Ultimate Java for Data Analytics and Machine Learning 1st Edition by Abhishek Kumar ISBN 8196815050 9788196815059 by Abhishek; Kumar instant download after payment.

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ISBN 10: 8196815050 
ISBN 13: 9788196815059
Author: Abhishek Kumar

Empower Your Data Insights with Java's Top Tools and Frameworks. Key Features ● Explore diverse techniques and algorithms for data analytics using Java. ● Learn through hands-on examples and practical applications in each chapter. ● Master essential tools and frameworks such as JFreeChart for data visualization and Deeplearning4j for deep learning. Book Description 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. What you will learn ● Understand foundational principles and types of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics. ● Master techniques for preprocessing data, including cleaning and munging, to prepare it for analysis. ● Learn how to create various charts and plots including bar charts, histograms, and scatter plots for effective data visualization. ● Explore Java-based libraries such as WEKA and Deeplearning4j for implementing machine learning algorithms. ● Develop expertise in statistical techniques including hypothesis testing, regression (linear and polynomial), and probability distributions. ● Acquire practical skills in SQL querying and JDBC for relational databases. ● Explore applications in business intelligence and deep learning, including image recognition and natural language processing. Table of Contents 1. Data Analytics Using Java 2. Datasets 3. Data Visualization 4. Java Machine Learning Libraries 5. Statistical Analysis 6. Relational Databases 7. Regression Analysis 8. Classification Analysis 9. Sentiment Analysis 10. Cluster Analysis 11. Working with NoSQL Databases 12. Recommender Systems 13. Applications of Data Analysis 14. Big Data Analysis with Java 15. Deep Learning with Java Index About the Authors 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.

Ultimate Java for Data Analytics and Machine Learning 1st 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
Scatter Plots
QQ Plots
Outliers
Data Cleaning and Normalization
Random Sampling
Random Variables
Probability Distributions
Discrete Probability Distribution
Binomial Distribution
Poisson Distribution
Geometric Distribution
Continuous Probability Distribution
Normal Distribution
Bayes’ Theorem
Real-Life Applications
Covariance and Correlation
Central Limit Theorem
Significance of the Central Limit Theorem
Example
Hypothesis Testing
Interpretation of Results
Common Pitfalls
Example
Confidence Intervals
Conclusion
Questions
Points to Remember
6. Relational Databases
Introduction
Structure
Relational Data Model
Normalization Example
Cardinality Example
Relational Databases
Structured Query Language (SQL)
Data Definition Language (DDL)
CREATE
ALTER
DROP
TRUNCATE
RENAME
Data Manipulation Language (DML)
INSERT
UPDATE
DELETE
MERGE
Data Query Language (DQL)
SELECT
JOIN
GROUP BY
ORDER BY
Transaction Control Language (TCL)
COMMIT
ROLLBACK
SAVEPOINT
Data Control Language (DCL)
GRANT
REVOKE
DENY
Working with JDBC and MySQL
Conclusion
Questions
Points to Remember
7. Regression Analysis
Introduction
Structure
Regression Algorithm
Types of Regression Analysis
Applications of Regression Analysis
Linear Regression
Ordinary Least Squares (OLS)
Gradient Descent
Assumptions of Linear Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Polynomial Regression with One Variable
Polynomial Regression with Multiple Variables
Comparing Regression models
Conclusion
Questions
Points to Remember
8. Classification Analysis
Introduction
Structure
Classification Algorithm
Types of Classification Algorithms
Mathematical Models in Classification
Binary and Muti-class Classification
Binary Classification
Multi-class Classification
Decision Trees
Handling Overfitting and Tree Pruning Techniques
Bayesian Classifier
Logistic Regression
Conclusion
Questions
Points to Remember
9. Sentiment Analysis
Introduction
Structure
Sentiment Analysis
Types of Sentiment Analysis
Techniques Used in Sentiment Analysis
Sentiment Analysis Using Java
Stanford CoreNLP
Key Features of Stanford CoreNLP
Using StanfordCoreNLP in Java
Integration with Other Libraries
Sentiment Analysis on Customer Reviews
Importance of Sentiment Analysis on Customer Reviews
Challenges in Sentiment Analysis on Customer Reviews
Sentiment Analysis on Customer Reviews Using Java and Stanford CoreNLP
Conclusion
Questions
Points to Remember
10. Cluster Analysis
Introduction
Structure
Clustering
K-Means Clustering
Choosing the Right Number of Clusters
Dealing with Non-Spherical Data Shapes
Example of K-Means Clustering
DBSCAN Clustering
Example of DBSCAN Clustering
Hierarchical Clustering
Agglomerative Hierarchical Clustering
Customer Segmentation Using K-Means Clustering
Interpreting Results and Implementing Strategies
Conclusion
Questions
Points to Remember
11. Working with NoSQL Databases
Introduction
Structure
SQL versus NoSQL DB
SQL Databases
NoSQL Databases
MongoDB Database
Installing MongoDB
Working with MongoDB in Java
Connecting to MongoDB
Accessing a Database and Collection
Inserting Documents
Querying Documents
Running Aggregations
Conclusion
Questions
Points to Remember
12. Recommender Systems
Introduction
Structure
Recommender Systems
Types of Recommender Systems
Content-Based versus Collaborative Recommender Systems
Cold Start Problem
Addressing Cold Start Problem
Content-Based Recommender Systems
Key Components of Content-Based Recommender Systems
Understanding the Operation of Content-Based Recommender Systems
Example of Content-Based Recommender System
Loading the Movie Dataset
Creating a DataModel
Creating an Item-Based Recommender
Item Similarity Calculation
Generating Recommendations
Displaying Recommendations
Collaborative Recommender Systems
Key Components of Collaborative Recommender Systems
Understanding the Operation of Collaborative Recommender Systems
Example of Collaborative Recommender System
Conclusion
Questions
Points to Remember
13. Applications of Data Analysis
Introduction
Structure
Data Analysis in Business Intelligence
Understanding Data Analysis in BI
Java Libraries for Data Analysis in BI
Apache POI for Excel Handling
Apache Commons CSV for CSV Handling
Case Studies in Business Intelligence
Time Series Prediction
Components of Time Series Data
Time Series Forecasting Methods
Java Libraries for Time Series Prediction
Time Series Forecasting Example
Advanced Forecasting Strategies: Neural Networks and Deep Learning
Real-time Data Analytics
Key Components of Real-time Data Analytics
Use Cases of Real-time Data Analytics
Java Libraries and Tools for Real-time Data Analytics
Apache Kafka Architecture
Setting up Apache Kafka
Real-time Data Analytics Example
Conclusion
Questions
Points to Remember
14. Big Data Analysis with Java
Introduction
Structure
Big Data
Real-world Case Studies
Characteristics of Big Data
Importance of Big Data
Big Data Challenges
Java Libraries for Big Data
Managing Large Datasets
Data Storage and Persistence
Distributed File Systems
NoSQL Databases
Data Ingestion and ETL
Data Ingestion
ETL (Extract, Transform, Load)
Data Compression and Serialization
Data Compression
Data Serialization
Data Partitioning and Sharding
Data Partitioning
Data Sharding
MapReduce
Understanding MapReduce
Map Phase
Shuffle and Sort
Reduce Phase
MapReduce in Java
Apache Hadoop
Understanding Apache Hadoop
Core Components
How Hadoop Works
Working with Hadoop in Java
Reading a File from HDFS
Writing a File to HDFS
Apache Spark
Understanding Apache Spark
Core Components
Resilient Distributed Datasets (RDDs)
Working with Apache Spark in Java
Word Count with Spark
Running Spark Applications
Scalability
Understanding Scalability
Principles of Scalability
Horizontal Scaling
Data Partitioning
Load Balancing
Distributed Computing
Working with Scalability in Java
Parallel Processing with ForkJoinPool
Conclusion
Questions
Points to Remember
15. Deep Learning with Java
Introduction
Structure
Neural Networks
Perceptrons
Historical Context
Structure of a Perceptron
How Perceptrons Work
Numeric Example
Limitations of Perceptrons
Deep Learning
Key Concepts in Deep Learning
Applications of Deep Learning
Latest Advancements in Deep Learning
Deep Learning versus Machine Learning
Deeplearning4j
Key Features and Capabilities
Getting Started with Deeplearning4j
Object Classification Using Convolutional Neural Networks
Convolutional Neural Networks
Key Components of CNNs
Object Classification Example
Conclusion
Questions
Points to Remember
Index

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