Md. Rezaul Karim - Machine Learning with Scala Quick Start Guide: Leverage popular machine learning algorithms and techniques and implement them in Scala

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Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, plus 10 years of R&D experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI).

Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a Ph.D. candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

Ajay Kumar N has experience in big data, and specializes in cloud computing and various big data frameworks, including Apache Spark and Apache Hadoop. His primary language of choice is Python, but he also has a special interest in functional programming languages such as Scala. He has worked extensively with NumPy, pandas, and scikit-learn, and often contributes to open source projects related to data science and machine learning.

Sarbashree Ray has over 5 years' experience in big data analytics, currently at Reliance Jio as a deputy manager. Sarbashree is an engineering professional with experience of designing and executing solutions for complex business problems involving large-scale big data and machine learning technologies, real-time analytics, and reporting solutions. He is also known for using the right tools when and where they make sense, and creating intuitive architectures that help organizations effectively analyze and process terabytes of structured and unstructured data. He is also able to integrate state-of-the-art big data technologies into overall architectures and lead a team of developers through the construction, testing, and implementation phases.

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Machine learning has made a huge impact not only in academia, but also in industry, by turning data into actionable intelligence. Scala is not only an object-oriented and functional programming language, but can also leverage the advantages of Java Virtual Machine (JVM). Scala provides code complexity optimization and offers concise notation, which is probably the reason it has seen a steady rise in adoption over the last few years, especially in data science and analytics.

This book is aimed at aspiring data scientists, data engineers, and deep learning enthusiasts who are newbies and want to have a great head start at machine learning best practices. Even if you're not well versed in machine learning concepts, but still want to expand your knowledge by delving into practical implementations of supervised learning, unsupervised learning, and recommender systems with Scala, you will be able to grasp the content easily!

Throughout the chapters, you'll become acquainted with popular machine learning libraries in Scala, learning how to carry out regression and classification analysis using both linear methods and tree-based ensemble techniques, as well as looking at clustering analysis, dimensionality reduction, and recommender systems, before delving into deep learning at the end.

After reading this book, you will have a good head start in solving more complex machine learning tasks. This book isn't meant to be read cover to cover. You can turn the pages to a chapter that looks like something you're trying to accomplish or that ignites your interest.