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Machine Learning Tutorial

Understand every core ML algorithm with diagrams, hand-worked numbers and Pakistani business cases.

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1What is Machine Learning?

2Supervised, Unsupervised and Reinforcement Learning

3The Machine Learning Workflow (CRISP-DM)

4Data Preparation: Cleaning, Encoding, Scaling

5Feature Engineering

6Train/Test Split, Overfitting and Underfitting

7Linear Regression

8Logistic Regression and Classification

9Decision Trees and Random Forests

10k-Nearest Neighbours and Support Vector Machines

11Gradient Boosting (XGBoost, LightGBM)

12Clustering with k-Means and Hierarchical Methods

13Dimensionality Reduction: PCA

14Evaluating Models: Accuracy, Precision, Recall, F1, ROC, RMSE

15Cross-Validation and Hyperparameter Tuning

16Time-Series Forecasting Basics

17Deploying and Monitoring Models (MLOps basics)

18Case Studies and Practice: Churn, Fraud, Demand Forecasting