Curriculum

18 Chapters · 18 Lessons

  1. 1

    Book Preview

    1. Ultimate Java for Data Analytics and Machine Learning Free preview
  2. 2

    Introduction

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Data Analytics Using Java

    1. (Included in full purchase)

      Data Analytics Using Java

  4. 4

    Chapter 2 : Datasets

    1. (Included in full purchase)

      Datasets

  5. 5

    Chapter 3 : Data Visualization

    1. (Included in full purchase)

      Data Visualization

  6. 6

    Chapter 4 : Java Machine Learning Libraries

    1. (Included in full purchase)

      Java Machine Learning Libraries

  7. 7

    Chapter 5 : Statistical Analysis

    1. (Included in full purchase)

      Statistical Analysis

  8. 8

    Chapter 6 : Relational Databases

    1. (Included in full purchase)

      Relational Databases

  9. 9

    Chapter 7 : Regression Analysis

    1. (Included in full purchase)

      Regression Analysis

  10. 10

    Chapter 8 : Classification Analysis

    1. (Included in full purchase)

      Classification Analysis

  11. 11

    Chapter 9 : Sentiment Analysis

    1. (Included in full purchase)

      Sentiment Analysis

  12. 12

    Chapter 10 : Cluster Analysis

    1. (Included in full purchase)

      Cluster Analysis

  13. 13

    Chapter 11 : Working with NoSQL Databases

    1. (Included in full purchase)

      Working with NoSQL Databases

  14. 14

    Chapter 12 : Recommender Systems

    1. (Included in full purchase)

      Recommender Systems

  15. 15

    Chapter 13 : Applications of Data Analysis

    1. (Included in full purchase)

      Applications of Data Analysis

  16. 16

    Chapter 14 : Big Data Analysis with Java

    1. (Included in full purchase)

      Big Data Analysis with Java

  17. 17

    Chapter 15 : Deep Learning with Java

    1. (Included in full purchase)

      Deep Learning with Java

  18. 18

    INDEX

    1. (Included in full purchase)

      INDEX

About the course

This book is a comprehensive guide to data analysis using Java. It starts with the fundamentals, covering the purpose of data analysis, different data types and structures, and how to pre-process datasets. It then introduces popular Java libraries like WEKA and Rapidminer for efficient data analysis. The middle section of the book dives deeper into statistical techniques like descriptive analysis and random sampling, along with practical skills in working with relational databases (JDBC, SQL, MySQL) and NoSQL databases. It also explores various analysis methods like regression, classification, and clustering, along with applications in business intelligence and time series prediction. The final part of the book gives a brief overview of big data analysis with Java frameworks like MapReduce, and introduces deep learning with the Deeplearning4J library. Whether you're new to data analysis or want to improve your Java skills, this book offers a step-by-step approach with real-world examples to help you master data analysis using Java.

About the Author

Abhishek Kumar has been a pivotal figure in the design and development of complex enterprise-grade software for over 12 years. His professional journey has seen him contributing his extensive systems programming expertise to leading technology companies including Adobe, Intel, ARM, Samsung, and NVIDIA. Currently, he serves as a Senior Computer Scientist, where he continues to excel in his field. Abhishek is deeply passionate about teaching programming and machine learning. This passion is reflected in his authorship of the book Rust Crash Course and the creation of several successful courses covering C++, Rust, Lua, Data Structures and Algorithms, and Machine Learning. His dedication to advancing the field is further demonstrated by his possession of a US patent in Computer Vision and Deep Learning.