Study Schedule Summary (Guide)

Related:

• 20-ML-Core/Guide/Overview
• 20-ML-Core/Module 01 - Math Foundations + Classical ML
• 20-ML-Core/Module 02 - Deep Learning Foundations
• 20-ML-Core/Module 03 - CNNs, RNNs, Transformers

Week 1-2: Math + Classical ML

• Linear algebra, probability, calculus
• Linear/logistic regression, trees, SVM
• Implement from scratch

Week 3-4: Deep Learning Foundations

• Neural networks, backprop, optimization
• CNNs: architectures, implementations
• Implement NN from scratch

Week 5-6: Advanced Deep Learning

• RNNs, LSTMs, attention
• Transformers: architecture, variants
• Implement attention from scratch

Week 7-8: Specialization

• NLP: Embeddings, BERT, GPT
• RecSys: CF, matrix factorization, two-tower
• Pick one area to go deeper

Week 9+: Practice

• Mock interviews
• Explain concepts out loud
• Review weak areas

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This guide covers 95% of ML concepts asked in Meta/Google interviews. Master these and you'll be well-prepared!