Data

Clustering & Classification

Discover the fundamentals of machine learning — from nearest neighbors to decision trees. Learn to group data and make predictions with classification algorithms.

1

What is ML?

Introduction to machine learning

Lesson 1: Supervised vs Unsupervised Learning Coming Soon
Lesson 2: Training & Testing Data Coming Soon
Lesson 3: Model Evaluation Basics Coming Soon
2

K-Nearest Neighbors

Classify by proximity

Lesson 4: How KNN Works Coming Soon
Lesson 5: Choosing K Coming Soon
Lesson 6: Distance Metrics Coming Soon
3

Decision Trees

Classify by asking questions

Lesson 7: Tree Structure Coming Soon
Lesson 8: Splitting Criteria Coming Soon
Lesson 9: Pruning Coming Soon
4

K-Means Clustering

Grouping data into clusters

Lesson 10: How K-Means Works Coming Soon
Lesson 11: Choosing K with Elbow Method Coming Soon
Lesson 12: Limitations of K-Means Coming Soon
5

Hierarchical Clustering

Building a tree of clusters

Lesson 13: Agglomerative Clustering Coming Soon
Lesson 14: Dendrograms Coming Soon
Lesson 15: Linkage Methods Coming Soon
6

Naive Bayes

Probabilistic classification

Lesson 16: Bayes for Classification Coming Soon
Lesson 17: Naive Independence Assumption Coming Soon
Lesson 18: Text Classification Coming Soon
7

Evaluating Models

Measuring classification performance

Lesson 19: Accuracy & Error Rate Coming Soon
Lesson 20: Precision, Recall, F1 Coming Soon
Lesson 21: Confusion Matrices Coming Soon
8

Feature Selection

Choosing the right inputs

Lesson 22: Feature Importance Coming Soon
Lesson 23: Dimensionality Reduction Coming Soon
Lesson 24: PCA Basics Coming Soon
9

Real-World Applications

ML in practice

Lesson 25: Image Classification Coming Soon
Lesson 26: Recommendation Systems Coming Soon
Lesson 27: Fraud Detection Coming Soon