AI/ML Learning Roadmap

Phase 1: Foundations (Weeks 1-4)

Programming Fundamentals

• Python basics: Variables, functions, classes, file I/O
• Data structures: Lists, dicts, sets, comprehensions
• Libraries: NumPy, Pandas basics

Math Essentials

• Linear algebra: Vectors, matrices, dot products
• Calculus: Derivatives, gradients, chain rule
• Statistics: Probability, distributions, Bayesian basics

Resources

Phase 2: Machine Learning (Weeks 5-10)

Core Concepts

• Supervised vs Unsupervised learning
• Train/validation/test splits, overfitting
• Common algorithms: Linear regression, Decision trees, SVM, Random forests
• Evaluation metrics: Accuracy, precision, recall, F1, AUC

Deep Learning Basics

• Neural network architecture
• Backpropagation and gradient descent
• CNNs for images, RNNs for sequences
• PyTorch or TensorFlow framework

Resources

Phase 3: LLMs and NLP (Weeks 11-16)

Transformer Architecture

• Attention mechanism (self-attention, multi-head)
• Encoder-decoder architecture
• Positional encoding

LLM Fundamentals

• Pretraining objectives (next token prediction)
• Tokenization (BPE, SentencePiece)
• Fine-tuning (SFT, RLHF, DPO)
• Inference and serving

Hands-On Projects 1. Fine-tune LLM with LoRA 2. Build RAG application 3. Deploy model with vLLM

Resources

Phase 4: Production ML (Weeks 17-24)

MLOps

• Experiment tracking (W&B, MLflow)
• Model versioning
• CI/CD for ML

Deployment

• Model serving (vLLM, TGI, Triton)
• Containerization (Docker, K8s)
• Monitoring and observability

Scaling

• Distributed training
• GPU optimization
• Cost management

Learning Resources Summary

Courses

• fast.ai: Practical deep learning
• Coursera ML Specialization: Fundamentals
• DeepLearning.AI: Specializations

Books

• "Deep Learning" by Goodfellow et al.
• "Hands-On Machine Learning" by Géron
• "Designing Machine Learning Systems" by Huyen

Communities

• Hugging Face Discord
• LocalLLaMA subreddit
• AI Twitter/X community

Project Ideas by Level