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雷达卡
2016 | English | PDF | ISBN: N/A | 215 Page
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.
224 Page PDF Ebook.
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
But R is also like a massive Tesla coil with huge bolts of electricity arching, bagging and popping above your head, and you’re at the controls. Academics are developing and releasing state-of-the-art machine learning algorithms as R packages all the time. With a few simple lines of code you can download these algorithms first, before any other platform, and run them on your data.
Use machine learning algorithms in the way that the people that thought them up intended. No waiting around for a sleepy development team to wake up, hear about the algorithm and eventually port it across. It’s ready for you to use, right there in your R interactive environment.
Machine Learning Mastery With R Is Designed for Fast Moving
Developers that Already Know a Little Machine Learning Like You…
So what is the missing gap here?
The gap is that you don’t know how to get started with R. You may have tried watching videos. You may have tried a tutorial or two. You may have even tried another book. Everyone has an idea on the parts, but now one is putting it all together…
You need a complete solution… lessons on the parts and end-to-end projects
To bridge the gap between a burning desire to use R for machine learning and actually delivering accurate predictions reliably on project after project you need to stop trying to work from bits and pieces. You need a complete solution.
You need to know what the professionals know. Without investing years of your life figuring it all out.
Everything You Need to Know to Work Through Predictive Modeling Projects in R
You Will Get:
14 Lessons on Machine Learning with R
3 Project Tutorials that Tie it All Together
This ebook was written around two themes designed to get you started and using machine learning with R effectively and quickly.
These two parts are Lessons and Projects:
Lessons: Learn how the sub-tasks of applied machine learning map onto the R and the best practice way of working through each task.
Projects: Tie together all of the knowledge from the lessons by working through case study predictive modeling problems.
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Machine Learning Mastery with R - Get Started, Build Accurate Models and Work Th.pdf
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