intermediate69 lessons · 6 phases
ML & DL Mastery
A structured, from-scratch path through machine learning and deep learning — built for engineers who want to understand the math and code, not just import sklearn. Six phases covering data science foundations, supervised and unsupervised ML, deep learning, NLP & LLMs, and production MLOps — 69 lessons in total.
pythonnumpypandasmachine-learningdeep-learningpytorch
prerequisites
- →Solid Python programming (functions, classes, list comprehensions)
- →Basic high-school linear algebra (vectors, matrices) is helpful but not required
- →No ML experience needed — we build intuition before formulas
you'll learn
- ✓Master NumPy, Pandas, and statistics for fast numerical computing and data analysis
- ✓Implement every classical ML algorithm from scratch — linear models, trees, SVMs, ensembles
- ✓Build and train neural networks with PyTorch from first principles via backpropagation
- ✓Design CNNs, RNNs, LSTMs, and understand transfer learning in practice
- ✓Apply transformers, BERT, GPT-2, and LoRA fine-tuning for NLP tasks
- ✓Build object detectors, GANs, diffusion models, and RAG pipelines
- ✓Track experiments, deploy with FastAPI/Docker, and monitor models for drift in production
course structure