Overview

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Machine learning models rarely perform well without careful design, evaluation, and optimization. In this course, you'll learn how to build machine learning models and systematically improve their performance using proven engineering practices. You’ll start by learning how to map business problems to appropriate machine learning tasks and train multiple model types using common ML libraries. You’ll explore how different algorithms behave under varying data conditions and learn how to justify model choices based on performance and bias-variance trade-offs. Next, you’ll optimize models through systematic hyperparameter tuning and evaluate the computational cost of different algorithms to choose efficient solutions. You’ll also learn validation techniques such as cross-validation and stratified sampling to estimate model performance reliably. The course concludes by showing how to automate machine learning workflows. You’ll build end-to-end pipelines that streamline feature engineering, model training, and optimization so experiments can be reproduced and improved efficiently. By the end of this course, you’ll understand how to design, optimize, and validate machine learning models that are ready for integration into larger ML systems.

Syllabus

ML: Build, Train, Justify Models: Identify the Right ML Task for a Business Problem

You will analyze business requirements and translate them into appropriate machine learning task types, ensuring correct problem framing before modeling begins.

ML: Build, Train, Justify Models: Train Multiple Models Using ML APIs on Tabular Data

You will use ML APIs to train and compare multiple algorithms on structured datasets using reproducible workflows.

ML: Build, Train, Justify Models: Justify Model Selection Using Bias–Variance Trade-Off

You will evaluate model behavior across algorithm families and justify selection decisions using bias–variance reasoning and performance evidence.

Optimize ML Models: Hyperparameter Tuning: Understand Defaults: Hyperparameters and Algorithm Complexity

You will examine default hyperparameters and computational complexity to understand how they influence model behavior and training cost.

Optimize ML Models: Hyperparameter Tuning: Tune Systematically: Improve Models with Structured Search

You will design structured search strategies, run tuning experiments, and interpret cross-validated results to improve model performance.

Choose Cost-Effective ML Algorithms Fast: Evaluating Resource Use for Cost-Effective Models

You will benchmark training time, memory usage, and computational cost to select algorithms that meet performance and efficiency goals.

Validate and Explain Your ML Models: Stronger Validation: Using K-Fold and Stratified Sampling

You will implement k-fold and stratified validation strategies to generate reliable performance estimates, especially for imbalanced datasets.

Validate and Explain Your ML Models: Explaining Your Model: Feature Importance and SHAP

You will interpret feature-importance outputs and SHAP explanations to clearly communicate model behavior to technical and non-technical stakeholders.

Automate ML Pipelines for Peak Performance: Build, Optimize, and Publish an Automated ML Pipeline

You will construct, tune, and package an automated machine learning pipeline that integrates preprocessing, model training, and optimization into a reusable workflow.