Introduction to Machine Learning

Learn machine learning from first principles by implementing core algorithms from scratch. You’ll understand training, evaluation, and optimization deeply—then build up to neural networks with clarity and rigor.

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Introduction to Machine Learning

Course content

12 sections | 48 lessons

Introduction

Supervised Learning: K-nearest neighbors (KNN)

Machine Learning Concepts

Supervised Learning: Linear regression

Supervised Learning: Multiple linear regression

Supervised Learning: Logistic regression

Supervised Learning: Support vector machines (SVMs)

Machine Learning concepts

Supervised Learning: Decision Trees

Supervised Learning: Neural Networks

Unsupervised Learning, Semi-Supervised Learning & Reinforcement Learning

Unsupervised Learning: K-means Clustering

Introduction to Machine Learning

Introduction1 Lessons

Introduction

Supervised Learning: K-nearest neighbors (KNN)3 Lessons

Supervised Learning: K-nearest neighbors (KNN)

Machine Learning Concepts8 Lessons

Machine Learning Concepts

Supervised Learning: Linear regression3 Lessons

Supervised Learning: Linear regression

Supervised Learning: Multiple linear regression2 Lessons

Supervised Learning: Multiple linear regression

Supervised Learning: Logistic regression4 Lessons

Supervised Learning: Logistic regression

Supervised Learning: Support vector machines (SVMs)2 Lessons

Supervised Learning: Support vector machines (SVMs)

Machine Learning concepts1 Lessons

Machine Learning concepts

Supervised Learning: Decision Trees5 Lessons

Supervised Learning: Decision Trees

Supervised Learning: Neural Networks15 Lessons

Supervised Learning: Neural Networks

Unsupervised Learning, Semi-Supervised Learning & Reinforcement Learning3 Lessons

Unsupervised Learning, Semi-Supervised Learning & Reinforcement Learning

Unsupervised Learning: K-means Clustering1 Lessons

Unsupervised Learning: K-means Clustering