Documentation v1.0
Machine Learning Algorithms
A practical, in-depth guide to machine learning algorithms — the intuition, math, and code behind regression, classification, clustering, and neural networks.
Supervised
Learn from labeled examples to predict outcomes.
Unsupervised
Discover hidden structure in unlabeled data.
Deep Learning
Neural networks that scale to complex tasks.
All Topics
01
Supervised Learning Basics
Getting Started
02
Linear Regression
Supervised Learning
03
Linear Regression — Interview Q&A
Supervised Learning
04
Logistic Regression
Supervised Learning
05
Logistic Regression — Interview Q&A
Supervised Learning
06
K-Nearest Neighbors
Supervised Learning
07
K-Nearest Neighbors — Interview Q&A
Supervised Learning
08
Decision Trees
Supervised Learning
09
Random Forest
Supervised Learning
10
Support Vector Machines
Supervised Learning
11
Naive Bayes
Supervised Learning
12
XGBoost, LightGBM, Tuning & Calibration
Gradient Boosting
13
K-Means Clustering
Unsupervised Learning
14
Gaussian Mixture Models
Unsupervised Learning
15
DBSCAN
Unsupervised Learning
16
Hierarchical Clustering
Unsupervised Learning
17
PCA
Unsupervised Learning
18
t-SNE & UMAP
Unsupervised Learning
19
Anomaly Detection
Anomaly Detection
20
Graph ML
Graph ML
21
Neural Networks
Deep Learning
22
Projects (Experienced)
Projects
23
End-to-End ML Project
Projects
24
Advanced Python
Python
25
Typing & Type Hints
Python
26
Object-Oriented Python
Python
27
SOLID Principles
Python
28
Design Patterns
Python