Python for AI Masterclass in 5 days

Explore exploratory data analysis to prevent debugging by visualizing distributions, relationships, and class separation before modeling, using matplotlib and seaborn plots like histograms, scatterplots, and heatmaps.

Explore regression as a core supervised task predicting continuous values. Start with linear regression, apply regularization (ridge, lasso, elastic net), and assess with MAE, RMSE, and R squared.

Explore classification in supervised learning, from binary and multiclass tasks to probabilistic predictions. Compare logistic regression, decision trees, random forests, and gradient boosting with practical, interpretable, robust approaches.

Transform raw data with feature engineering to produce numeric signals; encode, scale, and impute features using proper methods, then assemble a production-ready pipeline that prevents data leakage.

Replace accuracy with precision, recall, and F1 in imbalanced fraud detection, using confusion matrices and ROAUC; apply stratified cross-validation, learning curves, and simplicity for final model selection.

Master PyTorch basics by building tensors and mastering autograd, neural networks, loss functions, and optimizers; implement the training loop, convolutional networks, and model saving, evaluation, and inference.

Master PyTorch’s nn.Module as the universal base for building neural nets, learn automatic parameter tracking and a clean forward design, and explore sequential versus custom modules for scalable transfer learning.

Explore loss functions and optimizers, gradients via AutoGrad, and weight updates with zero_grad, backward, and step across training loops.

Explore the training loop end-to-end: data loading with batch size 32, forward pass, loss, backward pass, and weight updates across epochs, with validation, checkpointing, and diagnosing overfitting through loss curves.

Learn to call LLM APIs from Python by mastering prompt engineering patterns, embeddings, and a retrieval augmented generation pipeline, deployed with FastAPI and structured JSON outputs.

Master prompt engineering patterns that shape model outputs with a production-ready system prompt. Use role, output format, constraints, and examples to transition from demo to reliable, scalable features.

Explore embeddings as dense vectors that power semantic search, document chat, and retrieval augmented generation, enabling language understanding over your data with cosine similarity and vector search.

Speed up AI model serving with FastAPI, uvicorn, and Pydantic by building fast, async, self-validated APIs, exposing robust endpoints and interactive OpenAPI docs from local to production.