Home>Courses>Scikit-Learn for Machine Learning

Intermediate

27h

Certificate of Completion

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

Adaptive Learning

AI-POWERED

Explanations

Adaptive Learning

This course includes

55 Lessons
79 Playgrounds
6 Quizzes
Course Overview
What You'll Learn
Course Content
Apply Your Skills
Recommendations

Course Overview

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, ...Show More
This comprehensive course is designed to develop the knowledge and skills to effectively utilize the scikit-learn library in Pyt...Show More

What You'll Learn

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
An understanding of data preprocessing steps

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Course Content

1.

Course Overview

1 Lessons

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

2.

Introduction to Machine Learning

7 Lessons

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

3.

Preprocessing

10 Lessons

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

4.

Supervised Learning

11 Lessons

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

5.

Unsupervised Learning

8 Lessons

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

9.

Conclusion

1 Lessons

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

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