Machine Learning: End-to-End guide for Java developers

Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified

About This Book

• Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects
• Address predictive modeling problems using the most popular machine learning Java libraries
• A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical use-cases

Who This Book Is For

This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have.

What You Will Learn

• Understand key data analysis techniques centered around machine learning
• Implement Java APIs and various techniques such as classification, clustering, anomaly detection, and more
• Master key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of them
• Apply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognition
• Experiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive models
• Develop intelligent systems centered around various domains such as security, Internet of Things, social networking, and more

In Detail

Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. This course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning.

The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books:

• Java for Data Science
• Machine Learning in Java
• Mastering Java Machine Learning

Table of Contents

1. Module 1
1. Getting Started with Data Science
2. Data Acquisition
3. Data Cleaning
4. Data Visualization
5. Statistical Data Analysis Techniques
6. Machine Learning
7. Neural Networks
8. Deep Learning
9. Text Analysis
10. Visual and Audio Analysis
11. Mathematical and Parallel Techniques for Data Analysis
12. Bringing It All Together

2. Module 2
1. Applied Machine Learning Quick Start
2. Java Libraries and Platforms for Machine Learning
3. Basic Algorithms – Classification, Regression, and Clustering
4. Customer Relationship Prediction with Ensembles
5. Affinity Analysis
6. Recommendation Engine with Apache Mahout
7. Fraud and Anomaly Detection
8. Image Recognition with Deeplearning4j
9. Activity Recognition with Mobile Phone Sensors
10. Text Mining with Mallet – Topic Modeling and Spam Detection
11. What is Next?

3. Module 3
1. Machine Learning Review
2. Practical Approach to Real-World Supervised Learning
3. Unsupervised Machine Learning Techniques
4. Semi-Supervised and Active Learning
5. Real-Time Stream Machine Learning
6. Probabilistic Graph Modeling
7. Deep Learning
8. Text Mining and Natural Language Processing
9. Big Data Machine Learning – The Final Frontier