Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully
Key Features:
- Get to grips with the concepts of machine learning through exciting real-world examples
- Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning
- Learn to build your own machine learning system with this example-based practical guide
Book Description:
Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.
This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.
You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.
Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
What You Will Learn:
- Utilize the power of R to handle data extraction, manipulation, and exploration techniques
- Use R to visualize data spread across multiple dimensions and extract useful features
- Explore the underlying mathematical and logical concepts that drive machine learning algorithms
- Dive deep into the world of analytics to predict situations correctly
- Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
- Write reusable code and build complete machine learning systems from the ground up
- Solve interesting real-world problems using machine learning and R as the journey unfolds
- Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science
Who this book is for:
If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary.
Raghav Bali is a Data Scientist at Optum (United Health Group). His work involves research & development of enterprise level solutions based on Machine Learning, Deep Learning and Natural Language Processing for Healthcare & Insurance related use cases. In his previous role at Intel, he was involved in enabling proactive data driven IT initiatives. He has also worked in ERP and finance domains with some of the leading organizations in the world. Raghav has also authored multiple books with leading publishers. Raghav has a master's degree (gold medalist) in Information Technology from International Institute of Information Technology, Bangalore. Raghav loves reading and is a shutterbug capturing moments when he isn't busy solving problems.
Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.
