Data, Statistics & Classical ML Notes
Subject Overview
The second half of the Foundations year: take raw data, prepare it properly, and train traditional ML models that solve real problems. Six modules cover statistics for ML, data wrangling, supervised and unsupervised learning, and the workflow and experiment tracking that keep results reproducible. Students leave Year 1 able to clean data, train models, and reason about results.
Unit-wise Syllabus
8 units — click WhatsApp below to get the full notes for each
Unit 1: Statistics for ML (3 hours)
Descriptive versus inferential statistics, hypothesis testing, p-values, confidence intervals, common pitfalls
Unit 2: Data Wrangling (3 hours)
Cleaning, missing values, outliers, feature engineering, scaling, encoding categorical variables
Unit 3: Supervised Learning I (4 hours)
Linear regression, logistic regression, decision trees, train/test splits, cross-validation
Unit 4: Supervised Learning II (4 hours)
Random forests, gradient boosting (XGBoost, LightGBM), SVMs, model evaluation metrics
Unit 5: Unsupervised Learning (3 hours)
K-means, hierarchical clustering, PCA, t-SNE, UMAP — when and why to use each
Unit 6: ML Workflow & Experiment Tracking (3 hours)
scikit-learn pipelines, hyperparameter tuning, MLflow or Weights & Biases for tracking
Unit 7: Tools introduced
scikit-learn, XGBoost, LightGBM, MLflow / Weights & Biases for experiment tracking, Kaggle for datasets and competitions, Claude / ChatGPT as a debugging and code review partner
Unit 8: Mini-projects
Titanic survival prediction with a full ML pipeline, customer segmentation on real e-commerce data, house price predictor deployed as a simple Streamlit app