Udemy – Mastery in Advanced Machine Learning & Applied AI™ 2024-11

What you will learn

• Basic concepts of machine learning
• Reinforcement learning and its applications in decision making
• Supervised learning and its role in predictive modeling
• Techniques for effective training and evaluation of machine learning models
• Linear Regression and Its Application in Forecasting Tasks
• Evaluating the fit of machine learning models for better accuracy
• Application of supervised learning techniques in real-world data scenarios
• Multiple linear regression for modeling multiple variables
• Evaluating the performance of multiple linear regression models
• Practical applications of multiple linear regression in solving business problems
• Mastering logistic regression and using it in classification tasks
• Feature engineering techniques for improving logistic regression models.
• Using Logistic Regression for Classification and Prediction
• Understanding decision trees and using them in machine learning
• Evaluating the performance of decision trees for optimal predictions.
• Application of decision trees to real-world problems in various industries
• Mastering random forests and their benefits for prediction tasks
• Hyperparameter tuning techniques for optimizing machine learning models
• Combining decision trees and random forests for increased predictive power
• Mastering Support Vector Machines (SVM) for classification tasks
• Understanding Kernel Functions in SVM for Handling Nonlinear Data
• Real-world applications of support vector machines for classification problems.
• Implementing the K-Nearest Neighbor (KNN) algorithm for supervised learning
• Practical applications of the KNN algorithm for classification and prediction
• Understanding gradient boosting algorithms and their power in prediction tasks
• Mastering hyperparameter tuning to improve gradient boosting models
• Application of gradient boosting in various machine learning problems
• Mastering evaluation criteria for measuring the performance of machine learning models
• Understanding and using ROC and AUC curves to evaluate model performance
• An introduction to the concepts of unsupervised learning, with a focus on clustering and dimensionality reduction.
• Master anomaly detection techniques to identify outliers in data
• Advanced techniques in K-Means clustering for unsupervised learning tasks.
• Iterating the K-Means algorithm to improve clustering results
• Practical Applications of K-Means Clustering in Real-World Scenarios
• Mastery of hierarchical clustering techniques for data segmentation
• Visualize hierarchical clustering using dendrograms for clear insights
• Applying PCA to real-world problems to reduce data dimensionality
• Understanding Linear Discriminant Analysis (LDA) and its role in unsupervised learning
• Comparing PCA vs. LDA for Dimensionality Reduction Techniques
• Application of LDA for dimensionality reduction and classification in machine learning
• Mastering t-SNE for dimensionality reduction and advanced visualization
• Understand how t-SNE works and use it to visualize high-dimensional data.
• Understanding and applying dimensionality reduction evaluation criteria
• Hyperparameter tuning techniques for optimizing unsupervised learning models
• Using Bayesian Optimization to Improve the Performance of Unsupervised Models
• An introduction to extracting association rules to extract patterns from data
• Understanding trust and support in extracting association rules for actionable insights.
• Using the Apriori algorithm in extracting association rules for market portfolio analysis
• Step-by-step explanation and application of the Apriori algorithm in real-world analytics

This course is suitable for people who:

• Anyone who wants to learn the skills of the future and become a data scientist, senior data scientist, AI scientist, AI engineer, AI researcher, and AI specialist.

Installation Guide

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Quality: 720p