Usuelli - R machine learning essentials gain quick access to the machine learning concepts and practical applications using the R development environment

Michele Usuelli is a data scientist living in London. He has a background of and is passionate about statistics and computer science, and as part of his work, he has explored different software and tools for data analysis and machine learning, focusing on R.

Always wanting to share what he learned from his projects, Michele has written some articles on R-bloggers. R connected to Hadoop and some applications of R tools are the topics covered here.

Michele is passionate about cutting-edge technologies and fast-paced growing environments. Since the very beginning, his work took place in start-up environments. He started his career in one of the most innovative big data start-ups in Milan and worked for a top publishing company in the pricing and analytics division. Currently, he works for a leading R-based company.

I wouldn't have been able to write this book without my personal and professional growth in the last few years, and so I would like to thank all the people I worked with, and of course, my family and friends. I have worked with great people and learned a lot from them.

Eric Hare is a graduate from the Department of Statistics at Iowa State University. He graduated from the University of Washington in 2012 with a Bachelor's degree in Statistics and in Computer Engineering. He does research in statistical graphics, statistical computing, and data manipulation. He is currently working on a web application to analyze the statistical properties of Peptide Libraries.

Jithin S L completed his B.Tech in Information Technology from Loyola Institute of Technology and Science. He started his career in the field of analytics and then moved to various verticals of big data technology. He has worked with reputed organizations such as Thomson Reuters, IBM Corporation, and Flytxt, under different roles. He has worked in the banking, energy, healthcare, and telecom domains, and has handled global projects on big data technology.

He has submitted many research papers on technology and business at national and international conferences.

In Albert Einstein's words, learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning .

I surrender myself to God Almighty who helped me throughout these days to review this book in an effective way.

I dedicate my work on this book to my dad, Mr. N. Subbian Asari (late), my lovable mom, Mrs. M. Lekshmi, and my sweet sister, Ms. S.L Jishma, for coordinating and encouraging me to produce this book.

Last but not least, I would like to thank all my friends.

Jia Liu obtained her PhD degree in Statistics from Iowa State University. Her research interests are in mixed-effects model, Bayesian method, Bootstrap method, reliability, design of experiments, machine learning, and data mining. She has 3 years of working experience in the pharmaceutical industry.

Samir Madhavan has extensive experience in big data and machine learning. He has worked for the ubiquitous Unique Identification Project, Aadhar, where he was part of the team that helped in developing its fraud module. He was also part of the initial team when Flutura Decision Sciences and Analytics started off. He has created various analytical products, which are being used by the e-commerce, retail, and M2M industries.

Raghavendra Prasad Narayan has completed his Bachelor of Engineering in Electronics and Communication from VTU, Belgaum, and completed his Master's degree majoring in Knowledge Engineering from the National University of Singapore. Since 2009, his area of work has been machine learning and natural language processing (NLP). He has worked on the different problems of NLP, and to solve these problems, he has used the ML algorithms extensively (such as classification, clustering algorithms, feature selection/reduction methods, and graphical models). Other than NLP problems, he has also worked on social network analysis, stock market forecasting, yield predictions, and market mix modeling problems.

Currently, he is working at Meltwater Group in the Data Enrichment team as an NLP engineer.

Owen S. Vallis is currently a professor of Music Technology for the Music Technology: Interaction, Intelligence, and Design program at the California Institute of Arts. Owen is a musician, artist, and scientist interested in performance, sound, and technology. As a cofounder of Flipmu and The Noise Index, he explores a diverse range of projects including big data research and sound art installations. He produces, composes, and designs audio processors, and creates new hardware interfaces for musical performance.

Owen received his PhD in 2013 from the New Zealand School of Music, Victoria University, Wellington, and explored contemporary approaches to live computer music. During his graduate research, Owen focused on developing new musical interfaces, interactive musical agents, and large networked music ensembles. Owen graduated as a Bachelor of Arts in Music Technology from the California Institute of the Arts in 2008.

Having lived in Toronto, Canada; Wellington, New Zealand; Tokyo, Japan; San Francisco; Nashville; and Los Angeles; Owen has been able to develop a broad and interesting cross section of musical ideologies and aesthetics. Over the past 10 years, he has worked as a research scientist for Twitter; developed multitouch interfaces for Nokia research labs; worked for the leading ribbon microphone manufacturer Royer Labs; has had musical production featured in major motion films; built a recording facility; and produced, engineered, and mixed records in Tokyo, Nashville, and Los Angeles. Owen's work has been featured in Wired, Future Music, Pitchfork, XLR8R, Processing.org, and various computer arts magazines, and is shown at events such as NASA's Yuri's Night, Google I/O, and the New York Cutlog art festival.