Module 09: ML with Python
"The best ML engineer is not the one who knows the most algorithms - it's the one who can go from raw data to a deployed model faster and more reliably than anyone else."
This module is about tooling fluency. The ideas in ML are universal, but the tools you use determine how fast you can experiment, how reliably you can ship, and how easily a teammate can reproduce your work six months later.
Every lesson in this module is code-first. You will write real NumPy, Pandas, sklearn, PyTorch, and HuggingFace code - the same patterns that appear in production ML systems at every major tech company.
The ML Python Stack
Each layer builds on the one below. NumPy is the math engine everything else depends on. Pandas sits on top of NumPy and makes tabular data manageable. scikit-learn standardizes the API for classical ML and provides the Pipeline abstraction that prevents data leakage. PyTorch gives you dynamic computation graphs for deep learning. HuggingFace shortens the path from "I need a language model" to working code dramatically. W&B ties experiments together so you can reproduce any run from six months ago.
Why This Stack
There are other tools - JAX, TensorFlow, fast.ai, Ray, MLflow. This stack is taught because:
- NumPy + Pandas - universal. Every other Python ML tool either wraps them or exports to them.
- scikit-learn - the pipeline API is the cleanest abstraction in classical ML. Even if you use XGBoost or LightGBM, you will often wrap them in sklearn pipelines.
- PyTorch - dominant in research (85%+ of papers) and increasingly dominant in production. The dynamic graph makes debugging natural.
- HuggingFace - using a 7B parameter model in 10 lines of code is genuine engineering leverage.
- W&B - experiment tracking is the difference between "I think I tried that" and "here is the exact run with the exact config that produced this result."
Module Lessons
| # | Topic | Key Skills |
|---|---|---|
| 01 | NumPy for ML | Broadcasting, vectorization vs loops, SVD, memory layout |
| 02 | Pandas for ML | Feature aggregation, missing data, time series, memory optimization |
| 03 | scikit-learn Pipelines | No-leakage pipelines, custom transformers, grid search |
| 04 | PyTorch Foundations | Tensors, autograd, nn.Module, device management |
| 05 | PyTorch Training Loop | Forward/backward/step, mixed precision, checkpointing |
| 06 | DataLoaders and Datasets | Custom datasets, num_workers, streaming, performance |
| 07 | HuggingFace Ecosystem | Transformers, datasets, Trainer, PEFT/LoRA |
| 08 | Weights & Biases | Logging, sweeps, artifacts, reproducibility |
Prerequisites
- Python 3.9+ - comfortable with classes, decorators, context managers
- Linear algebra fundamentals at a conceptual level (matrix multiply, what eigenvalues mean)
- Basic ML vocabulary (train/test split, loss functions, gradient descent)
How to Use This Module
Run the code yourself. Every snippet is designed to be copy-pasted into a notebook or script and executed immediately. The interview Q&A at the end of each lesson reflects real questions asked in ML engineering interviews at top companies.
Do not skim. The difference between knowing that zero_grad() exists and understanding why you must call it before each backward pass is the difference between debugging a subtle training bug in five minutes versus five hours.
:::tip Project goal By the end of this module, build a complete pipeline: read a dataset with Pandas, preprocess with a sklearn Pipeline, train a PyTorch model tracked with W&B, then fine-tune a HuggingFace model with LoRA. That sequence is 80% of what production ML engineers do daily. :::