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Machine Learning
39 topics across 6 chapters
Chapter 1
Foundations
1
Probability & Statistics Fundamentals
2
Linear Algebra Essentials
3
Optimization Methods
4
Bias-Variance Tradeoff
5
Data Preprocessing & Sampling
6
Probability Distributions
Chapter 2
Supervised Learning
7
Regression
8
Classification
9
Decision Trees & Random Forests
10
Support Vector Machines
11
Neural Networks (Intro)
12
Gradient Boosting Methods
Chapter 3
Unsupervised Learning
13
Clustering
14
Dimensionality Reduction
15
Anomaly Detection
16
Topic Modeling
17
Self-Supervised Learning
Chapter 4
Model Evaluation & Deployment
18
Train/Test Split & Cross-Validation
19
ML Metrics & Evaluation
20
Hyperparameter Tuning & Model Selection
21
Deployment Pipelines
22
Model Monitoring & Reproducibility
Chapter 5
ML Engineering
23
Data Pipelines for ML
24
Feature Stores & Feature Engineering
25
Model Serving & APIs (REST/GRPC)
26
Experiment Tracking & Reproducibility
27
ML Tools & Frameworks Comparison
28
Scaling ML Systems & Hardware
Chapter 6
Ethics & Responsible AI
29
Bias & Fairness in ML
30
Privacy & Security in AI
31
Interpretability & Transparency
32
AI Regulation & Compliance

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