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.

Intermediate

55 Lessons

27h

Certificate of Completion

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

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

79 Playgrounds

6 Quizzes

This course includes

79 Playgrounds

6 Quizzes

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

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

What You'll Learn

An understanding of data preprocessing steps

Course Content

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

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

Introduction to Preprocessing Feature Extraction Scaling Discretizing Encoding Dimensionality Reduction Imputation of Missing Values Quiz: Preprocessing Coding Challenge: Preprocessing Solution: Preprocessing

Supervised Learning

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

Introduction to Supervised Learning Linear Models SVM SGD KNN Decision Trees Naive Bayes Ensemble Methods Quiz: Supervised Learning Coding Challenge: Supervised Learning Solution: Supervised Learning

Unsupervised Learning

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

Introduction to Unsupervised Learning GMM K-Means DBSCAN Hierarchical Clustering Quiz: Unsupervised Learning Coding Challenge: Unsupervised Learning Solution: 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

Tips and Tricks

8 Lessons

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

Conclusion

1 Lesson

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

Customer Segmentation with K-Means Clustering

Project

Course Author

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