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Machine Learning
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Overview
Introduction
1.1 Basics
New
Data Basics
1.1 Fundamentals
Supervised Learning
1. Linear Regression
1.1 Fundamentals
1.2 Model Mechanics
1.3 Problems
1.4 Implementation
2. Logistic Regression
2.1 Fundamentals
2.2 Model Mechanics
3. Decision Trees
3.1 Fundamentals
3.2 Random Forest
3.3 Information Gain
3.4 Gini Index Splitting
Deep Learning
1. Neural Networks
1.1 Fundamentals
1.2 Activation Functions
1.3 MLP
1.4 Debugging
1. Debugging
2. WandB
2. CNN
2.1 CNN Basics
3. Transformers
3.1 Fundamentals
Probabilistic Models
Unsupervised Learning
1. Introduction
1. Fundamentals
1.1 Fundamentals
2. Clustering
2.1 Clustering Overview
2.2 KMeans
2.3 Hirarchical Clustering
2.4 DBSCAN
2.5 GMM
2.6 Spectral Clustering
2.7 Deep Type Clustering
3. Dimenstionality Reduction
3.1 DR Overview
3.2 Kernel PCA
4. Anomaly Detection
4.1 AD Overview
5. Association Rule Learning
5.1 ARM Overview
Self Supervised Learning
Further Reading
1. ML Courses
2. Math For ML
3. Books
4. Work Platforms
5. ML Playground
6. ML Misc (Web, Journals, Etc)
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3. Books
