Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. R language is widely used among statisticians and data miners to develop statistical software and perform data analysis. It provides a cutting-edge power you need to work with Machine Learning techniques.

This comprehensive 4-in-1 is a step-by-step real world guide on machine learning and deep learning that takes you through the core aspects for building powerful data science applications with the help of the R programming language. Apply R to simple predictive modeling with short and simple code. Dive into the advanced algorithms such as hyper-parameter tuning and DeepLearning, and putting your models into production!

By the end of this course, you'll explore the advanced topics in machine learning with R in a step by step manner with examples to build powerful predictive models in R!

Contents and Overview

This training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Getting Started with Machine Learning in R, covers learning Machine learning techniques in the popular statistical language R. The course will take you through some different types of ML. You’ll work with a classic dataset using Machine Learning. You will learn Linear and Logistic Regression algorithms and analyze the dataset. You’ll explore algorithms like Random Forest and Naive Bayes for working on your data in R. Analysis of the data set is demonstrated from end to end, with example R code you can use. Then you’ll have a chance to do it yourself on another data set.

By the end of the course you will learn how to gain insights from complex data and how to choose the correct algorithm for your specific needs.

The second course, Advanced Machine Learning with R, covers advanced techniques like hyper parameter tuning, deep learning in a step by step manner with examples. In this course, you’ll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you’ll learn all about neural networks through an example of DNA classification data. You’ll explore networks, implement them, and classify them. After that, you’ll see how to tune hyper-parameters using a data set of sonar data and you’ll get to know their properties. Next, you’ll understand unsupervised learning with an example of clustering politicians, where you’ll explore new patterns, understand unsupervised learning, and visualize and cluster the data.

The third course, R Machine Learning solutions, covers building powerful predictive models in R. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationship. You’ll then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction.

The fourth course, Applied Machine Learning and Deep Learning with R covers building powerful machine learning and deep learning applications with help of the R programming language and its various packages. In this course, you’ll examine in detail the R software, which is the most popular statistical programming language of recent years.

Explore different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, you’ll dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, you’ll learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR.

By the end of this course, you'll explore the advanced topics in machine learning with R in a step by step manner with examples to build powerful predictive models in R.

About the Authors

• Phil Rennertis a Principal Research Engineer in Information Science, in the overall business of extracting wisdom from information overload. He has a long track record of solving challenging technical problems, innovating new techniques where existing ones don't apply. He is extensively skilled in machine learning, natural language processing, and data mining.

  
  

• Tim Hoolihancurrently works at DialogTech, a marketing analytics company focused on conversations. He is the Senior Director of Data Science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group. In his job, he uses deep neural networks to help automate of a lot of conversation classification problems. In addition, he works on some side-projects researching other areas of Artificial Intelligence and Machine Learning. Personally, he enjoys working on practice problems on Kaggle .com as well. Outside Data Science, he is interested in mathematical computation in general; he is a lifelong math learner and really enjoys applying it wherever he can. Recently, he has been spending time in financial analysis, and game development. He also knows a variety of languages: R, Python, Ruby, PHP, C/C++, and so on. Previously, he worked in web application and mobile development.

  
  

• 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.

  
  

• Olgun is PhD candidate at Department of Statistics, Mimar Sinan University. He has been working on Deep Learning for his PhD thesis. Also working as Data Scientist.He is so familiar with Big Data technologies like Hadoop, Spark and able to use Hive, Impala. He is a big fan of R. Also he really loves to work with Shiny, SparkR. He has many academic papers and proceedings about applications of statistics on different disciplines. Mr. Olgun really loves statistic and loves to investigate new methods, share his experience with people.