Machine Learning Mastery Roadmap
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1. Foundations (Math + Programming)
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Math Prerequisites:
- Linear Algebra: Vectors, matrices, SVD
- Calculus: Derivatives, gradients, chain rule
- Probability & Statistics: Bayes’ Theorem, distributions, hypothesis testing
- Optimization: Convex functions, gradient descent
Programming:
- Python: Numpy, Pandas, Matplotlib
- Tools: Jupyter, Git, virtual environments
2. Core Machine Learning Concepts
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Supervised Learning:
- Linear/logistic regression, decision trees, SVMs, boosting
Unsupervised Learning:
- Clustering (K-means, DBSCAN), PCA, t-SNE
Model Evaluation:
- Confusion matrix, precision, recall, F1, ROC-AUC, cross-validation
3. Deep Learning
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Fundamentals:
- Perceptrons, feedforward nets, backpropagation, activations
Architectures:
- CNNs, RNNs, LSTMs, Transformers
Frameworks:
- TensorFlow/Keras, PyTorch
4. Mathematical Deep Dives
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- Information theory, VC dimension, Rademacher complexity
- Generalization theory, manifold learning
5. Projects & Practice
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Mini-Projects:
- Implement ML models from scratch
- Use real datasets (Titanic, MNIST)
Real-World Projects:
- Full ML pipelines, API deployment, Docker containers
6. Advanced Topics & Research Areas
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- Reinforcement Learning: Q-learning, PPO
- Bayesian ML: MCMC, variational inference
- Graph ML: GNNs
- Causal Inference: Do-calculus, DAGs
7. Evaluation & Interview Prep
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- Feature engineering, system design, ML case studies
- Write ML algorithms from scratch
Milestone Guide:
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- Month 1–2: Python + Math + linear models
- Month 3–4: Tree models, SVMs, tuning
- Month 5–6: Deep learning basics, CNNs
- Month 7+: Specialization and real projects