Building Features from Nominal and Numeric Data in Microsoft Azure

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Mike West

1:19:42

13 View

01 Course Overview

01 Course Overview.mp4

01:59

02 Setting the Stage

02 Module Overview.mp4

02:18

03 Is This Course for You.mp4

01:39

04 Skills Recommended for This Course.mp4

01:28

05 Measures of Central Tendency.mp4

01:43

06 Measures of Variability.mp4

02:52

07 Modality and Skewness.mp4

02:26

08 Kurtosis.mp4

01:27

09 Gaussian Distributions.mp4

05:05

10 Calculating the Mean Median and Mode.mp4

03:15

11 Summary.mp4

02:31

03 Approaching Normalization and Standardization

12 Introduction.mp4

01:18

13 Machine Learning Process Distilled.mp4

03:29

14 Outlier Detection and Imputation.mp4

02:37

15 Demo Outlier Detection in Python.mp4

05:49

16 Demo Imputation in Python.mp4

03:02

17 Standardization and Normalization.mp4

01:42

18 Demo Normalize and Standardize in Python.mp4

01:19

19 Summary.mp4

01:20

04 Defining Normalization and Standardization Techniques

20 Module Overview.mp4

01:44

21 Common Scaling Approaches.mp4

02:16

22 Demo Common Scaling Approaches.mp4

02:33

23 Binning.mp4

01:34

24 Demo Binning.mp4

01:37

25 Heteroscedasticity.mp4

01:14

26 Demo Correcting Heteroscedasticity.mp4

02:04

27 Z score.mp4

01:31

28 Demo Z score.mp4

02:17

29 Summary.mp4

01:55

05 Leveraging Nominal Data in Machine Learning

30 Module Overview.mp4

01:19

31 Data Types in Statistics.mp4

01:23

32 Data Measurement Scales.mp4

03:08

33 Problems with Categorical Data.mp4

01:27

34 One hot Encoding.mp4

00:55

35 Demo Label and One hot Encoding.mp4

01:44

36 Demo Label Encoding and XGBoost.mp4

02:01

37 Summary.mp4

01:41

Description

Applying statistical techniques to your data within Azure Machine Learning Service will often boost model performance. This course will teach you the basics of data cleansing, including basic syntax and functions.

What You'll Learn?

At the core of applied machine learning is data. In this course, Building Features from Nominal and Numeric Data in Microsoft Azure, you will learn how to cleanse data within the confines of Azure Machine Learning Service. First, you will discover the sundry options you have within Azure Machine Learning Service for building your models end to end. Next, you will explore the importance of applying statistical techniques to your data to improve model performance. Finally, you will learn how to apply various data cleansing techniques to your data for enhancing real-world performance. When you are finished with this course, you will have a foundational knowledge of Azure Machine Learning Service and a solid understating of how to apply statistical techniques to your data that will help you as you move forward to becoming a machine learning engineer.

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Microsoft Azure

Mike West

Instructor's Courses

Mike has Bachelor of Science degrees in Business and Psychology. He started his career as a middle school psychologist prior to moving into the information technology space. His love of computers resulted in him spending many additional hours working on computers while studying for his master's degree in Statistics. His current areas of interests include Machine Learning, Data Engineering and SQL Server. When not working, Mike enjoys spending time with his family and traveling.

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.