Machine Learning & AI Roadmap

From linear regression to large language models. Understand the math, build the models, and ship AI products.

4 stages · 17 topics 0% done
Machine Learning & AI
✓ Linear Algebra
✓ Calculus
✓ Mathematics
✓ Statistics
✓ Python for ML
✓ Fundamentals
✓ Supervised Learning
✓ Unsupervised Learning
✓ PyTorch
✓ TensorFlow
✓ Deep Learning
✓ CNNs
✓ RNNs / Transformers
✓ MLOps
✓ Production
✓ LLMs
✓ Deployment
Text version of this roadmap

1. Mathematics

  • Linear Algebra: Vectors, matrices, eigendecomposition
  • Calculus: Derivatives, gradients, optimization
  • Statistics: Probability, distributions, hypothesis tests

2. Fundamentals

  • Python for ML: NumPy, pandas, matplotlib
  • Supervised Learning: Regression, classification, trees
  • Unsupervised Learning: Clustering, dimensionality reduction

3. Deep Learning

  • PyTorch: Tensors, autograd, modules, training
  • TensorFlow: Keras, TFX, deploy with TFServing
  • CNNs: Convolutions, pooling, architectures
  • RNNs / Transformers: LSTM, attention, BERT, GPT

4. Production

  • MLOps: Pipelines, feature stores, model registry
  • LLMs: Prompt engineering, RAG, fine-tuning
  • Deployment: APIs, containers, edge inference