Advanced Machine Learning with R [Video]

More Information
Learn
  • Work with advanced techniques in machine learning with R
  • Explore advanced techniques such as hyper parameter tuning and deep learning
  • Work with Neural Networks (NNs) and explore, implement, and classify documents
  • Get to know hyper-parameter tuning by exploring and iterating through parameters
  • Understand unsupervised learning, clustering data, and visualizing
  • Know how to evaluate the performance of your models and put your model into use
  • Work with a variety of real-world algorithms that suit your problem
About

Machine learning is the 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. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more.

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.

Moving on, we discuss some of the details of putting a model into a production system so you can use it as a part of a larger application. Finally, we’ll offer some suggestions for those who wish to practice the concepts further.

Style and Approach

In a step-by-step manner, these videos will cover more advanced topics in Machine learning. A variety of practical, solid, real-world problem types will be used to illustrate these concepts.

Features
  • Dive into the advanced algorithms such as hyper-parameter tuning and deep learning, and putting your models into production
  • Practical, solid, real-world examples that will help you get acquainted with the various stages of machine learning using the R language
  • Explore important machine learning concepts such as neural network, hyper parameter, unsupervised learning
Course Length 1 hour 32 minutes
ISBN 9781788291491
Date Of Publication 30 Aug 2017

Authors

Tim Hoolihan

Tim Hoolihan currently 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.

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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.