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Scikit-Learn for Machine Learning

Learn how to build and evaluate machine learning models using scikit-learn, from data preprocessing to model selection and evaluation.

4.3

55 Lessons

2 Projects

27h

Updated 2 weeks ago

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LEARNING OBJECTIVES

An understanding of data preprocessing steps
Proficiency in model selection and evaluation
Implementation level skills for designing supervised learning algorithms
An insight into unsupervised learning techniques
Working knowledge of hyperparameter tuning and optimization

Learning Roadmap

55 Lessons6 Quizzes

Course Overview

Get familiar with fundamental machine learning concepts, data preprocessing, techniques, and model evaluation using scikit-learn.

Introduction

Introduction to Machine Learning

Look at core machine learning principles, process steps, and using scikit-learn for practical applications.

What Is Machine Learning?

The Machine Learning Process

What Is Scikit-Learn?

Importing Data with Scikit-Learn

Quiz: Introduction to Machine Learning

Coding Challenge: Importing Data

Solution: Importing Data

Preprocessing

10 Lessons

Break apart preprocessing techniques like feature extraction, scaling, encoding, and imputation for data preparation.

Supervised Learning

11 Lessons

Apply your skills to train and evaluate supervised learning models using key algorithms and techniques.

Unsupervised Learning

8 Lessons

Explore clustering techniques for uncovering patterns in unlabeled data using unsupervised learning.

Model Evaluation

9 Lessons

See how it works to evaluate machine learning models through metrics, cross-validation, and real-world application.

How to Predict the Traffic Volume Using Machine Learning

Project

Premium

Tips and Tricks

8 Lessons

Master the strategies for enhancing machine learning workflows with scikit-learn.

Conclusion

Learn how to use scikit-learn to build, evaluate, and improve machine learning models.

Wrap Up

Customer Segmentation with K-Means Clustering

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

This comprehensive course is designed to develop the knowledge and skills to effectively utilize the scikit-learn library in Python for machine learning tasks. It is an excellent resource to help you develop practical machine learning applications using Python and scikit-learn. In this course, you’ll learn fundamental concepts such as supervised and unsupervised learning, data preprocessing, and model evaluation. You’ll also learn how to implement popular machine learning algorithms, including regression, classification, and clustering, using scikit-learn’s user-friendly API. The course also introduces advanced topics such as ensemble methods, model interpretation, and hyperparameter optimization. After taking this course, you’ll gain hands-on experience in applying machine learning techniques to solve diverse data-driven problems. You’ll also be equipped with the expertise to confidently leverage scikit-learn for a wide range of machine learning applications in industry as well as academia.

ABOUT THE AUTHOR

Arthur Mello

Data scientist and educator

Learn more about Arthur

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