Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure

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Ravikiran Srinivasulu

2:19:08

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1. Course Overview

1. Course Overview.mp4

01:27

2. Getting Started with Azure Machine Learning

1. Introduction.mp4

02:35

2. What Is Machine Learning.mp4

03:12

3. Introduction to Azure Machine Learning.mp4

02:27

4. Azure Machine Learning Experiment Workflow.mp4

02:11

5. Prerequisites.mp4

00:30

6. Demo - Creating an Azure Machine Learning Studio Workspace.mp4

04:01

7. Demo - Creating an Azure Machine Learning Service Workspace.mp4

02:22

8. Demo - Exploring the Dataset.mp4

03:00

9. Summary.mp4

00:52

3. Differentiating Data, Features, Targets, and Models

1. Introduction.mp4

00:52

2. Moving from Raw Data to Features.mp4

01:38

3. 6 Characteristics of a Good Feature.mp4

05:11

4. Define Target for ML Problems.mp4

03:42

5. Demo - Exploring Datasets for Different Problems.mp4

01:24

6. How Algorithms Learn Models.mp4

01:13

7. Demo - Modifying the Metadata of Datasets.mp4

02:44

8. Summary.mp4

01:00

4. Preparing Input Data for Machine Learning Models

01. Introduction.mp4

01:14

02. Data Preprocessing Methods.mp4

00:26

03. Demo - Exploratory Data Analysis.mp4

05:34

04. Demo - Data Cleaning (Erroneous Data).mp4

02:06

05. Demo - Data Cleaning (Outliers).mp4

02:17

06. Demo - Data Cleaning (Duplicate Rows).mp4

01:20

07. Demo - Data Transformation.mp4

03:36

08. Demo - Reducing Data (Record Sampling).mp4

02:01

09. Demo - Reducing Data (Attribute Sampling).mp4

00:48

10. Demo - Discretizing Data.mp4

02:37

11. Entropy-based Discretization.mp4

02:30

12. Demo - Entropy-based Discretization.mp4

00:36

13. Summary.mp4

00:43

5. Handling Missing Data

01. Introduction.mp4

00:58

02. Reasons Why Data Is Missing.mp4

01:24

03. Demo - Listwise Deletion.mp4

02:31

04. Problems in Deleting Rows.mp4

02:06

05. Demo - Using Indicator Variables.mp4

01:49

06. Replace with Mean, Median, and Mode.mp4

02:10

07. Disadvantages of Single Imputation Methods.mp4

02:10

08. Demo - Replace with MICE.mp4

01:33

09. How MICE Works.mp4

01:40

10. Summary.mp4

00:35

6. Role of Feature Engineering in Machine Learning

1. Introduction.mp4

00:48

2. Why Feature Engineering.mp4

02:23

3. Role of Feature Engineering in Model Complexity.mp4

02:04

4. Build Better Models with Feature Engineering.mp4

02:12

5. Feature Engineering Numeric Variables.mp4

01:33

6. Feature Engineering Categorical Variables.mp4

02:05

7. Demo - One-hot Encoding Categorical Variables.mp4

03:38

8. Demo - Learning with Counts Categorical Variables.mp4

03:48

9. Summary.mp4

00:57

7. Split a Data Set into Training and Testing Subsets

1. Introduction.mp4

01:30

2. Demo - Training and Testing on Same Data.mp4

03:20

3. Demo - Split Data into Training and Test Set.mp4

03:27

4. Splitting Data for Model Tuning.mp4

04:06

5. Demo - Cross-validation.mp4

04:09

6. Demo - Model Selection.mp4

02:58

7. Leave-one-out Cross Validation.mp4

01:22

8. Summary.mp4

00:57

8. Identify Data-level Issues In Machine Learning Models

1. Introduction.mp4

00:51

2. Imbalanced Dataset for Classification Problems.mp4

01:47

3. Demo - SMOTE.mp4

01:36

4. Data Scale Issues in Distance-based Models.mp4

02:17

5. Multicollinearity Problem in Regression Models.mp4

02:05

6. Outliers in Regression Models.mp4

02:38

7. Problem with High-dimensional Datasets.mp4

02:36

8. Summary.mp4

00:56

Description

In this course, you'll learn how to prepare, clean up, and engineer new features from the data with Azure Machine Learning, so the dataset can be represented in a form that's easy for the learning algorithm to learn the patterns.

What You'll Learn?

Data comes from many different sources. So when you join them, they are naturally inconsistent. In this course, Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure, you will be taken on a journey where you begin with data that's unsuitable for machine learning and use different modules in Azure Machine Learning to clean and preprocess the data. First, you will learn how to set up the data and workspace in Azure Machine Learning. Next, you will discover the role of feature engineering in machine learning. Finally, you will explore how to Identify specific data-level issues for machine learning models. When you’re finished with this course, you will have a clean dataset processed with azure machine learning modules that’s ready to build production-ready machine learning models.

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Microsoft Azure

Ravikiran Srinivasulu

Instructor's Courses

Ravikiran is an independent cloud consultant and author focused on developing solutions in Microsoft Azure. His interests include everything in the cloud space, DevOps and Machine Learning with contributions in domains like Healthcare, Banking and Web Analytics. He is very passionate about the latest and futuristic technologies and constantly updates himself with the current technology trends. He works at the intersection of education and technology. In spare time, he likes going on long road trips with family and friends.

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.