01Lesson link
What Is Machine Learning?
Understand the basic idea of machine learning and how it differs from rule-based programming.
ML basicsUse casesLearning systems
Open Lesson 1 24-lesson learning path
Machine Learning, step by step.
Build understanding through short lessons, interactive labs, business examples, and knowledge checks. Move from core concepts to model evaluation, explainability, and a final capstone.
Course overview
24 lessons
Learning format
Interactive
Final activity
Business capstone
Lesson links below match the 24 lesson folder names in your project. Each lesson folder needs a working page.tsx file to display lesson content.
Curriculum
All 24 lessons
Choose a lesson or search for a concept. Lessons 21–24 include ensemble learning, imbalanced classification, explainability, and the final business capstone. All 24 lesson links use the folder names you provided.
02Lesson link
Types of Machine Learning
Compare supervised, unsupervised, and reinforcement learning.
SupervisedUnsupervisedReinforcement
Open Lesson 2 03Lesson link
Supervised Learning
Learn how labelled examples help models predict known outcomes.
FeaturesLabelsPrediction
Open Lesson 3 04Lesson link
Unsupervised Learning
Explore how models find structure in data without labelled outcomes.
PatternsClusteringStructure
Open Lesson 4 05Lesson link
Reinforcement Learning
Understand agents, actions, rewards, and learning through feedback.
AgentsRewardsPolicy
Open Lesson 5 06Lesson link
Model Evaluation
Measure model performance and choose metrics that fit the task.
MetricsValidationBaseline
Open Lesson 6 07Lesson link
Overfitting and Underfitting
Recognize when a model memorizes training data or fails to learn enough.
GeneralizationBiasVariance
Open Lesson 7 08Lesson link
Feature Engineering
Prepare useful input features to improve model learning.
EncodingScalingFeature selection
Open Lesson 8 09Lesson link
Neural Networks
Explore layers, weights, activations, and how neural networks learn.
LayersWeightsActivation
Open Lesson 9 10Lesson link
Deep Learning
Understand deep neural networks and where they are useful.
Deep networksTrainingApplications
Open Lesson 10 11Lesson link
Natural Language Processing
Learn how machine learning systems process and analyze language.
Text dataTokensLanguage models
Open Lesson 11 12Lesson link
Computer Vision
Explore how models extract information from images.
ImagesClassificationDetection
Open Lesson 12 13Lesson link
Generative AI
Understand models that generate text, images, and other content.
GenerationPromptsEvaluation
Open Lesson 13 14Lesson link
AI Agents
Learn how AI agents combine goals, tools, and multi-step actions.
GoalsToolsWorkflow
Open Lesson 14 15Lesson link
MLOps and Deployment
See how models are packaged, deployed, monitored, and maintained.
DeploymentMonitoringVersioning
Open Lesson 15 16Lesson link
Time Series Forecasting
Analyze time-ordered data and create forecasts.
TrendSeasonalityForecasting
Open Lesson 16 17Lesson link
Recommendation Systems
Understand how systems recommend products or content to users.
PreferencesSimilarityRanking
Open Lesson 17 18Lesson link
Anomaly Detection
Identify unusual records, events, or patterns in data.
OutliersAnomaliesThresholds
Open Lesson 18 19Lesson link
Clustering Algorithms
Group similar records without predefined labels.
K-meansDistanceClusters
Open Lesson 19 20Lesson link
Dimensionality Reduction
Reduce the number of features while preserving useful structure.
PCAFeature spaceVisualization
Open Lesson 20 21Lesson link
Ensemble Learning
Combine model predictions with bagging, boosting, and voting.
BaggingBoostingVoting
Open Lesson 21 22Lesson link
Imbalanced Classification
Handle uneven class distributions with appropriate metrics, weighting, and resampling.
Confusion matrixPR-AUCClass weights
Open Lesson 22 23Lesson link
Explainable AI and Model Interpretation
Inspect model behaviour using feature importance, SHAP, LIME, and responsible review.
Global vs localSHAPLIME
Open Lesson 23 24Lesson link
Final Capstone Project
Plan an end-to-end customer churn project from business question to responsible recommendation.
Data preparationModel evaluationBusiness action
Open Lesson 24