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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Are you a Student, Teacher or Retiree?
Do you have any Questions?
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
Customer Questions (65)
Thanks for your interest.
Sorry, I do not support third-party resellers for my books (e.g. reselling in other bookstores).
My books are self-published and I think of my website as a small boutique, specialized for developers that are deeply interested in applied machine learning.
As such I prefer to keep control over the sales and marketing for my books.
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All of the books and bundles are Ebooks in PDF file format.
This is intentional and I put a lot of thought into the decision:
- The books are full of tutorials that must be completed on the computer.
- The books assume that you are working through the tutorials, not reading passively.
- The books are intended to be read on the computer screen, next to a code editor.
- The books are playbooks, they are not intended to be used as references texts and sit the shelf.
- The books are updated frequently, to keep pace with changes to the field and APIs.
I hope that explains my rationale.
If you really do want a hard copy, you can purchase the book or bundle and create a printed version for your own personal use. There is no digital rights management (DRM) on the PDF files to prevent you from printing them.
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I have a thick skin, so please be honest.
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Sorry, I no longer distribute evaluation copies of my books due to some past abuse of the privilege.
If you are a teacher or lecturer, I’m happy to offer you a student discount.
Contact me directly and I can organize a discount for you.
Sorry, I do not offer Kindle (mobi) or ePub versions of the books.
The books are only available in PDF file format.
This is by design and I put a lot of thought into it. My rationale is as follows:
- I use LaTeX to layout the text and code to give a professional look and I am afraid that EBook readers would mess this up.
- The increase in supported formats would create a maintenance headache that would take a large amount of time away from updating the books and working on new books.
- Most critically, reading on an e-reader or iPad is antithetical to the book-open-next-to-code-editor approach the PDF format was chosen to support.
My materials are playbooks intended to be open on the computer, next to a text editor and a command line.
They are not textbooks to be read away from the computer.
Sorry, all of my books are self-published and do not have ISBNs.
I offer a discount on my books to:
If you fall into one of these groups and would like a discount, please contact me and ask.
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I provide two copies of the table of contents for each book on the book’s page.
- A written summary that lists the tutorials/lessons in the book and their order.
- A screenshot of the table of contents taken from the PDF.
If you are having trouble finding the table of contents, search the page for the section titled “Table of Contents”.
I only support payment via PayPal or Credit Card.
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Contact me and let me know that you would like to upgrade and what books or bundles you have already purchased and which email address you used to make the purchases.
I will create a special offer code that you can use to get the price of books and bundles purchased so far deducted from the price of the super bundle.
I am happy for you to use parts of my material in the development of your own course material, such as lecture slides for an in person class or homework exercises.
I am not happy if you share my material for free or use it verbatim. This would be copyright infringement.
All code on my site and in my books was developed and provided for educational purposes only. I take no responsibility for the code, what it might do, or how you might use it.
If you use my material to teach, please reference the source, including:
- The Name of the author, e.g. “Jason Brownlee”.
- The Title of the tutorial or book.
- The Name of the website, e.g. “Machine Learning Mastery”.
- The URL of the tutorial or book.
- The Date you accessed or copied the code.
- Jason Brownlee, Machine Learning Algorithms in Python, Machine Learning Mastery, Available from https://machinelearningmastery.com/machine-learning-with-python/, accessed April 15th, 2018.
Also, if your work is public, contact me, I’d love to see it out of general interest.
Thanks for asking.
I prefer to keep complete control over my content for now.
My books are self-published and are only available from my website.
I don’t have exercises or assignments in my books.
I do have end-to-end projects in some of the books, but they are in a tutorial format where I lead you through each step.
The book chapters are written as self-contained tutorials with a specific learning outcome. You will learn how to do something at the end of the tutorial.
Some books have a section titled “Extensions” with ideas for how to modify the code in the tutorial in some advanced ways. They are like self-study exercises.
Sorry, I do not offer a certificate of completion for my books or my email courses.
Sorry, new books are not included in your super bundle.
I release new books every few months and develop a new super bundle at those times.
All existing customers will get early access to new books at a discount price.
Note, that you do get free updates to all of the books in your super bundle. This includes bug fixes, changes to APIs and even new chapters sometimes. I send out an email to customers for major book updates or you can contact me any time and ask for the latest version of a book.
I have books that do not require any skill in programming, for example:
Other books do have code examples in a given programming language.
You must know the basics of the programming language, such as how to install the environment and how to write simple programs. I do not teach programming, I teach machine learning for developers.
You do not need to be a good programmer.
That being said, I do offer tutorials on how to setup your environment efficiently and even crash courses on programming languages for developers that may not be familiar with the given language.
My books do not cover the theory or derivations of machine learning methods.
This is by design.
My books are focused on the practical concern of applied machine learning. Specifically, how algorithms work and how to use them effectively with modern open source tools.
If you are interested in the theory and derivations of equations, I recommend a machine learning textbook. Some good examples of machine learning textbooks that cover theory include:
I generally don’t run sales.
If I do have a special, such as around the launch of a new book, I only offer it to past customers and subscribers on my email list.
I do offer book bundles that offer a discount for a collection of related books.
I do offer a discount to students, teachers, and retirees. Contact me to find out about discounts.
Sorry, I don’t have videos.
I only have tutorial lessons and projects in text format.
This is by design. I used to have video content and I found the completion rate much lower.
I want you to put the material into practice. I have found that text-based tutorials are the best way of achieving this. With text-based tutorials you must read, implement and run the code.
With videos, you are passively watching and not required to take any action.
After reading and working through the tutorials you are far more likely to use what you have learned.
There are no physical books, therefore no shipping is required.
All books are EBooks that you can download immediately after you complete your purchase.
I support purchases from any country via PayPal or Credit Card.
The book “Long Short-Term Memory Networks with Python” is not focused on time series forecasting, instead, it is focused on the LSTM method for a suite of sequence prediction problems.
That being said, the examples can be adapted for time series forecasting.
There is a section on showing you how load multivariate inputs and there is a tutorial showing you how to handle multivariate inputs.
There is also a section cautioning that to-date results of LSTMs on autoregression problems have not been stellar.
The book “Master Machine Learning Algorithms” is for programmers and non-programmers alike. It teaches you how 10 top machine learning algorithms work, with worked examples in arithmetic, and spreadsheets, not code. The focus is on an understanding on how each model learns and makes predictions.
The book “Machine Learning Algorithms From Scratch” is for programmers that learn by writing code to understand. It provides step-by-step tutorials on how to implement top algorithms as well as how to load data, evaluate models and more. It has less on how the algorithms work, instead focusing exclusively on how to implement each in code.
The two books can support each other.
The books are a concentrated and more convenient version of what I put on the blog.
I design my books to be a combination of lessons and projects to teach you how to use a specific machine learning tool or library and then apply it to real predictive modeling problems.
The books get updated with bug fixes, updates for API changes and the addition of new chapters, and these updates are totally free.
I do put some of the book chapters on the blog as examples, but they are not tied to the surrounding chapters or the narrative that a book offers and do not offer the standalone code files.
With each book, you also get all of the source code files used in the book that you can use as recipes to jump-start your own predictive modeling problems.
My books are playbooks. Not textbooks.
They have no deep explanations of theory, just working examples that are laser-focused on the information that you need to know to bring machine learning to your project.
There is little math, no theory or derivations.
My readers really appreciate the top-down, rather than bottom-up approach used in my material. It is the one aspect I get the most feedback about.
My books are not for everyone, they are carefully designed for practitioners that need to get results, fast.
Ebooks can be purchased from my website directly.
- First, find the book or bundle that you wish to purchase, you can see the full catalog here:
- Click on the book or bundle that you would like to purchase to go to the book’s details page.
- Click the “Buy Now” button for the book or bundle to go to the shopping cart page.
- Fill in the shopping cart with your details and payment details, and click the “Place Order” button.
- After completing the purchase you will be emailed a link to download your book or bundle.
All prices are in US dollars (USD).
Books can be purchased with PayPal or Credit Card.
After you complete your purchase you will receive an email with a link to download your bundle.
The download will include the book or books and any bonus material.
There are no physical books, therefore no shipping is required.
All books are EBooks that you can download immediately after you complete your purchase.
I recommend reading one chapter per day.
Momentum is important.
Some readers finish a book in a weekend.
Most readers finish a book in a few weeks by working through it during nights and weekends.
You will get your book immediately.
After you complete and submit the payment form, you will be immediately redirected to a webpage with a link to download your purchase.
You will also immediately be sent an email with a link to download your purchase.
Generally, I would recommend starting with the book or topic that most interests you.
Nevertheless, one suggested order for reading the books is as follows:
- Linear Algebra for Machine Learning
- Statistical Methods for Machine Learning
- Master Machine Learning Algorithms
- Machine Learning Algorithms From Scratch
- Machine Learning Mastery With Weka
- Machine Learning Mastery With Python
- Machine Learning Mastery With R
- Time Series Forecasting With Python
- XGBoost With Python
- Deep Learning With Python
- Long Short-Term Memory Networks with Python
- Deep Learning for Natural Language Processing
I hope that helps.
Sorry, I do not have a license to purchase my books or bundles for libraries.
The books are for individual use only.
Multi-seat licenses create a bit of a maintenance nightmare for me, sorry. It takes time away from reading, writing and helping my readers.
If you have a big order, such as for a class of students or a large team, please contact me and we will work something out.
My best advice is to start with a book on a topic that you can use immediately.
Baring that, pick a topic that interests you the most.
If you are unsure, perhaps try working through some of the free tutorials to see what area that you gravitate towards.
Generally, I recommend focusing on the process of working through a predictive modeling problem end-to-end:
I have three books that show you how to do this, with three top open source platforms:
- Master Machine Learning With Weka (no programming)
- Master Machine Learning With R (caret)
- Master Machine Learning With Python (pandas and scikit-learn)
These are great places to start.
You can always circle back and pick-up a book on algorithms later to learn more about how specific methods work in greater detail.
Thanks for your interest.
You can see the full catalog of my books and bundles here:
Thanks for asking.
I try not to plan my books too far into the future. I try to write about the topics that I am asked about the most or topics where I see the most misunderstanding.
If you would like me to write more about a topic, I would love to know.
Contact me directly and let me know the topic and even the types of tutorials you would love for me to write.
Contact me and let me know the email address (or email addresses) that you think you used to make purchases.
I can look up what purchases you have made and resend purchase receipts to you so that you can redownload your books and bundles.
All prices are in US Dollars (USD).
All currency conversion is handled by PayPal for PayPal purchases, or by Stripe and your bank for credit card purchases.
It is possible that your link to download your purchase will expire after a few days.
This is a security precaution.
Please contact me and I will resend you purchase receipt with an updated download link.
The book “Deep Learning With Python” could be a prerequisite to”Long Short-Term Memory Networks with Python“. It teaches you how to get started with Keras and how to develop your first MLP, CNN and LSTM.
The book “Long Short-Term Memory Networks with Python” goes deep on LSTMs and teaches you how to prepare data, how to develop a suite of different LSTM architectures, parameter tuning, updating models and more.
The book “Long Short-Term Memory Networks With Python” focuses on how to implement different types of LSTM models.
The book “Deep Learning for Natural Language Processing” focuses on how to use a variety of different networks (including LSTMs) for text prediction problems.
The LSTM book can support the NLP book, but it is not a prerequisite.
Linux, MacOS, and Windows.
There are no code examples in “Master Machine Learning Algorithms“, therefore no programming language is used.
Algorithms are described and their working is summarized using basic arithmetic. The algorithm behavior is also demonstrated in excel spreadsheets, that are available with the book.
It is a great book for learning how algorithms work, without getting side-tracked with theory or programming syntax.
If you are interested in learning about machine learning algorithms by coding them from scratch (using the Python programming language), I would recommend a different book:
I write the content for the books (words and code) using a text editor, specifically sublime.
I typeset the books and create a PDF using LaTeX.
All of the books, except one, have been tested and work with Python 3 (e.g. 3.5 or 3.6).
Most of the books have also been tested and work with Python 2.7.
There is one book that required Python 2.7, that is:
This book will be updated for Python 3 soon.
Where possible, I recommend using the latest version of Python 3.
After you fill in the order form and submit it, two things will happen:
- You will be redirected to a webpage where you can download your purchase.
- You will be sent an email (to the email address used in the order form) with a link to download your purchase.
The redirect in the browser and the email will happen immediately after you complete the purchase.
You can download your purchase from either the webpage or the email.
If you cannot find the email, perhaps check other email folders, such as the “spam” folder?
If you have any concerns, contact me and I can resend your purchase receipt email with the download link.
I do test my tutorials and projects on the blog first. It’s like the early access to ideas, and many of them do not make it to my training.
Much of the material in the books appeared in some form on my blog first and is later refined, improved and repackaged into a chapter format. I find this helps greatly with quality and bug fixing.
The books provide a more convenient packaging of the material, including source code, datasets and PDF format. They also include updates for new APIs, new chapters, bug and typo fixing, and direct access to me for all the support and help I can provide.
I believe my books offer thousands of dollars of education for tens of dollars each.
They are months if not years of experience distilled into a few hundred pages of carefully crafted and well-tested tutorials.
I think they are a bargain for professional developers looking to rapidly build skills in applied machine learning or use machine learning on a project.
Also, what are skills in machine learning worth to you? to your next project? and you’re current or next employer?
Nevertheless, the price of my books may appear expensive if you are a student or if you are not used to the high salaries for developers in North America, Australia, UK and similar parts of the world. For that, I am sorry.
I do offer discounts to students, teachers and retirees.
Please contact me to find out more.
I offer a ton of free content on my blog, you can get started with my best free material here:
About my Books
My books are playbooks.
They are intended for developers who want to know how to use a specific library to actually solve problems and deliver value at work.
- My books guide you only through the elements you need to know in order to get results.
- My books are in PDF format and come with code and datasets, specifically designed for you to read and work-through on your computer.
- My books give you direct access to me via email (what other books offer that?)
- My books are a tiny business expense for a professional developer that can be charged to the company and is tax deductible in most regions.
Very few training materials on machine learning are focused on how to get results.
The vast majority are about repeating the same math and theory and ignore the one thing you really care about: how to use the methods on a project.
Comparison to Other Options
Let me provide some context for you on the pricing of the books:
There are free videos on youtube and tutorials on blogs.
- Great, I encourage you to use them, including my own free tutorials.
There are very cheap video courses that teach you one or two tricks with an API.
- My books teach you how to use a library to work through a project end-to-end and deliver value, not just a few tricks
A textbook on machine learning can cost $50 to $100.
- All of my books are cheaper than the average machine learning textbook, and I expect you may be more productive, sooner.
A bootcamp or other in-person training can cost $1000+ dollars and last for days to weeks.
- A bundle of all of my books is far cheaper than this, they allow you to work at your own pace, and the bundle covers more content than the average bootcamp.
Sorry, my books are not available on websites like Amazon.com.
I carefully decided to not put my books on Amazon for a number of reasons:
- Amazon takes 65% of the sale price of self-published books, which would put me out of business.
- Amazon offers very little control over the sales page and shopping cart experience.
- Amazon does not allow me to contact my customers via email and offer direct support and updates.
- Amazon does not allow me to deliver my book to customers as a PDF, the preferred format for my customers to read on the screen.
I hope that helps you understand my rationale.
I am sorry to hear that you’re having difficulty purchasing a book or bundle.
I use Stripe and PayPal services to support secure and encrypted payment processing on my website.
Some common problems when customers have a problem include:
- Perhaps you can double check that your details are correct, just in case of a typo?
- Perhaps you could try a different payment method, such as PayPal or Credit Card?
- Perhaps you’re able to talk to your bank, just in case they blocked the transaction?
I often see customers trying to purchase with a domestic credit card that does not allow international purchases. This is easy to overcome by talking to your bank.
If you’re still having difficulty, please contact me and I can help investigate further.
I give away a lot of content for free. Most of it in fact.
It is important to me to help students and practitioners that are not well off, hence the enormous amount of free content that I provide.
You can access the free content:
I have thought very hard about this and I sell machine learning Ebooks for a few important reasons:
- I use the revenue to support the site and all the non-paying customers.
- I use the revenue to support my family so that I can continue to create content.
- Practitioners that pay for tutorials are far more likely to work through them and learn something.
- I target my books towards working professionals that are more likely to afford the materials.
All updates to the book or books in your purchase are free.
Books are usually updated once every few months to fix bugs, typos and keep abreast of API changes.
Contact me anytime and check if there have been updates. Let me know what version of the book you have (version is listed on the copyright page).
Please contact me anytime with questions about machine learning or the books.
One question at a time please.
Also, each book has a final chapter on getting more help and further reading and points to resources that you can use to get more help.
Yes, the books can help you get a job, but indirectly.
Getting a job is up to you.
It is a matching problem between an organization looking for someone to fill a role and you with your skills and background.
That being said, there are companies that are more interested in the value that you can provide to the business than the degrees that you have. Often, these are smaller companies and start-ups.
You can focus on providing value with machine learning by learning and getting very good at working through predictive modeling problems end-to-end. You can show this skill by developing a machine learning portfolio of completed projects.
My books are specifically designed to help you toward these ends. They teach you exactly how to use open source tools and libraries to get results in a predictive modeling project.
Do you have another question?