1: Introduction to AI, ML, and Data Science
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2: Types of Machine Learning
3: Basic Terminology - Features, Labels, Training, Testing
4: Introduction to Matrices and Linear Algebra
5: Review of Fundamental Concepts in Information Theory and Machine Learning
6: Review of Fundamental Concepts of Probability and Statistics for Machine Learning
7: Introduction to Statistical Validation and A/B Testing
8: Data Collection and Cleaning
9: Feature Scaling and Normalization
10: Sampling Strategies for Machine Learning
11: Feature Engineering in Machine Learning
12: Introduction to Genetic Algorithms
13: Linear Regression, Ridge Regression and Lasso regression
14: Evaluation Metrics
15: Decision Trees
16: Random Forests
17: Expectation-Maximization (EM) Algorithm
18: Mixture Models and Gaussian Mixture Models (GMMs) in Machine Learning
19: Clustering Algorithms: K-Means, Hierarchical
20: Principal Component Analysis (PCA)
21: Association Rules: Apriori, Eclat
22: Introduction to Association Rules