Machine Learning — Study Path Agent

Machine Learning

22 topics across 5 chapters

Chapter 1

Supervised Learning

1

Linear Regression

2

Classification Algorithms

3

Decision Trees & Random Forests

4

Neural Networks & Deep Learning Basics

Chapter 2

Unsupervised Learning

5

K-Means & Clustering

6

Dimensionality Reduction (PCA, t-SNE)

7

Association Rule Learning

8

Anomaly Detection

Chapter 3

Reinforcement Learning

9

Markov Decision Processes

10

Policy Gradient Methods

11

Q-Learning & Value-Based Methods

12

Deep Reinforcement Learning

Chapter 4

Model Evaluation & Validation

13

Train/Test Split & Validation Sets

14

Cross-Validation & Hyperparameter Tuning

15

Evaluation Metrics (Accuracy, Precision, Recall)

16

Bias, Variance & Model Robustness

Chapter 5

Ethics, Fairness & Responsible AI