shashankdaima/AI-Roadmap

This is roadmap i am using to learn various things in ML from practically zero. I got most of the content from claude or from fafo.

Machine Learning and AI Expert Roadmap

1. Prerequisites (2-3 months)

• Mathematics

  • Linear Algebra
    • Vectors, matrices, tensors
    • Eigenvalues and eigenvectors
    • Matrix decomposition
  • Calculus
    • Derivatives and gradients
    • Chain rule
    • Backpropagation fundamentals
  • Statistics & Probability
    • Probability distributions
    • Hypothesis testing
    • Bayesian statistics
  • Optimization
    • Gradient descent variants
    • Convex optimization

• Programming

  • Python fundamentals
  • NumPy, Pandas, Matplotlib
  • Git version control
  • Basic software engineering practices

2. Foundation ML/DL (3-4 months)

• Classical Machine Learning

  • Supervised Learning
    • Linear/Logistic Regression (https://www.youtube.com/watch?v=jerPVDaHbEA&list=PLTDARY42LDV7WGmlzZtY-w9pemyPrKNUZ&index=2)
    • Decision Trees, Random Forests
    • SVMs
    • Feature engineering
  • Unsupervised Learning
    • Clustering (K-means, DBSCAN)
    • Dimensionality reduction (PCA, t-SNE)
  • Model evaluation & validation
    • Cross-validation
    • Metrics (accuracy, precision, recall, F1)
    • Bias-variance tradeoff

• Deep Learning Basics

  • Neural Networks
    • Architecture components
    • Activation functions
    • Loss functions
    • Optimizers
  • CNNs for computer vision
  • RNNs, LSTMs for sequences
  • Transformers architecture

3. Advanced Topics (4-6 months)

• Natural Language Processing

  • Word embeddings
  • Language models
  • Attention mechanisms
  • Transfer learning in NLP
  • RAG (Retrieval Augmented Generation)
    • Vector databases
    • Embedding techniques
    • Retrieval strategies

• Generative AI

  • VAEs and GANs
  • Diffusion Models
    • DDPM architecture
    • Noise scheduling
    • Conditioning techniques
  • Large Language Models
    • Architecture scaling
    • Training strategies
    • Fine-tuning approaches

• Reinforcement Learning

  • Markov Decision Processes
  • Q-Learning
  • Policy Gradient Methods
  • Deep RL
  • PPO, A3C algorithms

4. Practical Implementation (Ongoing)

• Frameworks & Tools

  • PyTorch
  • Hugging Face
  • MLflow for experiment tracking
  • Weight & Biases for visualization
  • Docker for deployment

• MLOps & Deployment

  • Model serving
  • Monitoring
  • CI/CD for ML
  • Scale considerations

5. Specialization & Research (3-4 months)

• Read influential papers
• Implement paper reproductions
• Join research communities
• Contribute to open source
• Write technical blogs
• Participate in competitions

Key Projects to Build

1. Classical ML

   • Credit card fraud detection
   • Customer churn prediction
   • Recommendation system

2. Computer Vision

   • Image classification from scratch
   • Object detection system
   • Image generation with GANs

3. NLP

   • Text classification
   • Question-answering system with RAG
   • Fine-tuned language model

4. Reinforcement Learning

   • Custom gym environment
   • Game-playing agent
   • Real-world optimization problem

Learning Resources

1. Courses

   • Fast.ai Practical Deep Learning
   • Stanford CS224N (NLP)
   • Berkeley CS285 (RL)
   • DeepMind YouTube series

2. Books

   • Deep Learning by Goodfellow et al.
   • Pattern Recognition by Bishop
   • Reinforcement Learning by Sutton & Barto

3. Coding Resources

   • Papers with Code (paperswithcode.com)
   • Hugging Face course
   • PyTorch tutorials
   • LangChain documentation

Online Communities

• Reddit (r/MachineLearning)
• Twitter ML community
• Discord ML servers
• Local ML meetups
• ArXiv sanity preserver

Research Paper Reading Order

1. Foundational papers:
   • AlexNet
   • ResNet
   • Transformer architecture
2. Modern developments:
   • BERT, GPT series
   • Stable Diffusion
   • RL papers (PPO, A3C)
3. https://github.com/jrin771/Everything-LLMs-And-Robotics/blob/main/README.md?plain=1

Contributing Knowledge

1. Start a technical blog
2. Create GitHub repositories with implementations
3. Write paper summaries
4. Share learning resources
5. Mentor others