Most tutorials stop at model.fit(). You keep going — to a live service, real traffic, and alerts when the model drifts.
You can write Python, but every machine learning tutorial either hands you a magic `model.fit()` line you don't understand, or buries you in mathematics before you've trained anything. This course fixes both ends. You start by coding linear and logistic regression by hand in NumPy, writing the gradient descent loop yourself line by line, until training stops being a black box. Then you move up: scikit-learn Pipelines on messy real datasets, decision trees and ensembles like Random Forest and Gradient Boosting, neural networks in PyTorch, convolutional networks (CNNs) for images, and sequence models for text and time series. The back half is the part most courses skip. You tune hyperparameters with Optuna instead of guessing, evaluate models with precision, recall and calibration, explain individual predictions with SHAP, wrap the winner in a FastAPI web service, containerise it with Docker, and watch it for data drift once real users are hitting it. Every module ships runnable Python and a project you can put in a repository.
Built by Lakshya Kumar
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Clean, encode and scale your data properly and a simple model will beat a fancy one trained on garbage.
One tree you can read by eye, then hundreds of them voting — still the first thing worth trying on table-shaped data.
Hand-code the forward and backward pass, then rebuild it in PyTorch and see exactly what the framework was doing for you.
Build a convolutional network (CNN) from filters up, then fine-tune a pretrained one to hit real accuracy on a small dataset.
Give a model memory over text and time-series data, and find out exactly which limits made transformers necessary.
A one-hour Optuna search finds better settings than a week of tweaking numbers by hand and hoping.
Pick the metric that matches the decision, then explain any single prediction to someone who has to trust it.
Wrap your trained model in a FastAPI web service, containerise it with Docker, and serve real requests over the network.
The world shifts under your model. Detect the drift, alert on it, and retrain automatically before users notice.
Complete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Pick a real tabular dataset (not Iris, not MNIST — something from Kaggle, UCI, or your own domain). Train at least two model families (e.g., Ridge + GBM), compare them with proper nested cross-validation, tune hyperparameters with Optuna, generate SHAP explanations for the winning model, serialize it, and deploy behind a FastAPI + Docker endpoint. Ship as a GitHub repo with: README with dataset description, model card (performance metrics, feature importance, fairness notes), `train.py`, `serve/` directory with Dockerfile, and a `curl` example that hits your running container.
Paste this into any AI chat. Fill in the bracketed parts with your context — you'll get back a straight answer on whether this belongs on your plate.
I'm considering a 'Machine Learning: Zero to Production' course. It starts from a single neuron in NumPy and ends with a containerised, monitored ML service. 100 challenges in Python: gradient descent from scratch, scikit-learn Pipelines, decision trees, ensembles (RF/GBM), PyTorch neural nets, CNNs, sequence models, Optuna tuning, SHAP, FastAPI+Docker serving, and drift monitoring. Context about me: 1. My current background: [e.g. "Python developer, never touched ML", "data analyst who uses Excel and SQL", "CS student who took one stats course", "backend engineer tired of calling OpenAI APIs without understanding them"] 2. What I can already do in Python: [e.g. "write functions and classes", "use pandas", "never used NumPy", "comfortable with decorators"] 3. What I want to be able to do after this: [e.g. "get an ML engineer job", "deploy my own model at work", "understand what Kaggle competitors are doing", "build a recommendation system"] Answer these: - For my background, which 2 modules will give me the highest leverage in the next 3 months, and why? - Name a concrete artifact I'd build that I could actually show in a job interview or use at work. - Is 60 hours worth it for me, or should I do something shorter first? - What will I NOT be able to do after this course — e.g. "train large language models", "build real-time video classifiers at scale", "replace a data science team"?
Build a feature pipeline (batch via Airflow + real-time via streaming) that writes to an offline and online feature store. Train a model on the offline features and serve predictions using the online store at P95 < 50ms. Test feature parity between training and serving.
Deploy a model to production (real or simulated) with monitoring: prediction logging, ground-truth join, drift detection (KS-test on inputs, prediction distribution change), and an alert that fires on a deliberately injected distribution shift.
Build a pipeline that retrains a model weekly: pulls fresh data, validates quality, retrains, evaluates against the current production model, and only promotes if metrics improve. Include rollback for catastrophic regressions.
Wire MLflow or Weights & Biases into your training stack. Run 5+ experiments with different hyperparameters; produce a comparison report. Reproduce one experiment from scratch using only the tracked metadata. Document the data versioning approach.
Canonical reference. Open it alongside every scikit-learn task.