R has been the gold standard in applied machine learning for a long time. Surveys show that it is the most popular platform used by professional data scientists. It is also preferred by the best data scientists in the world. In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, learn how to get started, practice and apply machine learning using the R platform.
14 step-by-step tutorial lessons. 3 end-to-end projects. 85 R scripts.
You Need R to Really Kick Ass at Applied Machine Learning …But You Don’t Want to Deep-Dive into Theory or Language Syntax Professional developers can pick-up R fast… As a developer, you know how to pick up a new programming language quickly. Once you know how to define a function, use some loops and look-up at the API documentation, you’re off.
You have no interest in spending days or weeks of your time learning the intricate syntax of yet another language – especially when that language looks like every other one you’ve ever used.
When you already know some machine learning, R is a super power… As someone who knows a little machine learning, you know that what matters in applied predictive modeling is working through problems systematically. Through careful trial and error you must discover the data transforms and algorithms that are best for your dataset.
You have no interest in yet another slow and plodding introduction to machine learning.
You really need to know how R maps onto the tasks of a machine learning project… What you really need is a clear and straight forward presentation of how to complete each step of an applied machine learning project using the best packages and functions on the R platform.
Introducing Machine Learning Mastery With R. In this new Ebook, Machine Learning Mastery With R will break down exactly what steps you need to do in a predictive modeling machine learning project and walk you through step-by-step exactly how to do it in R.
With the help of 3 larger end-to-end project tutorials and a reusable project template, you will tie all of the steps back together and confidently know how to complete your own machine learning projects. The true fact of the matter is this:
When Machine Learning in R is Done Right, It Makes Working Through Projects Shockingly… Fast and Fun! There’s a reason that R is the most popular platform for applied machine learning for professional data scientists. What do you think that reason is?
Why would someone choose to use a language where a strange arrow operator (<-) is used for assignment? Why would professionals put up with 20 ways to do each task, when other platforms offer just one? Why would data scientists invest so much time into reading the documentation for third-party R packages when other platforms have much better doco? Any ideas why?
Power.
R is a like a candy shop… for data scientists. For applied machine learning the R platform is like a candy shop with rows and rows of thousands of colorful sweets to try. There are packages and functions for every possible algorithm, statistical method and technique you have heard of (and hundreds you haven’t).
R is the power tool of power tools… for machine learning.
This book is a hands-on introduction to learning algorithms. It is for people who may know a little machine learning (or not) and who may have heard about TensorFlow, but found the documentation too daunting to approach.
Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming. Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science ...
Implement scikit-learn into every step of the data science pipeline. Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, sci ...
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
Данный материал НЕ НАРУШАЕТ авторские права никаких физических или юридических лиц. Если это не так - свяжитесь с администрацией сайта. Материал будет немедленно удален. Электронная версия этой публикации предоставляется только в ознакомительных целях. Для дальнейшего её использования Вам необходимо будет приобрести бумажный (электронный, аудио) вариант у правообладателей.
Название: 
Распечатать
