Feature Engineering

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Google Cloud

4:02:51

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01 - Introduction

01 - Feature Engineering PDF.mp4

00:10

02 - Introduction.mp4

01:10

02 - Raw data to features

03 - Raw Data to Features.mp4

02:49

04 - Good vs Bad Features.mp4

02:46

05 - Quiz - Features are Related to the Objective.mp4

03:20

06 - Quiz - Features are knowable at prediction time.mp4

04:18

07 - Features are knowable at prediction time.mp4

03:20

08 - Features should be numeric.mp4

00:27

09 - Quiz - Features should be numeric.mp4

05:06

10 - Features should have enough examples.mp4

01:22

11 - Quiz - Features should have enough examples (part 1).mp4

02:24

12 - Quiz - Features should have enough examples (part 2).mp4

02:30

13 - Bringing human insights.mp4

00:27

14 - Representing Features.mp4

08:41

15 - ML vs Statistics.mp4

03:09

16 - Lab - Improving model accuracy with new features.mp4

00:10

17 - Improve model accuracy with new features.mp4

12:06

03 - Preprocessing and feature creation

18 - Preprocessing and feature creation.mp4

07:07

19 - Apache Beam Cloud Dataflow.mp4

09:59

20 - A Simple Dataflow Pipeline.mp4

00:19

21 - Lab - A simple Dataflow pipeline (Python).mp4

00:10

22 - Lab Solution - A Simple Dataflow Pipeline.mp4

06:55

23 - Data Pipelines at Scale.mp4

05:55

24 - MapReduce in Dataflow.mp4

00:33

25 - Lab - MapReduce in Dataflow (Python).mp4

00:10

26 - Lab Solution - MapReduce in Dataflow.mp4

03:38

27 - Dataflow Wrapup.mp4

00:09

28 - Preprocessing with Cloud Dataprep.mp4

06:44

29 - Lab Intro - Computing Time-Windowed Features in Cloud Dataprep.mp4

10:28

30 - Lab - Computing Time-Windowed Features in Cloud Dataprep.mp4

00:10

31 - Lab Solution - Computing Time-Windowed Features in Cloud Dataprep.mp4

00:36

04 - Feature crosses

32 - Introduction.mp4

01:10

33 - What is a feature cross.mp4

05:36

34 - Discretization.mp4

01:58

35 - Memorization vs. Generalization.mp4

04:35

36 - Taxi colors.mp4

04:54

37 - Lab Intro - Feature Crosses to create a good classifier.mp4

00:26

38 - Lab Solution - Feature Crosses to create a good classifier.mp4

06:18

39 - Sparsity + Quiz.mp4

05:34

40 - Lab Intro - Too Much of a Good Thing.mp4

00:31

41 - Lab Solution - Too Much of a Good Thing.mp4

07:25

42 - Implementing Feature Crosses.mp4

05:13

43 - Embedding Feature Crosses.mp4

09:15

44 - Where to Do Feature Engineering.mp4

06:33

45 - Feature Creation in TensorFlow.mp4

02:31

46 - Feature Creation in DataFlow.mp4

02:50

47 - Lab Intro - Improve ML Model with Feature Engineering.mp4

00:42

48 - Lab - Improve Machine Learning model with Feature Engineering.mp4

00:10

49 - Debrief - ML Fairness.mp4

03:50

50 - Solution - Improve ML Model with Feature Engineering.mp4

20:29

05 - TensorFlow Transform

51 - Introduction.mp4

00:37

52 - TensorFlow Transform.mp4

08:47

53 - Analyze phase.mp4

03:12

54 - Transform phase.mp4

04:32

55 - Supporting serving.mp4

03:41

56 - Exploring tf.transform.mp4

01:12

57 - Lab - Exploring tf.transform.mp4

00:10

58 - Exploring tf.transform.mp4

19:52

06 - Summary

59 - Summary.mp4

03:40

Description

Welcome to Feature Engineering where we will discuss good vs. bad features and how you can preprocess and transform them for optimal use in your models.

What You'll Learn?

Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering where we will discuss good vs. bad features and how you can preprocess and transform them for optimal use in your models.

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Pluralsight

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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.