Machine Learning Learning Roadmap

Why Machine Learning

Machine learning is the skill that turns data into decisions. Understanding ML fundamentals — not just API calls — lets you diagnose model failures, choose the right approach for a problem, and build systems that generalize rather than memorize.

Stage 1: Foundations (Weeks 1-2)

Goal: Understand what ML is and train your first models correctly.

Topics:

• ML Fundamentals — bias-variance tradeoff, overfitting, the ML workflow
• Supervised Learning — linear models, trees, ensembles, SVMs, KNN
• Cross-Validation — k-fold, stratified, time-series, nested CV

Milestone: Take any UCI dataset, split properly (no leakage), compare 5 algorithms with stratified k-fold CV, and report results with standard deviations — not just point estimates.

Stage 2: Feature Engineering and Ensembles (Weeks 3-4)

Goal: Extract signal from raw data and build strong models.

Topics:

• Feature Engineering Pipelines — custom transformers, target encoding, interaction features, leakage prevention
• Ensemble Methods — voting, stacking, blending, when ensembles help

Milestone: Take a raw dataset with dates, text, and categoricals. Build a feature engineering pipeline inside a sklearn Pipeline (no leakage), then build a stacking ensemble. Show that stacking beats the best individual model.

Stage 3: Specialized Scenarios (Weeks 4-5)

Goal: Handle the messy real-world ML problems.

Topics:

• Unsupervised Learning — PCA, UMAP, anomaly detection, NMF topic modeling
• Imbalanced Data — SMOTE, class weights, threshold tuning, PR-AUC

Milestone: Build a fraud detection model on a dataset with 0.1% positive rate. Show that accuracy is useless. Optimize for a specific business cost function (false negative costs 10× more than false positive). Justify your threshold choice.

Stage 4: Model Understanding (Week 5-6)

Goal: Explain model decisions and build trust.

Topics:

• Model Interpretability — SHAP, LIME, partial dependence plots

Milestone: Take a black-box GBM model and produce: global feature importance (SHAP), a local explanation for the top 3 misclassified examples, and a PDP showing how the top feature affects predictions.

Stage 5: Projects

Beginner: Heart disease classifier, bike sharing demand predictor, movie sentiment analysis
Intermediate: Credit scoring model, survival analysis, causal inference study
Advanced: ML interpretability toolkit, federated learning simulation, active learning system

See Machine Learning Projects for full descriptions.

What Comes After ML Fundamentals

• PyTorch / TensorFlow — for image, text, and sequence data
• XGBoost / LightGBM — for competitive tabular ML
• MLOps — for deploying and monitoring models in production
• Causal inference — for going beyond prediction to understanding

Resources

• “Hands-On Machine Learning” by Aurélien Géron (chapters 1-9)
• “The Elements of Statistical Learning” (ESL) — for the math
• fast.ai Practical ML course — code-first approach
• Kaggle competitions — best way to build intuition