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 |