MB | Duration: 3h 38m Machine Learning, Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Markov Models What you'll learn Explore the fundamental mathematical concepts of machine learning algorithms Apply linear machine learning models to perform regression and classification Utilize mixture models to group similar data items Develop machine learning models for time-series data prediction Design ensemble learning models using various machine learning algorithms Requirements Foundations of Mathematics and Algorithms Description This Machine Learning course offers a comprehensive introduction to the core concepts, algorithms, and techniques that form the foundation of modern machine learning. Designed to focus on theory rather than hands-on coding, the course covers essential topics such as supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction. Learners will explore how these algorithms work and gain a deep understanding of their applications across various domains.The course emphasizes theoretical knowledge, providing a solid grounding in critical concepts such as model evaluation, bias-variance trade-offs, overfitting, underfitting, and regularization. Additionally, it covers essential mathematical foundations like linear algebra, probability, statistics, and optimization techniques, ensuring learners are equipped to grasp the inner workings of machine learning models.Ideal for students, professionals, and enthusiasts with a basic understanding of mathematics and programming, this course is tailored for those looking to develop a strong conceptual understanding of machine learning without engaging in hands-on implementation. It serves as an excellent foundation for future learning and practical applications, enabling learners to assess model performance, interpret results, and understand the theoretical basis of machine learning solutions.By the end of the course, parti[beeep]nts will be well-prepared to dive deeper into machine learning or apply their knowledge in data-driven fields, without requiring programming or software usage. Overview Section 1: Introduction Lecture 1 Introduction to Machine Learning Lecture 2 Types of Machine Learning Lecture 3 Polynomial Curve Fitting Lecture 4 Probability Lecture 5 Total Probability, Bayes Rule and Conditional Independence Lecture 6 Random Variables and Probability Distribution Lecture 7 Expectation, Variance, Covariance and Quantiles Section 2: Linear Models for Regression Lecture 8 Maximum Likelihood Estimation Lecture 9 Least Squares Method Lecture 10 Robust Regression Lecture 11 Ridge Regression Lecture 12 Bayesian Linear Regression Lecture 13 Linear models for classification::Discriminant Functions Lecture 14 Probabilistic Discriminative and Generative Models Lecture 15 Logistic Regression Lecture 16 Bayesian Logistic Regression Lecture 17 Kernel Functions Lecture 18 Kernel Trick Lecture 19 Support Vector Machine Section 3: Mixture Models and EM Lecture 20 K-means clustering Lecture 21 Mixtures of Gaussians Lecture 22 EM for Gaussian Mixture Models Lecture 23 PCA, Choosing the number of latent dimensions Lecture 24 Hierarchial clustering Students, data scientists and engineers seeking to solve data-driven problems through predictive modeling Homepage https://www.udemy.com/course/mastering-machine-learning-from-basics-to-breakthroughs/ Rapidgator https://rg.to/file/7a1c1299ff4b931eddf50e6b453b5dbb/ddbol.Mastering.Machine.Learning.From.Basics.To.Breakthroughs.rar.html Fikper Free Download https://fikper.com/46pB4R63M3/ddbol.Mastering.Machine.Learning.From.Basics.To.Breakthroughs.rar.html No Password - Links are Interchangeable

Free Download Introduction to AI and Machine Learning on Google Cloud Duration: 2h 50m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 378 MB Genre: eLearning | Language: English This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises. Homepage https://www.pluralsight.com/courses/introduction-ai-machine-learning-google-cloud TakeFile https://takefile.link/mt7ylysqlg62/clutv.Introduction.to.AI.and.Machine.Learning.on.Google.Cloud.rar.html Rapidgator https://rg.to/file/3bb6644cc7ee9b81d506bb54bd54f855/clutv.Introduction.to.AI.and.Machine.Learning.on.Google.Cloud.rar.html Fikper Free Download https://fikper.com/L2Wl0zcgJY/clutv.Introduction.to.AI.and.Machine.Learning.on.Google.Cloud.rar.html No Password - Links are Interchangeable

Free Download Math 0-1 Probability for Data Science & Machine Learning Published 9/2024 Created by Lazy Programmer Team,Lazy Programmer Inc. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 94 Lectures ( 17h 30m ) | Size: 7.62 GB A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers What you'll learn: Conditional probability, Independence, and Bayes' Rule Use of Venn diagrams and probability trees to visualize probability problems Discrete random variables and distributions: Bernoulli, categorical, binomial, geometric, Poisson Continuous random variables and distributions: uniform, exponential, normal (Gaussian), Laplace, Gamma, Beta Cumulative distribution functions (CDFs), probability mass functions (PMFs), probability density functions (PDFs) Joint, marginal, and conditional distributions Multivariate distributions, random vectors Functions of random variables, sums of random variables, convolution Expected values, expectation, mean, and variance Skewness, kurtosis, and moments Covariance and correlation, covariance matrix, correlation matrix Moment generating functions (MGF) and characteristic functions Key inequalities like Markov, Chebyshev, Cauchy-Schwartz, Jensen Convergence in probability, convergence in distribution, almost sure convergence Law of large numbers and the Central Limit Theorem (CLT) Applications of probability in machine learning, data science, and reinforcement learning Requirements: College / University-level Calculus (for most parts of the course) College / University-level Linear Algebra (for some parts of the course) Description: Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LLMs like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Prin[beeep]l Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. 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