Java Data Science Cookbook

Key Features

• This book provides modern recipes in small steps to help an apprentice cook become a master chef in data science
• Use these recipes to obtain, clean, analyze, and learn from your data
• Learn how to get your data science applications to production and enterprise environments effortlessly

Book Description

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to.

This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data.

Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more-things that will come in handy at work.

What you will learn

• Find out how to clean and make datasets ready so you can acquire actual insights by removing noise and outliers
• Develop the skills to use modern machine learning techniques to retrieve information and transform data to knowledge. retrieve information from large amount of data in text format.
• Familiarize yourself with cutting-edge techniques to store and search large volumes of data and retrieve information from large amounts of data in text format
• Develop basic skills to apply big data and deep learning technologies on large volumes of data
• Evolve your data visualization skills and gain valuable insights from your data
• Get to know a step-by-step formula to develop an industry-standard, large-scale, real-life data product
• Gain the skills to visualize data and interact with users through data insights

About the Author

Rushdi Shams has a PhD on application of machine learning in Natural Language Processing (NLP) problem areas from Western University, Canada. Before starting work as a machine learning and NLP specialist in industry, he was engaged in teaching undergrad and grad courses. He has been successfully maintaining his YouTube channel named Learn with Rushdi for learning computer technologies.

Table of Contents

1. Obtaining and Cleaning Data
2. Indexing and Searching Data
3. Analyzing Data Statistically
4. Learning from Data - Part 1
5. Learning from Data - Part 2
6. Retrieving Information from Text Data
7. Handling Big Data
8. Learn Deeply from Data
9. Visualizing Data

Rushdi Shams has a PhD on application of machine learning in Natural Language Processing (NLP) problem areas from Western University, Canada. Before starting work as a machine learning and NLP specialist in industry, he was engaged in teaching undergrad and grad courses. He has been successfully maintaining his YouTube channel named Learn with Rushdi for learning computer technologies.