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Machine Learning
45 topics across 7 chapters
Chapter 1
Supervised Learning
1
Linear Regression
2 subtopics
2
Ordinary Least Squares
3
Assumptions & Diagnostics
4
Classification
2 subtopics
5
Logistic Regression
6
Support Vector Machines
7
Regression
2 subtopics
8
Ridge & Lasso Regression
9
Bias-Variance Tradeoff
Chapter 2
Unsupervised Learning
10
Clustering
2 subtopics
11
K-Means
12
Hierarchical Clustering
13
Dimensionality Reduction
2 subtopics
14
PCA
15
t-SNE & UMAP
Chapter 3
Reinforcement Learning
16
Q-Learning
1 subtopics
17
Q-Learning Essentials
Chapter 4
Model Evaluation & Validation
18
Model Evaluation
2 subtopics
19
Holdout & Validation Set
20
Metrics: Accuracy, AUC, F1
21
Cross-Validation
2 subtopics
22
K-Fold Cross-Validation
23
Stratified CV
Chapter 5
Feature Engineering
24
Feature Scaling
2 subtopics
25
Standardization
26
Normalization
27
Feature Selection
2 subtopics
28
Feature Importance
29
Regularization Techniques
Chapter 6
Foundations: Math & Theory
30
Probability & Statistics
2 subtopics
31
Probability Basics
32
Bayes Theorem Applications
33
Optimization & Gradient Descent
2 subtopics
34
Gradient Descent Basics
35
Convex Optimization
Chapter 7
Deep Learning
36
Neural Networks Basics
37
Backpropagation & Training Tricks

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