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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
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
1.2 Activation Functions
Probabilistic Models
Unsupervised Learning
1. Introduction
2. Clustering
2.1 Clustering Overview
2.2 KMeans
2.3 Hirarchical Clustering
2.4 DBSCAN
2.5 GMM
2.6 Spectral Clustering
3. Dimenstionality Reduction
3.1 DR Overview
4. Anomaly Detection
4.1 AD Overview
5. Association Rule Learning
5.1 ARM Overview
6. Test Page
Self Supervised Learning
Further Reading
1. ML Courses
2. Math For ML
3. Books
4. Work Platforms
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4. Work Platforms
https://deepnote.com/pricing
Free Tier
Up to 3 editors
Up to 5 projects
AI-powered code completion
Unlimited Basic machines with 5 GB RAM, 2 vCPU
7 day revision history
