R: Complete Machine Learning Solutions

More Information
Learn
  • Create and inspect the transaction dataset and perform association analysis with the Apriori algorithm
  • Predict possible churn users with the classification approach
  • Implement the clustering method to segment customer data
  • Compress images with the dimension reduction method
  • Build a product recommendation system
About

This course will take you from the very basics of R to creating insightful machine learning models with R.

We start off with basic R operations, reading data into R, manipulating data, forming simple statistics for visualizing data. We will then walk through the processes of transforming, analyzing, and visualizing the RMS Titanic data. You will also learn how to perform descriptive statistics.

This course will teach you to use regression models. We will then see how to fit data in tree-based classifier, Naive Bayes classifier, and so on.

We then move on to introducing powerful classification networks, neural networks, and support vector machines. During this journey, we will introduce the power of ensemble learners to produce better classification and regression results.

We will see how to apply the clustering technique to segment customers and further compare differences between each clustering method.

We will discover associated terms and underline frequent patterns from transaction data.

We will go through the process of compressing and restoring images, using the dimension reduction approach and R Hadoop, starting from setting up the environment to actual big data processing and machine learning on big data.

By the end of this course, we will build our own project in the e-commerce domain. Then, we will tackle the problem of personalization.

By taking this course, you will gain a detailed and practical knowledge of R and machine learning concepts to build complex machine learning models.

Style and Approach:

This course is full of hands-on recipes for machine learning with R. Each topic is fully explained, followed by step-by-step and practical examples.

This course is a blend of text, videos, code examples, and assessments, all packaged up keeping your journey in mind. The curator of this course has combined some of the best that Packt has to offer in one complete package. It includes content from the following Packt products:

Note: This interactive EPUB adheres to the latest specification, and requires that your reader supports video and interactive content. We recommend using Readium with the latest stable version of Google Chrome, or iBooks for OSX.

Features
  • Visualize patterns and associations using a range of graphs and find frequent itemsets using the Eclat algorithm
  • Compare the differences between different regression methods to discover how they solve problems
  • Incorporate R and Hadoop to solve machine learning problems on big data
  • Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science
Course Length 8 hours
ISBN 9781787280991
Date Of Publication 28 Feb 2017

Authors

Dipankar Sarkar

Dipankar Sarkar is a web and mobile entrepreneur. He has a Bachelor’s degree in Computer Science and Engineering from Indian Institute of Technology, Delhi. He is a firm believer in the Open source movement and has participated in the Google Summer of Code, 2005-06 and 2006-07. He has conducted technical workshops for Windows mobile and Python at various technical meetups. He recently took part in the Startup Leadership Program, Delhi Chapter. He has worked with Slideshare LLC, one of the world’s largest online presentation hosting and sharing services as an early engineering employee. He has since then worked with Mpower Mobile LLC, a mobile payment startup and Clickable LLC, a leading search engine marketing startup. He was a co-founder at Kwippy, which was one of the top micro-blogging sites. He is currently working in the social TV space and has co-founded Jaja. He has previously authored “Nginx web server implementation cookbook” and this is his second book on Nginx. This book “Mastering Nginx” is a more structured approach to how one can learn and master Nginx, with practical examples and strategies.

Yu-Wei, Chiu (David Chiu)

Yu-Wei, Chiu (David Chiu) is the founder of LargitData Company. He has previously worked for Trend Micro as a software engineer, with the responsibility of building up big data platforms for business intelligence and customer relationship management systems. In addition to being a startup entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques to data analysis. Yu-Wei is also a professional lecturer, and has delivered talks on Python, R, Hadoop, and tech talks at a variety of conferences.

In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, a book compiled for Packt Publishing.

Raghav Bali

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.