Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.
Key Features:
- Build your confidence with R and find out how to solve a huge range of data-related problems
- Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today
- Don't just learn - apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis
Book Description:
R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it...
This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R.
In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.
For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media.
This Learning Path has been curated from three Packt products:
- R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar
- Machine Learning with R - Second Edition By Brett Lantz
- Mastering Machine Learning with R By Cory Lesmeister
What You Will Learn:
- Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
- Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
- Solve interesting real-world problems using machine learning and R as the journey unfolds
- Write reusable code and build complete machine learning systems from the ground up
- Learn specialized machine learning techniques for text mining, social network data, big data, and more
- Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
- Evaluate and improve the performance of machine learning models
- Learn specialized machine learning techniques for text mining, social network data, big data, and more
Who this book is for:
Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science
Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.
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.
Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.
