Go beyond theory and build machine learning models that actually ship. This course walks you through the full engineering lifecycle: training supervised and unsupervised models with Python and scikit-learn, engineering features, tuning hyperparameters, and evaluating results with the right metrics. You will then package models with Docker, serve them as REST APIs with FastAPI and Flask, deploy on AWS SageMaker, and keep them healthy with drift monitoring and retraining pipelines.

Modules

Learning Algorithm Foundations

Lessons

• How Machines Learn from Labeled Data Preview
• Regression and Classification in Practice
• Clustering and Dimensionality Reduction Without Labels

Feature Engineering and Model Selection

Lessons

• Turning Raw Data into Useful Features
• Choosing the Right Model for the Problem
• Hyperparameter Tuning with Search Strategies

Evaluating Model Performance

Lessons

• Accuracy and Why It Misleads
• Precision Recall and the F1 Score
• ROC Curves and AUC for Threshold Decisions

Serving Models as REST APIs

Lessons

• From Trained Model to Prediction Service
• Building a FastAPI Inference Service
• Flask Deployments and Input Validation

Packaging and Cloud Deployment

Lessons

• Containerizing a Model with Docker
• Deploying to AWS SageMaker Endpoints
• Scaling Latency and Cost Trade-offs

Monitoring and Retraining

Lessons

• Detecting Data and Concept Drift
• Building a Retraining Pipeline
• Closing the Loop with Automated Evaluation