Lilly Tech Systems
Learn Machine Learning
Master the fundamentals of machine learning — from supervised and unsupervised learning to model evaluation, feature engineering, scikit-learn, and production deployment with MLOps.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is machine learning? Types of ML, the ML pipeline, and when to use ML vs. traditional programming.
2. Supervised Learning
Regression and classification algorithms: Linear Regression, Random Forest, XGBoost, SVM, and more.
3. Unsupervised Learning
Clustering, dimensionality reduction, anomaly detection, and association rules.
4. Model Evaluation
Metrics, cross-validation, confusion matrices, learning curves, and bias-variance tradeoff.
5. Feature Engineering
Numerical and categorical features, feature selection, creation, and missing value handling.
6. Scikit-learn
The sklearn ecosystem: pipelines, preprocessing, model training, hyperparameter search, and persistence.
7. MLOps & Deployment
Model versioning, serving, containerization, CI/CD for ML, monitoring, and cloud deployment.
8. Best Practices
Problem framing, data quality, model selection, experiment tracking, ethics, and production readiness.
What You'll Learn
By the end of this course, you'll be able to:
Understand ML Algorithms
Know when to use regression, classification, clustering, and how each algorithm works.
Build ML Pipelines
Implement end-to-end ML workflows with scikit-learn, from data prep to prediction.
Evaluate Models Properly
Choose the right metrics, avoid data leakage, and understand the bias-variance tradeoff.
Deploy to Production
Serve models with Flask/FastAPI, containerize with Docker, and monitor in production.