Machine Learning: End-to-End guide for Java developers (PDF)

Develop, Implement and Tuneup your Machine Learning applications using the power of Java programming

• 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

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.

• 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

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. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The 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

On completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence.

This comprehensive course proceeds from being a tutorial to a practical guide, providing an introduction to machine learning and different machine learning techniques, exploring machine learning with Java libraries, and demonstrating real-world machine learning use cases using the Java platform.