Advanced Machine Learning with R: Tackle data analytics and machine learning challenges and build complex applications with R 3.5

Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages

Key Features:

Gain expertise in machine learning, deep learning and other techniquesBuild intelligent end-to-end projects for finance, social media, and a variety of domainsImplement multi-class classification, regression, and clustering

Book Description:

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.

This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You’ll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you how some of these black-box models can be diagnosed and understood.

By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.

This Learning Path includes content from the following Packt products:

R Machine Learning Projects by Dr. Sunil Kumar ChinnamgariMastering Machine Learning with R - Third Edition by Cory Lesmeister

What you will learn:

Develop a joke recommendation engine to recommend jokes that match users’ tastesBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networksMake predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationProduce simple and effective data visualizations for improved insightsUse NLP to extract insights for textImplement tree-based classifiers including random forest and boosted tree

Who this book is for:

If you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.

Cory Lesmeister has over fourteen years of quantitative experience and is currently a senior data scientist for the advanced analytics team at Cummins, Inc. in Columbus, Indiana. He has spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. He also has several years of experience in the insurance and banking industries, both as a consultant and as a manager of marketing analytics. A former US Army active duty and reserve officer, Cory was stationed in Baghdad, Iraq, in 2009 serving as the strategic advisor to the 29,000-person Iraqi Oil Police, succeeding where others failed by acquiring and delivering promised equipment to help the country secure and protect its oil infrastructure. He has a BBA in aviation administration from the University of North Dakota and a commercial helicopter license. Dr. Sunil Kumar Chinnamgari has a Ph.D. in computer science and he specializes in machine learning and natural language processing. He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of a lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meetups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.

© 2019 Packt Publishing (Libro electrónico): 9781838645748

Fecha de lanzamiento

Libro electrónico: 20 de mayo de 2019