Machine Learning Mastery With R
Discover The Most Popular Machine Learning Platform
With Step-By-Step Tutorials And End-To-End Projects
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.
Bring Machine Learning With R To Your Projects
Click to jump straight to the packages.
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?
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.
Here is an overview of the step-by-step lessons you will complete:
- Lesson 1: How to Install and Start R.
- Lesson 2: How to Navigate The R Programming Language.
- Lesson 3: How to Load Standard Machine Learning Datasets.
- Lesson 4: How to Load Your Own Custom Machine Learning Data.
- Lesson 5: How to Understand Data With Descriptive Statistics.
- Lesson 6: How to Understand Data Using Data Visualization.
- Lesson 7: How to Pre-Process Data Ready for Modeling.
- Lesson 8: How to Estimate Model Skill Using Resampling Methods.
- Lesson 9: How To Use Different Algorithm Evaluation Metrics.
- Lesson 10: How to Spot-Check Machine Learning Algorithms.
- Lesson 11: How to Compare and Choose the Best Models.
- Lesson 12: How to Improve Results with Algorithm Parameter Tuning.
- Lesson 13: How to Improve Results with Ensemble Methods.
- Lesson 14: How to Finalize Model Ready To Make Predictions on New Data.
Each lesson was designed to be completed in about 30 minutes by the average developer.
Here is an overview of the 3 end-to-end projects you will complete:
- Project 1: Multiclass Classification of Flower Species.
- Project 2: Regression of Boston House Prices.
- Project 3: Classification of Breast Cancer.
- Bonuses: 1) Project Templte and 2) More Project Ideas.
Each project was designed to be completed in about 60 minutes by the average developer.
Here’s Everything You’ll Get…
in Machine Learning Mastery With R
A digital download that contains everything you need, including:
- Clear descriptions that help you to understand the principles that underlie the platform.
- Step-by-step R tutorials to show you exactly how to apply each technique and algorithm.
- End-to-end R projects that show you exactly how to tie the pieces together and get a result.
- R source code recipes for every example in the book so that you can run the tutorial and project code in seconds.
- Digital Ebook in PDF format so that you can have the book open side-by-side with the code and see exactly how each example works.
Resources you need to go deeper, when you need to, including:
- Top machine learning textbooks to deepen your foundation of R and machine learning, if you crave more.
- The best places online where you can ask your challenging questions and actually get a response.
Tutorials for getting started and data preparation, including:
- The installation of the R platform and the 3 ways you can run an R script.
- The R language syntax crash course and how to install the packages you need.
- The standard machine learning datasets and why they are so important when practicing in R.
- The loading of data from CSV or URL and the important foundation this lays for loading your own data.
- The calculation of descriptive statistics and the 8 techniques you need to use to understand your data.
- The visualization of your data and the 9 plots you need to get insights into your predictive modeling problem.
- The data preparation process and the 8 methods you must consider before modeling your problem.
Lessons on applied machine learning with the R platform, including:
- The importance of estimating model performance on unseen data and 5 techniques you need to do so.
- The metrics used to measure model performance and which ones to use for regression and classification problems.
- The necessity of not assuming a solution, the spot checking method and 8 linear and nonlinear algorithm recipes you can use immediately.
- The comparison and selection of trained models and 8 techniques to help you choose.
- The tuning of machine learning algorithm hyperparameters and 3 different methods to apply.
- The performance benefits of combining the predictions from many models and the 3 ensemble algorithms you must consider.
- The finalization of a trained model to save it to file and later load it to make new predictions on unseen data.
Projects that tie together the lessons into end-to-end sequence to deliver a result, including:
- – The project template that you can use to jump-start any predictive modeling problem in R.
- The first machine learning project in R for multi-class classification that provides a gentle guide as to how the lessons tie together.
- The regression project to predict house prices that shows the improvements of data transforms, tuning and ensemble methods.
- The binary classification problem that predicts breast cancer showing the judicious use of algorithm tuning and ensemble algorithms.
- The selection of extra predictive modeling projects that can be used for ongoing practice to build up a portfolio of work.
What More Do You Need?
Take a Sneak Peek Inside the Ebook
BONUS: Machine Learning With R Code Recipies
…you also get 85 fully working R scripts
Each recipe presented in the book is standalone, meaning that you can copy and paste it into your project and use it immediately.
- You get one R script (.R) for each example provided in the book.
- You get my own person R library of R scripts.
Your R Machine Learning Code Recipe Library covers the following topics:
- Loading Data
- Data Summarization
- Data Visualization
- Data Cleaning
- Feature Selection
- Machine Learning Algorithms
- Ensemble Algorithms
- Resampling Methods
- Evaluation Metrics
- Model Selection
- Hyperparameter Tuning
- Making Predictions
- Saving Your Model
- And More….
This means that you can follow along and compare your answers to a known working implementation of each algorithm in the provided R files.
This helps a lot to speed up your progress when working through the details of a specific task.
About The Author
Hi, I'm Jason Brownlee.
I live in Australia with my wife and son and love to write and code.
I have a computer science background as well as a Masters and Ph.D. degree in Artificial Intelligence.
I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones. (yes I have written tons of code that runs operationally)
I get a lot of satisfaction helping developers get started and get really good at machine learning.
I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
I'm here to help if you ever have any questions. I want you to be awesome at machine learning.
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Have more Questions?
Are you a Student?
Want it for the Team?
What Are Skills in Machine Learning Worth?
Your boss asks you:
Hey, can you build a predictive model for this?
Imagine you had the skills and confidence to say:
...and follow through.
I have been there. It feels great!
How much is that worth to you?
The industry is demanding skills in machine learning.
The market wants people that can deliver results, not write academic papers.
Business knows what these skills are worth and are paying sky-high starting salaries.
A Data Scientists Salary Begins at:
$100,000 to $150,000.
A Machine Learning Engineers Salary is Even Higher.
What Are Your Alternatives?
You made it this far.
You're ready to take action.
But, what are your alternatives? What options are there?
(1) A Theoretical Textbook for $100+
...it's boring, math-heavy and you'll probably never finish it.
(2) An On-site Boot Camp for $10,000+
...it's full of young kids, you must travel and it can take months.
(3) A Higher Degree for $100,000+
...it's expensive, takes years, and you'll be an academic.
For the Hands-On Skills You Get...
And the Speed of Results You See...
And the Low Price You Pay...
Machine Learning Mastery Ebooks are
And they work. That's why I offer the money-back guarantee.
You're A Professional
The field moves quickly,
...how long can you wait?
You think you have all the time in the world, but...
- New methods are devised and algorithms change.
- New books get released and prices increase.
- New graduates come along and jobs get filled.
Right Now is the Best Time to make your start.
Bottom-up is Slow and Frustrating,
...don't you want a faster way?
Can you really go on another day, week or month...
- Scraping ideas and code from incomplete posts.
- Skimming theory and insight from short videos.
- Parsing Greek letters from academic textbooks.
Targeted Training is your Shortest Path to a result.
Professionals Use Training To Stay On Top Of Their Field
Get The Training You Need!
You don't want to fall behind or miss the opportunity.
Frequently Asked Questions
What programming language is used? All examples use the R programming language version 3.2 or higher.
Do I need to be a good programmer? Not at all. This Ebook requires that you have a programmers mindset of thinking in procedures and learning by doing. You do not need to be an excellent programmer to read and learn about machine learning algorithms.
How much math do I need to know? No background in statistics, probability or linear algebra is required. We do not derive any equations.
How many pages it the Ebook? The Ebook is 224 pages.
How many example R scripts are included? My personal library of 180 R recipes are included.
Is there a hard copy physical book? Not at this stage. Ebook only.
Will I get updates? Yes. You will be notified about updates to the book and code that you can download for free.
How long will the Ebook take to complete? I recommend reading one chapter per day. With 14 lessons and 3 projects and moving fast through the intro and conclusions, you can finish in 3 weeks. On the other hand, if you are keen you could work through all of the material in a weekend.
What if I need help? The final chapter is titled “Getting More Help” and points to resources that you can use to get more help with machine learning in R.
How much machine learning do I need to know? Only a little. You will be lead step-by-step through the process of working a machine learning project from end-to-end. One lesson on each step and 3 example projects that tie it all together.
Are there any additional downloads? Yes. In addition to the download for the Ebook itself, you will have access to my personal library of R recipes covering each step of the applied machine learning process.
Is there any digital rights management (DRM)? No, there is no DRM.