AI

Machine Learning — ML with Python

Train models that learn from data using Python and scikit-learn — the core skill behind modern AI.

32 lessons7 chaptersStart: What is Machine Learning? →

1Foundations

What is Machine Learning?25 min Types of Machine Learning25 min The ML Workflow30 min Features (X) and Labels (y)30 min Train / Test Split35 min

2Supervised Learning

Linear Regression: Predict a Number40 min Logistic Regression: Predict a Category40 min K-Nearest Neighbours (KNN)35 min Decision Trees35 min Random Forest35 min Support Vector Machines (SVM)40 min Naive Bayes35 min Boosting & Gradient Boosting40 min

3Unsupervised Learning

K-Means Clustering40 min PCA: Dimensionality Reduction35 min

4Evaluate & Improve

Metrics: Accuracy, Precision, Recall40 min Overfitting vs Underfitting35 min Cross-Validation35 min Feature Scaling35 min How Models Learn: Cost Functions & Gradient Descent40 min Regression Metrics: MAE, MSE, RMSE & R²35 min Classification Depth: Confusion Matrix, F1 & ROC-AUC40 min Regularization & the Bias–Variance Tradeoff40 min

5Working with Real Data

Handling Missing Data (Imputation)35 min Encoding Categories & Feature Engineering40 min Pipelines: Chaining Steps Safely35 min Hyperparameter Tuning (GridSearchCV)40 min

6Going Deeper

Ensembles: Bagging, Boosting, Stacking & Voting35 min Imbalanced Data: When One Class Is Rare40 min A First Neural Network (Keras)45 min Saving & Deploying a Model40 min

7Project

Project: Build a Model End to End120 min