Ultimate Java for Data Analytics and Machine Learning (Kumar, Abhishek) (Z-Library)

Ultimate Java for Data Analytics and Machine Learning Unlock Java's Ecosystem for Data Analysis and Machine Learning Using WEKA, JavaML, JFreeChart, and Deeplearning4j Abhishek Kumar www.orangeava.com

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Dedicated To My Parents Usha Singh and Ram Vijay Kumar My Siblings Avinash and Anjali My Wife Harshita and My Daughter Yavnika

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. Abhishek holds a Bachelor of Technology in Electrical Engineering and a Master of Technology in Information and Communication Technology, both from the prestigious Indian Institute of Technology (IIT) Delhi. His academic and professional achievements highlight his commitment to excellence and innovation in the technology sector.

About the Technical Reviewer Priya Bhatia embarked on her journey in the realm of artificial intelligence with a robust academic foundation, completing her master's in AI from the esteemed Indian Institute of Technology (IIT) Hyderabad. This rigorous program not only provided her with deep theoretical insights but also honed her practical skills in the field, laying a solid groundwork for her future endeavors. In recognition of her academic prowess, Priya was awarded the prestigious Reliance Foundation scholarship in 2020-2021 for academic excellence in AI and computer science. This accolade is a testament to her dedication and outstanding performance in these disciplines. Further solidifying her academic standing, Priya Bhatia authored a research paper on AI in the healthcare domain, which was published by IEEE, demonstrating her capability to contribute meaningful advancements in technology. Transitioning from academia to the professional arena, she has amassed over three years of rich experience in the Data Science domain, working with renowned MNCs like Thales and the vibrant startup iNeuron.ai. In these roles, she has not only applied her academic learnings but also adapted to real-world challenges, delivering data-driven solutions and innovations in various projects. Beyond her professional and academic achievements, she is passionate about mentoring and education. For the past three years, she has been a public speaker and mentor in Data Structures and Algorithms (DSA) and Data Science, guiding and inspiring a new generation of enthusiasts and professionals in these fields. She extends her educational outreach through her YouTube channel, Priya Bhatia. Here, she shares quality videos on a spectrum of topics related to DSA and Data Science, catering to a wide audience eager to learn and grow in these areas.

Her path in the world of AI and Data Science is marked by a seamless integration of academic excellence, professional acumen, and a commitment to fostering knowledge and skill development in others, making her a distinguished figure in this dynamic and impactful field.

Acknowledgements Writing Ultimate Java for Data Analytics and Machine Learning has been a transformative journey, and I am deeply grateful to the many individuals who have contributed to the realization of this book. This project would not have been possible without the dedicated support, insights, and expertise of numerous people. First and foremost, I extend my sincere thanks to the dynamic Java and data science communities. Their continuous innovations and contributions have been a significant source of inspiration. The comprehensive documentation and resources available, particularly the official Java documentation and various data analytics libraries, have been indispensable. Special recognition goes to the technical reviewers, whose thorough reviews and valuable feedback have been crucial in refining the content and ensuring its accuracy. I am profoundly grateful to my family for their unwavering support and encouragement. In particular, I would like to thank my spouse for being a constant source of strength and understanding throughout this journey. To everyone at the publishing house who has contributed in various capacities, your collective efforts have been vital to this project. Your collaborative spirit and professionalism have significantly enriched the creation of Ultimate Java for Data Analytics and Machine Learning. Lastly, to the readers, thank you for choosing this book as your guide to Ultimate Java for Data Analytics and Machine Learning. I hope it serves as a valuable resource on your path to mastering data analytics and exploring the exciting world of data science.

Preface In the dynamic realm of data science, the ability to seamlessly integrate data analytics with robust programming skills is invaluable. Welcome to Ultimate Java for Data Analytics and Machine Learning – a comprehensive guide that bridges the gap between data science and software development using Java. This book aims to equip you with the knowledge and skills necessary to perform efficient data analysis, data visualization, and deep learning using Java. Whether you are a student, a seasoned Java developer, or an aspiring data scientist, this book is tailored to meet your needs. With extensive real-world use cases and easy-to-follow examples, you will be guided through the fundamental concepts and advanced techniques of data analytics, all implemented using Java. A basic understanding of statistics and relational databases will be beneficial but is not mandatory. However, a good grasp of Java is required to make the most out of this book. This book is structured into 15 chapters, each delving into different aspects of data analytics, from basic concepts to advanced applications. Here's an overview of what you can expect to learn: Chapter 1. Data Analytics Using Java: We begin our journey by understanding the fundamentals of data analytics, its importance, and the various techniques and tools available. This chapter sets the stage for the entire book, introducing you to the core concepts and methodologies of data analysis. Chapter 2. Datasets: This chapter focuses on data – its types, structures, and the processes involved in generating and pre- processing datasets. You'll learn the essentials of data cleaning and data munging to prepare your data for analysis. Chapter 3. Data Visualization: This chapter explores the world of data visualization, a crucial aspect of data science. You will learn

various charting and plotting techniques using the JFreeChart library to create insightful visual representations of your data. Chapter 4. Java Machine Learning Libraries: This chapter introduces you to popular Java libraries including WEKA, Rapidminer, ADAMS, JavaML, OpenNLP, and Mallet, and how to utilize them for implementing machine learning algorithms. It discovers the power of Java for machine learning applications. Chapter 5. Statistical Analysis: The chapter dives deep into statistical principles essential for data science, such as descriptive statistics, random sampling, Bayes' theorem, and hypothesis testing. It covers how to apply these principles using Java APIs. Chapter 6. Relational Databases: The chapter covers working with JDBC, SQL, and MySQL databases to manage and analyze structured data efficiently. In this chapter, we will learn about relational databases, their design, and data models. Chapter 7. Regression Analysis: The chapter focuses on the methods of regression analysis, including linear and polynomial regression. You will learn how to identify patterns in data and establish mathematical functions using Java. Chapter 8. Classification Analysis: This chapter covers classification algorithms like decision trees, Bayesian classifiers, and logistic regression. You will learn how to partition datasets into labeled groups for better analysis. Chapter 9. Sentiment Analysis: The chapter explores sentiment analysis using natural language processing (NLP) with Stanford CoreNLP. It will cover how to analyze customer reviews and determine sentiments in text data. Chapter 10. Cluster Analysis: The chapter discovers clustering algorithms such as K-Means, DBSCAN, and hierarchical clustering. You will learn how to group data points based on similarity and apply these techniques for customer segmentation. Chapter 11. Working with NoSQL Databases: The chapter compares SQL and NoSQL databases, and also focuses on MongoDB

in Java. You will understand the flexibility of NoSQL databases for handling dynamic data. Chapter 12. Recommender Systems: The chapter discusses the algorithms behind recommender systems used by platforms such as Amazon and Netflix. You will learn about content-based and collaborative recommender systems. Chapter 13. Applications of Data Analysis: This chapter reviews popular applications of data analytics in business intelligence and time series predictions. You will learn how to use Apache POI for working with Excel spreadsheets and perform real-time data analytics. Chapter 14. Big Data Analysis with Java: This chapter explores the challenges and techniques of big data analysis. You will learn about Google's MapReduce, Apache Hadoop, Apache Spark, and how to manage large datasets using Java. Chapter 15. Deep Learning with Java: The chapter delves into deep learning, neural networks, and the Deeplearning4j library. You will learn how to implement object classification using convolutional neural networks in Java. This book is a practical guide filled with examples, real-world scenarios, and best practices. It will empower you to harness the power of data analytics and Java, enhancing your skills and enabling you to tackle complex data challenges with confidence. Happy learning!

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Table of Contents 1. Data Analytics Using Java Introduction Structure Introduction to Data Analytics Types of Data Analytics Descriptive Analytics Predictive Analytics Prescriptive Analytics Importance of Data Analytics Data Analytics Methods Data Analytics Tools and Frameworks Apache Hadoop Apache Spark Apache Mahout Java JFreechart Deeplearning4j Apache Storm Conclusion Questions Points to Remember 2. Datasets Introduction Structure Types of Data Numeric Data Types Integral Types Floating-Point Types Text Data Types Object Data Types Datasets

Generating Datasets Pre-processing Data Handling Missing Values Converting Data Types Cleaning Data Scaling or Normalizing Variables Encoding Categorical variables Removing Outliers Feature Engineering Conclusion Questions Points to Remember 3. Data Visualization Introduction Structure Types of Charts and Plots Introduction to JFreeChart Bar Charts Histograms Line Charts Scatter Plot Time Series Charts Box Plots Understanding Quartiles Example in Java Pie Charts Advanced Data Visualization Tools Conclusion Questions Points to Remember 4. Java Machine Learning Libraries Introduction Structure

Java in Machine Learning WEKA Working with Weka RapidMiner Working with RapidMiner ADAMS Working with ADAMS JavaML Working with JavaML OpenNLP Working with OpenNLP Real-World Applications of OpenNLP Mallet Working with Mallet Comparative Analysis Conclusion Questions Points to Remember 5. Statistical Analysis Introduction Structure Descriptive Statistics Measures of Central Tendency Mean Median Mode Measures of Variability Range Variance Standard Deviation Interquartile Range Frequency Distributions Exploratory Data Analysis (EDA) Box Plots