Mastering scikit-learn: Building Machine Learning Models

Overview

Machine learning in production starts with scikit-learn. This course walks through the full modeling lifecycle — loading data, preprocessing, choosing algorithms, cross-validation, hyperparameter tuning, and interpreting results — using scikit-learn’s consistent API so patterns transfer to other libraries later.

What you will learn

• Scikit-learn estimators, transformers, and pipelines
• Train/test splits, cross-validation, and avoiding leakage
• Classification and regression algorithms and when to apply them
• Feature scaling, encoding, and missing-value strategies
• Grid search and metric selection for model comparison
• Interpreting coefficients, feature importance, and error types

Who this course is for

Python developers entering ML, data analysts ready to build predictive models, and students who completed introductory statistics or data science courses.

Prerequisites

Python basics and comfort with NumPy/Pandas at an introductory level.

How enrollment works

Sharad Khare hosts structured video lessons on Udemy for convenient playback, progress tracking, and certificates. Use the button above to open the official course page. Pricing and promotions are set by Udemy. Many learners pair video lessons with the free note packs on this site’s Notes page for offline review.