Roadmap

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
| Topic | Resource | Time |
|-------|----------|------|
| Python | Python Crash Course book | 2 weeks |
| Math | 3Blue1Brown YouTube | 1 week |
| NumPy/Pandas | Kaggle Learn | 1 week |

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
| Topic | Resource | Time |
|-------|----------|------|
| ML Fundamentals | Andrew Ng Coursera | 4 weeks |
| Deep Learning | fast.ai Practical DL | 2 weeks |

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
| Topic | Resource | Time |
|-------|----------|------|
| Transformers | "Attention Is All You Need" paper | 1 week |
| Hugging Face | HF NLP Course | 3 weeks |
| Karpathy | "Let's build GPT" YouTube | 2 days |

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
| Level | Project |
|-------|---------|
| Beginner | Fine-tune classifier on custom data |
| Intermediate | Build RAG chatbot for documents |
| Advanced | Train custom LLM from scratch |
| Expert | Multi-agent system with tool use |