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Machine Learning 101

From linear regression to neural networks — build real ML intuition through interactive model building and visual explanations.

14 Modules 12 hands-on labs Beginner
Machine Learning Classification Regression Neural Networks Clustering
Start Module 1 → View All Modules
Outcomes

What You'll Learn

Supervised Learning
Build and train classification and regression models from scratch.
Unsupervised Learning
Apply clustering and dimensionality reduction to discover patterns.
Neural Networks
Understand perceptrons, backpropagation, and multi-layer architectures.
Model Evaluation
Use cross-validation, confusion matrices, and ROC curves to assess models.
Feature Engineering
Transform raw data into meaningful features for better predictions.
End-to-End ML Pipeline
Design complete ML workflows from data collection to deployment.
Curriculum

Course Modules

14 modules from linear regression to real-world ML applications.

1
What is Machine Learning?
4 lessons1 lab
Start Here
What is Machine Learning?Slides
Types of Machine LearningSlides
Your First ML Model: ConceptSlides
Evaluating ModelsSlides
2
Linear Models
3 lessons1 lab
Available
Linear RegressionSlides
Logistic RegressionSlides
RegularizationSlides
3
Tree-Based Methods
3 lessons1 lab
Available
Decision TreesSlides
Random ForestsSlides
Gradient BoostingSlides
4
Support Vector Machines
3 lessons1 lab
Available
Maximum Margin ClassifierSlides
The Kernel TrickSlides
SVM in PracticeSlides
5
Unsupervised Learning
3 lessons1 lab
Available
ClusteringSlides
Hierarchical & Density-Based ClusteringSlides
Dimensionality ReductionSlides
6
Neural Networks Foundations
4 lessons1 lab
Available
The PerceptronSlides
Multi-Layer PerceptronsSlides
BackpropagationSlides
Training Neural NetworksSlides
7
Convolutional Neural Networks (CNNs)
3 lessons1 lab
Available
Convolution for ImagesSlides
CNN ArchitectureSlides
CNN ApplicationsSlides
8
Recurrent Neural Networks (RNNs)
3 lessons1 lab
Available
Sequence ModelingSlides
LSTM and GRUSlides
Sequence ApplicationsSlides
9
Transformers & Attention
3 lessons1 lab
Available
The Attention MechanismSlides
The Transformer ArchitectureSlides
Modern Transformer ModelsSlides
10
Practical ML
3 lessons1 lab
Available
Feature EngineeringSlides
Model Selection & TuningSlides
ML PipelineSlides
11
Deep Learning in Practice
3 lessons1 lab
Available
Training at ScaleSlides
Transfer Learning & Fine-TuningSlides
Generative ModelsSlides
12
Capstone & What's Next
3 lessons1 project
Available
Responsible MLSlides
Emerging DirectionsSlides
Career and ResourcesSlides
13
Genetic Algorithms & Evolutionary Search
4 lessonsNo project yet
Available
Evolution as a Search StrategySlides
The Genetic Algorithm LoopSlides
Designing a Fitness FunctionSlides
When Evolution Wins — and When It LosesSlides
14
Reinforcement Learning
4 lessonsNo project yet
Available
Learning from RewardSlides
Markov Decision ProcessesSlides
Q-LearningSlides
Policy Methods and Where RL Is RealSlides
Before You Start

Prerequisites

Ready to start Machine Learning?

Begin with Module 1 and discover how machines learn patterns from data to make predictions.

Start Module 1 →
Labs & Demos 12 interactive demos — 12 interactive demos