Preparing Data for Machine Learning with Java

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Federico Mestrone

2:02:32

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1. Course Overview

1. Course Overview.mp4

01:14

2. Ingesting Data from Files in Various Formats

1. Introduction.mp4

02:29

2. What Is Data Preparation.mp4

01:22

3. Ingesting CSV and Excel Files.mp4

05:23

4. Ingesting JSON and XML Files.mp4

03:33

5. Demo - Ingestion.mp4

06:06

6. Summary.mp4

00:46

3. Automating Data Collection and Scheduling

1. Introduction.mp4

01:55

2. Folder Monitoring.mp4

05:53

3. Task Scheduling.mp4

03:04

4. Demo - Using the File Watcher API.mp4

05:30

5. Demo - Using the Quartz Scheduler Library.mp4

05:00

6. Selenium.mp4

03:09

7. Demo - Using the Selenium IDE.mp4

02:18

8. Demo - Coding for Selenium.mp4

05:39

9. Summary.mp4

00:46

4. Data Cleaning Using Regex and Formatter

1. Introduction.mp4

01:54

2. Lambdas and Streams.mp4

05:48

3. Regular Expressions Overview.mp4

03:32

4. Using Regular Expressions in Java.mp4

05:10

5. Demo - Data Cleaning Pipeline.mp4

05:11

6. Summary.mp4

00:23

5. Data Transformation

1. Introduction.mp4

01:15

2. Data Transformation Basics.mp4

05:01

3. Scaling, Data Skew, and Data Bias.mp4

06:36

4. Demo - Data Transformation Pipeline.mp4

06:23

5. Summary.mp4

00:32

6. Data Preparation at Scale

1. Introduction.mp4

02:15

2. Distributed Data Pipelines.mp4

03:25

3. Beam SDK Concepts.mp4

03:52

4. Beam SDK Engines and GCP Dataflow.mp4

05:46

5. Demo - Developing Beam SDK Pipelines.mp4

05:58

6. Demo - Deploying Beam SDK Pipelines to GCP Dataflow.mp4

04:25

7. Summary.mp4

00:36

8. Wrap Up.mp4

00:23

Description

Data is at the heart of machine learning. This course will teach you how to bring data into Java from various sources, as well as how to perform basic tidying up and transformations in view of further processing by specialized Java ML libraries.

What You'll Learn?

Machine learning algorithms require that data is formatted and presented in very specific ways. In this course, Preparing Data for Machine Learning with Java, you’ll learn to use the standard Java API to make data ready for ML libraries. First, you’ll explore various options to read files into Java objects and data structures. Next, you’ll discover how to scrape the web for data you could use in your ML models. Finally, you’ll learn how to perform transformation both in vanilla Java and at scale with the Beam SDK. When you’re finished with this course, you’ll have the skills and knowledge of data gathering needed to digitize various sources into Java data structures.

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Java

Machine Learning

Federico Mestrone

Instructor's Courses

Also known as Fed in the English-speaking world, Federico has an intriguing eclectic approach to technology. In love with it since he was 12 - back in the days of the Commodore 64 - he betrays it on a regular basis: first with ballet and contemporary dance, then with synchronised swimming, recently with Japanese language and culture. Mostly a Java/Scala developer and Linux/Mac OS user, over the course of 20+ years he has had commercial experience in several other platforms and programming languages, from C++ to iOS/Android to Python to Angular. Currently he concentrates on (technical) training and education, but hasn't really decided what he wants to do when he grows up yet!

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.