Lilly Tech Systems
Python for Machine Learning
Master machine learning with Python — from Scikit-learn fundamentals to deep learning with PyTorch and production ML pipelines.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
ML ecosystem overview, environment setup, GPU configuration, and the ML workflow.
2. Scikit-learn Basics
The sklearn API, estimators, train/test split, cross-validation, and pipelines.
3. Regression
Linear, polynomial, decision tree, random forest, and gradient boosting regression.
4. Classification
Logistic regression, SVM, KNN, decision trees, random forests, and evaluation metrics.
5. Clustering
K-Means, DBSCAN, hierarchical clustering, PCA, t-SNE, and anomaly detection.
6. Model Evaluation
Cross-validation, learning curves, hyperparameter tuning, and bias-variance tradeoff.
7. Deep Learning with PyTorch
Tensors, neural networks, training loops, CNNs, transfer learning, and GPU training.
8. ML Pipeline
End-to-end pipelines, deployment with FastAPI, Docker, MLflow, and CI/CD.
9. Best Practices
Experiment tracking, reproducibility, model registry, monitoring, and common mistakes.
What You'll Learn
By the end of this course, you'll be able to:
Train ML Models
Build and train regression, classification, and clustering models with Scikit-learn.
Evaluate Properly
Use cross-validation, tune hyperparameters, and avoid common pitfalls like data leakage.
Build Neural Networks
Create and train deep learning models with PyTorch, including CNNs and transfer learning.
Deploy to Production
Build end-to-end ML pipelines with FastAPI, Docker, and MLflow for production readiness.