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AI Features

Scikit-Learn for Machine Learning

1.Course Overview

2.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

3.Preprocessing

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

4.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

5.Unsupervised Learning

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

6.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

7.Tips and Tricks

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

8.Conclusion

Project

Customer Segmentation with K-Means Clustering

Scikit-Learn for Machine Learning

Quiz: Preprocessing

Check your understanding of preprocessing and its steps.

We'll cover the following...

Quiz: Preprocessing

1.

When we use MinMaxScaler to scale a feature, what will be its range?

A.

Between $-1$ and $1$

B.

Between $0$ and $1$

C.

Between $-\infty$ and $+\infty$

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