Preparing Data for Feature Engineering and Machine Learning

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Janani Ravi

3:17:18

47 View

01 - Course Overview

01 - Course Overview.mp4

01:44

02 - Understanding the Role of Features in Machine Learning

02 - Module Overview.mp4

01:10

03 - Prerequisites and Course Outline.mp4

01:13

04 - Features and Labels.mp4

06:21

05 - The Machine Learning Workflow.mp4

03:59

06 - Components of Feature Engineering.mp4

02:37

07 - Feature Selection, Feature Learning, and Feature Extraction.mp4

06:51

08 - Feature Combination and Dimensionality Reduction.mp4

03:51

09 - Training, Validation, and Test Data.mp4

05:35

10 - K-fold Cross Validation.mp4

04:13

11 - Module Summary.mp4

01:18

03 - Preparing Data for Machine Learning

12 - Module Overview.mp4

01:35

13 - Problems with Data.mp4

03:58

14 - Dealing with Missing Values.mp4

04:35

15 - Dealing with Outliers.mp4

05:36

16 - Applying Different Techniques to Handle Missing Values.mp4

07:26

17 - Detecting and Handling Outliers.mp4

07:01

18 - Reading and Exploring the Dataset.mp4

07:51

19 - Perform Simple and Multiple Linear Regression.mp4

04:25

20 - Module Summary.mp4

01:08

04 - Understanding and Implementing Feature Selection

21 - Module Overview.mp4

01:33

22 - Types of Data.mp4

04:45

23 - Measuring Correlations.mp4

04:33

24 - Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods.mp4

05:34

25 - Feature Selection Using Missing Value Ratio.mp4

05:11

26 - Calculating and Visualizing Correlations Using Pandas.mp4

06:06

27 - Calculating and Visualizing Correlations Using Yellowbrick.mp4

02:50

28 - Feature Selection Using Filter Methods.mp4

06:07

29 - Feature Selection Using Wrapper Methods.mp4

05:55

30 - Feature Selection Using Embedded Methods.mp4

05:05

31 - Module Summary.mp4

01:35

05 - Exploring Feature Extraction Techniques

32 - Module Overview.mp4

01:21

33 - Representing Images as Matrices and Image Preprocessing Techniques.mp4

04:45

34 - Feature Detection and Extraction from Images.mp4

05:03

35 - Feature Extraction from Text.mp4

05:53

36 - Module Summary.mp4

01:15

06 - Implementing Feature Extraction

37 - Module Overview.mp4

01:08

38 - Tokenization and Visualizing Frequency Distributions.mp4

03:57

39 - Performing Normalization Using Different Techniques.mp4

05:04

40 - Creating Feature Vectors from Text Data.mp4

06:24

41 - Loading and Transforming Images.mp4

05:07

42 - Extracting Features from Images.mp4

03:28

43 - Detecting Keypoints and Descriptors to Perform Image Matching.mp4

05:06

44 - Extracting Text from Images Using OCR.mp4

03:54

45 - Extracting Features from Dates.mp4

04:48

46 - Working with Geospatial Features.mp4

06:47

47 - Summary and Further Study.mp4

01:37

Description

This course covers categories of feature engineering techniques used to get the best results from a machine learning model, including feature selection, and several feature extraction techniques to re-express features in the most appropriate form.

What You'll Learn?

However well designed and well implemented a machine learning model is, if the data fed in is poorly engineered, the model’s predictions will be disappointing.

In this course, Preparing Data for Feature Engineering and Machine Learning, you will gain the ability to appropriately pre-process your data -- in effect engineer it -- so that you can get the best out of your ML models.

First, you will learn how feature selection techniques can be used to find predictors that contain the most information. Feature selection can be broadly grouped into three categories known as filter, wrapper, and embedded techniques and we will understand and implement all of these.

Next, you will discover how feature extraction differs from feature selection, in that data is substantially re-expressed, sometimes in forms that are hard to interpret. You will then understand techniques for feature extraction from image and text data.

Finally, you will round out your knowledge by understanding how to leverage powerful Python libraries for working with images, text, dates, and geo-spatial data.

When you’re finished with this course, you will have the skills and knowledge to identify the correct feature engineering techniques, and the appropriate solutions for your use-case.

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Machine Learning

Data Science

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.