AI Features

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All LessonsFree Lessons (3)

Course Overview

Introduction to Machine Learning

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

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

Supervised Learning

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

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

Model Evaluation

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

Project

How to Predict the Traffic Volume Using Machine Learning

Tips and Tricks

Introduction to Tips and Tricks

Permutation Feature Importance

Model Persistence

Quiz: Tips and Tricks

Coding Challenge: Tips and Tricks

Solution: Tips and Tricks

Conclusion

Project

Customer Segmentation with K-Means Clustering

Scikit-Learn for Machine Learning

Coding Challenge: Tips and Tricks

Practice pipelines, feature importance, and model persistence.

We'll cover the following...

Expected output

We’ll now work with a dataset containing information on a bank’s customer base that contains the following variables:

customerID: The customer’s unique ID.
gender: The customer’s gender.
...