Preparing Data for Machine Learning with Java

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

2. Ingesting Data from Files in Various Formats

1. Introduction

2. What Is Data Preparation

3. Ingesting CSV and Excel Files

4. Ingesting JSON and XML Files

5. Demo - Ingestion

6. Summary

3. Automating Data Collection and Scheduling

1. Introduction

2. Folder Monitoring

3. Task Scheduling

4. Demo - Using the File Watcher API

5. Demo - Using the Quartz Scheduler Library

6. Selenium

7. Demo - Using the Selenium IDE

8. Demo - Coding for Selenium

9. Summary

4. Data Cleaning Using Regex and Formatter

1. Introduction

2. Lambdas and Streams

3. Regular Expressions Overview

4. Using Regular Expressions in Java

5. Demo - Data Cleaning Pipeline

6. Summary

5. Data Transformation

1. Introduction

2. Data Transformation Basics

3. Scaling, Data Skew, and Data Bias

4. Demo - Data Transformation Pipeline

5. Summary

6. Data Preparation at Scale

1. Introduction

2. Distributed Data Pipelines

3. Beam SDK Concepts

4. Beam SDK Engines and GCP Dataflow

5. Demo - Developing Beam SDK Pipelines

6. Demo - Deploying Beam SDK Pipelines to GCP Dataflow

7. Summary

8. Wrap Up

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تولید کننده: PluralSight