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.

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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

Course Content

Course Overview

1 Lessons

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

Introduction

Introduction to Machine Learning

7 Lessons

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.

Introduction to Preprocessing

Dimensionality Reduction

Imputation of Missing Values

Quiz: Preprocessing

Coding Challenge: Preprocessing

Solution: Preprocessing

Supervised Learning

11 Lessons

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

8 Lessons

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.

Introduction to Model Evaluation

Regression Metrics

Classification Metrics

Clustering Metrics

Basic Cross-Validation

Advanced Cross-Validation

Quiz: Model Evaluation

Coding Challenge: Model Evaluation

Solution: Model Evaluation

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.

Introduction to Tips and Tricks

Pipelines

Baselines

Permutation Feature Importance

Model Persistence

Quiz: Tips and Tricks

Coding Challenge: Tips and Tricks

Solution: Tips and Tricks

Conclusion

1 Lessons

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

Wrap Up

Customer Segmentation with K-Means Clustering

Project

Course Author

Arthur Mello

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