Building Classification Models with scikit-learn

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Janani Ravi

2:33:45

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1. Course Overview

1. Course Overview.mp4

01:42

2. Understanding Classification as a Machine Learning Problem

01. Version Check.mp4

00:16

02. Module Overview.mp4

01:18

03. Prerequisites and Course Outline.mp4

01:29

04. Classification as a Machine Learning Problem.mp4

03:50

05. Logistic Regression Intuition.mp4

05:34

06. Cross Entropy Intuition.mp4

02:03

07. Accuracy, Precision, and Recall.mp4

06:10

08. Determining Decision Threshold Using ROC Curves.mp4

06:43

09. Types of Classification.mp4

03:57

10. Module Summary.mp4

01:13

3. Building a Simple Classification Model

1. Module Overview.mp4

01:02

2. Installing and Setting up scikit-learn.mp4

03:06

3. Exploring the Titanic Dataset.mp4

07:19

4. Visualizing Relationships in the Data.mp4

05:11

5. Preprocessing the Data.mp4

04:28

6. Training a Logistic Regression Binary Classifier.mp4

05:11

7. Calculating Accuracy, Precision and Recall for the Classification Model.mp4

04:53

8. Defining Helper Functions to Train and Evaluate Classification Models.mp4

07:04

9. Module Summary.mp4

01:18

4. Performing Classification Using Multiple Techniques

01. Module Overview.mp4

01:22

02. Choosing Classification Algorithms.mp4

02:02

03. Linear Discriminant Analysis and Quadratic Discriminant Analysis.mp4

07:28

04. Implementing Linear Discriminant Analysis Classification.mp4

03:42

05. Implementing Quadratic Discriminant Analysis Classification.mp4

01:53

06. Stochastic Gradient Descent.mp4

02:46

07. Implementing Stochastic Gradient Descent Classification.mp4

02:36

08. Support Vector Machines.mp4

07:18

09. Implementing Support Vector Classification.mp4

02:45

10. Nearest Neighbors.mp4

03:05

11. Implementing K-nearest-neighbors Classification.mp4

01:26

12. Decision Trees.mp4

03:00

13. Implementing Decision Tree Classification.mp4

02:33

14. Naive Bayes.mp4

04:04

15. Implementing Naive Bayes Classification.mp4

01:31

16. Module Summary.mp4

01:41

5. Hyperparameter Tuning for Classification Models

1. Module Overview.mp4

01:01

2. Hyperparameter Tuning.mp4

03:53

3. Hyperparameter Tuning a Decision Tree Clasifier Using Grid Search.mp4

06:40

4. Hyperparameter Tuning a Logistic Regression Classifier Using Grid Search.mp4

02:33

5. Module Summary.mp4

01:04

6. Applying Classification Models to Images

1. Module Overview.mp4

01:04

2. Representing Images as Matrices.mp4

03:02

3. Exploring the Fashion MNIST Dataset.mp4

06:06

4. Classifying Images Using Logistic Regression.mp4

04:04

5. Summary and Further Study.mp4

01:19

Description

This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.

What You'll Learn?

Perhaps the most ground-breaking advances in machine learning have come from applying machine learning to classification problems.

In this course, Building Classification Models with scikit-learn you will gain the ability to enumerate the different types of classification algorithms and correctly implement them in scikit-learn.

First, you will learn what classification seeks to achieve, and how to evaluate classifiers using accuracy, precision, recall, and ROC curves.

Next, you will discover how to implement various classification techniques such as logistic regression, and Naive Bayes classification.

You will then understand other more advanced forms of classification, including those using Support Vector Machines, Decision Trees and Stochastic Gradient Descent.

Finally, you will round out the course by understanding the hyperparameters that these various classification models possess, and how these can be optimized.

When you’re finished with this course, you will have the skills and knowledge to select the correct classification algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.

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Machine Learning

Janani Ravi

Instructor's Courses

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.