Machine Learning Bootcamp

From your first print statement to NumPy and Pandas — the language ML is written in.

Wrap a trained model in a tiny web app or dashboard.

The numerical reasoning every model rests on.

What to do before any model.fit() — clean, explore, encode.

Linear regression to XGBoost — the workhorses you'll reach for first.

Find structure without labels — clustering and projection.

Ship a model to the cloud — Docker, CI/CD, MLflow, AWS.

Tokens to embeddings — turn text into vectors a model can learn.

ANNs, CNNs, RNNs, LSTMs, Transformers — animated end-to-end.

Build with LLMs — embeddings, RAG, agents, evaluation.

Learn by trial and error — agents, rewards, Q-learning, policy gradients.

Predict the future from the past — ARIMA, smoothing, and ML forecasting.

How Netflix, Spotify & Amazon decide what's next — CF, matrix factorization, ranking.