Calculus for Machine Learning
Understanding the Language of Mathematics
Calculus seems to be obscure, but it is everywhere. In machine learning, while we rarely write code on differentiation or integration, the algorithms we use have theoretical roots in calculus.
If you ever wondered how to understand the calculus part when you listen to people explaining the theory behind a machine learning algorithm, this new Ebook, in the friendly Machine Learning Mastery style that you’re used to, is all you need.
Using clear explanations and step-by-step tutorial lessons, you will understand the concept of calculus, how it is relates to machine learning, what it can help us on, and much more.
About this Ebook:
- Read on all devices: PDF format Ebook, no DRM
- Tons of tutorials: 34 step-by-step lessons, 283 pages
- Foundations: intuitions behind differentiation and integration, why it is useful, and more
- Real-world projects: reinventing the backpropagation algorithm through steps and making a neural network, finding the support vector in support vector machines
- Working code: 43 Python (.py) code files included
Clear and Just Enough Calculus.
Designed for Developers. Nothing Hidden.
We are not mathematicians!
Calculus is a sub-field of mathematics concerned with very small values. It can tell us what happens when we take a small step in one direction or another. It is a perfect tool to describe the progress of how machines learn.
As a machine learning practitioner, you must have an understanding of calculus.
It’s a vast field of study that has impacted other fields, such as statistics, engineering, and physics. Thankfully, we don’t need to know the breadth and depth of calculus in order to improve our understanding and application of machine learning.
Frustrated with the Math?
Have you ever been frustrated reading the description of a machine learning technique?
You’re reading along, things are going well, and then you hit an equation. You are stopped in your tracks with questions like:
- … what do the terms mean?
- … why are there some funny symbols that I cannot understand?
- … what does this Greek letter mean?
Unless you have a basic knowledge of calculus, you will not be able to read and understand even the most basic equations.
Why Is Calculus Important to Machine Learning?
So, why is calculus used so much to describe machine learning algorithms?
Machine learning is based on finding the optimal way to describe data, so we can use that same way to predict data we haven’t seen. To consider what is optimal and what is not, calculus is the tool we use.
Calculus is the mathematics of change.
It provides useful tools to check how things will change caused by perturbation in something else. It also help us understand the cause and effect of an algorithm.
Calculus is not obscure. It is the language for modeling behaviors. Without calculus, we would not be able to fully understand techniques such as:
- Backpropagation in neural networks
- Regression using optimal least square
- Expectation maximization in fitting probability models
The Wrong Ways to Learn Calculus
Now you know the importance of calculus to machine learning. But when learning, it is easy to take the wrong route:
1. Learn calculus too early
It sounds cool to know calculus. And indeed, it is not difficult to learn the basics.
However, you can skip learning calculus and still do well in machine learning. Not all projects require you to know calculus well. If you’re a practitioner, there are more important things than calculus to focus on. Getting a solid background in linear algebra, calculus, statistics, probability, and then moving on to machine learning is a bottom-up path and takes too long to see results.
It would be more exciting if you could get results first by trying out some machine learning models. After getting some experience in implementing a machine learning algorithm, you can go back to investigate the theoretical background and gain some insight into why the machine learning procedure works. This is the top-down approach. We keep calculus at the second or third step in your learning journey.
2. Learn too much
You learn calculus in school from a mathematics class. The approach is probably aimed at training a mathematician and making sure you can work on harder calculus problems on your own. But for a practitioner, you probably do not need to do a lot of exercises and become very familiar with all the tricks and techniques in dealing with calculus problems.
Instead, while focusing on machine learning applications, you should know what calculus tells us about the model. Knowing the intuition and the connection is more important than working out the equations.
Sometimes, we may lose track if we go too deep. We may go into solving differential equations and forget about the importance of multivariate calculus. Calculus is a broad field of study with a lot of theorems and derivations. But not everything is useful to your machine learning model. You only need to focus on a specific subset. Afterwards, of course, you can always go deeper if you want to.
A Better Way into Calculus
Learning from a traditional textbook and following a bottom-up approach is hard, especially if you’re time-constrained. Many people dive into calculus, following the online course videos designed for undergraduate students, and then give up.
Learning calculus can be fun and easy if you approach it the right way. You should see calculus as just another set of tools we can harness on our journey toward machine learning mastery.
3 Areas of Calculus to Focus On
You don’t need to know all of calculus
The three key areas of calculus that I recommend you focus on are:
Differentiation is the key thing in calculus. It describes the rate of change or the cause and effect of tuning parameters.
Algorithms described in books, papers, and on websites usually involves differentiations. You need to know what it means and its notations.
2. Vector calculus
This brings differentiation to a higher dimension. Usually, machine learning algorithms involve more than one parameter. Sometimes, there are multiple outputs from a single model. We typically describe such machine learning algorithms with vector functions and use multivariate calculus to describe their behavior.
You need to know how to do differentiation on a vector function and how to present it as a vector of a matrix. This is the tool behind backpropagation algorithms in neural network training.
3. Calculus as a Tool for Optimization
One important use of calculus, differentiation in particular, is to find the optimum value of a function.
While we may find the maximum or minimum of a function algorithmically, using calculus can give you the answer in one shot. More importantly, it is also the tool to find the maximum or minimum under constraints.
One example is the support vector machine classifier. This is essentially finding the maximum separation of two classes. You need to understand calculus in order to know how support vector machine gives its solution.
Introducing Our New Ebook:”Calculus for Machine Learning“
Welcome to “Calculus for Machine Learning”
This book is designed to teach machine learning practitioners, like you, the basics of calculus step-by-step with concrete examples and occasionally with executable code in Python.
This book was carefully designed to help you bring the knowledge of a wide variety of the tools and techniques of calculus to your next project.
The tutorials were designed to teach you these techniques the fastest and most effective way that I know how: to learn by doing. We give you executable code that you can run to develop the intuitions required and that you can copy-and-paste into your project to immediately get a result.
Calculus is important to machine learning, and I believe that if it is taught at the right level for practitioners, it can be a fascinating, fun, directly applicable, and immeasurably useful toolbox of techniques.
I hope that you agree.
Click to jump straight to the packages.
Who Is This Book For?
…so, is this book right for YOU?
This book is for developers that may know some applied machine learning.
Maybe you know how to work through a predictive modeling problem end-to-end, or at least most of the main steps, with popular tools.
The lessons in this book do assume a few things about you, such as:
- You may know your way around basic Python for programming.
- You may know some basic algebra
- You want to learn calculus to deepen your understanding and application of machine learning.
This guide was written in the top-down and results-first machine learning style that you’re used to from Machine Learning Mastery.
What if I Am New to Machine Learning?
This book does not assume you have a background in machine learning.
That being said, we do recommend that you learn how to work through a predictive modeling problem first. It will give you the context for what we cover. Otherwise the topic will feel too abstract.
What if I Am Just a Developer?
Perfect. This book is written for you!
What if My Math Is Really Poor?
Maybe you learned algebra and calculus a long time ago back in school.
Maybe you have never covered calculus before.
Perfect. This book is for you. I assume you know some basic arithmetic, and even then, I give you a refresher.
What if I Am Not a Python Programmer?
You can handle this book if you are a programmer in another language, even if you are not experienced in Python.
Everything is demonstrated with a small code example that you can run directly.
All code is provided for you to play with, modify, and learn from.
The calculus covered in this book is mainly about the concept and rationale. We showed you how to verify the result with Python. If you prefer, an online computer algebra system can do the same as our Python programs.
The book even has an appendix to show you how to set up Python on your workstation and use a computer algebra system.
What if I Am Working Through a Calculus Course at a University?
This book is not a substitute for an undergraduate course in calculus or a textbook for such a course, although it is a great complement to such materials.
About Your Outcomes
…so what will YOU know after reading this book?
After reading and working through this book, you will know:
- What is calculus and why it is relevant and important to machine learning.
- How to do differentiation and verify the result of differentiation with a computer.
- What is a multivariate function and how to differentiate it.
- Jacobian, Hessian, and Laplacian matrix, and their application in machine learning algorithms.
- Chain rule and how we use it in the backpropagation algorithm.
- How calculus can help functional optimization with constraints.
- Applying calculus techniques to build neural network training algorithms as well as support vector machine classifiers.
This new basic understanding of calculus will impact your practice of machine learning.
After reading this book, you will be able to:
- Read the calculus equations in machine learning papers.
- Implement the calculus descriptions of machine learning algorithms.
- Describe your machine learning models using the notation and operations of calculus.
What Exactly Is in This Book?
This book was designed to be a crash course in calculus for machine learning practitioners. Ideally, those with a background as a developer.
This book was designed around major operations and techniques in calculus that are directly relevant to machine learning algorithms.
There are a lot of things you could learn about calculus, from theory to abstract concepts. Our goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials.
We designed the tutorials to focus on how to get things done with calculus. They give you the tools to both rapidly understand and apply each technique or operation.
Each tutorial is designed to take you about one hour to read through and complete, excluding the extensions and further reading.
You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle.
I would recommend picking a schedule and sticking to it.
The tutorials are divided into six parts:
- Part 1: Foundation. Discover a gentle introduction to the field of calculus and its relationship with the field of machine learning.
- Part 2: Limits and Differentiation. Discover calculus from the first principle. Learn how to look at a mathematical function and find out its rate of change.
- Part 3: Multivariate Calculus. Discover the key to handling differentiation in more complex functions. This will include the chain rule and Jacobian matrix that are very useful in solving optimization problems, including the case of backpropagation algorithm.
- Part 4: Mathematical Programming. Discover the way to find an exact solution to function optimization problems. This is the basis of many machine learning algorithms, including linear regression and support vector machines.
- Part 5: Approximation. Another application of calculus is to rewrite the problem in a simpler form. In this part, we will see how we can use calculus to find an approximation of functions that is easier to handle.
- Part 6: Calculus in Machine Learning. We go through the projects step by step to build a neural network as well as a support vector machine classifier, using the calculus techniques that we learned.
Below is an overview of the 34 step-by-step tutorial lessons you will work through:
Each lesson was designed to be completed in about 30 to 60 minutes by the average developer.
- Lesson 01: What Is Calculus?
- Lesson 02: Rate of Change
- Lesson 03: Why It Works?
- Lesson 04: A Brief Tour of Calculus Prerequisites
Limits and Differential Calculus
- Lesson 05: Limits and Continuity
- Lesson 06: Evaluating Limits
- Lesson 07: Function Derivatives
- Lesson 08: Continuous Functions
- Lesson 09: Derivatives of Powers and Polynomials
- Lesson 10: Derivative of the Sine and Cosine
- Lesson 11: The Power, Product, and Quotient Rules
- Lesson 12: Indeterminate Forms and L’Hopital’s Rule
- Lesson 13: Applications of Derivatives
- Lesson 14: Slopes and Tangents
- Lesson 15: Differential and Integral Calculus
- Lesson 16: Introduction to Multivariate Calculus
- Lesson 17: Vector-Valued Functions
- Lesson 18: Partial Derivatives and Gradient Vectors
- Lesson 19: Higher-Order Derivatives
- Lesson 20: The Chain Rule
- Lesson 21: The Jacobian
- Lesson 22: Hessian Matrices
- Lesson 23: The Laplacian
- Lesson 24: Introduction to Optimization and Mathematical Programming
- Lesson 25: The Method of Lagrange Multipliers
- Lesson 26: Lagrange Multipliers with Inequality Constraints
- Lesson 27: Approximation
- Lesson 28: Taylor Series
Calculus in Machine Learning
- Lesson 29: Gradient Descent Procedure
- Lesson 30: Calculus in Neural Networks
- Lesson 31: Implementing a Neural Network in Python
- Lesson 32: Training a Support Vector Machine: The Separable Case
- Lesson 33: Training a Support Vector Machine: The Non-Separable Case
- Lesson 34: Implementing a Support Vector Machine in Python
- Appendix A: Notations in Mathematics
- Appendix B: How to Set up a Workstation for Python
- Appendix C: How to Solve Calculus Problems
You can see that each part targets a specific learning outcome, and so does each tutorial within each section. This acts as a filter to ensure you are only focused on what you need to know to get to a specific result and do not get bogged down in the math or near-infinite number of digressions.
The tutorials were not designed to teach you everything there is to know about each of the theories or techniques of calculus. They were designed to give you an understanding of how they work, how to use them, and how to interpret the results the fastest way I know how: to learn by doing.
Table of Contents
The screenshot below was taken from the PDF Ebook. It provides you with a full overview of the table of contents from the book.
Take a Sneak Peek Inside The Ebook
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BONUS: Python Code to Work in the Math
…you also get 43 fully working Python scripts
Sample Code Recipes
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 Python script (.py) for each example provided in the book.
This means that you can follow along and compare your answers to a known working implementation of each example in the provided Python files.
This helps to speed up your progress when working through the details of a specific task, such as:
- Doing differentiation using a computer
- Finding the rate of change of functions
- Solving constrained optimization problems using SciPy
- Building a neural network from scratch
- Creating a support vector machine classifiers from scratch
The provided code was developed in a text editor and is intended to be run on the command line. No special IDE or notebooks are required.
All code examples were designed and tested with Python 3.7+.
All code examples will run on modest and modern computer hardware and were executed on a CPU.
Python Technical Details
This section provides some technical details about the code provided with the book.
- Python Version: You can use Python 3.7 or higher.
- Python Modules: You will use NumPy, SciPy, and SymPy.
- Operating System: You can use Windows, Linux, or Mac OS X.
- Hardware: A standard modern workstation will do.
- Editor: You can use a text editor and run the example from the command line.
Don’t have a Python environment?
The appendix contains step-by-step tutorials showing you exactly how to set up a Python machine learning environment.
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About The Author
Hi, I'm Jason Brownlee. I run this site and I wrote and published this book.
I live in Australia with my wife and sons. I love to read books, write tutorials, and develop systems.
I have a computer science and software engineering background as well as Masters and PhD degrees in Artificial Intelligence with a focus on stochastic optimization.
I've written books on algorithms, won and ranked well in competitions, consulted for startups, and spent years in industry. (Yes, I have spend a long time building and maintaining REAL operational systems!)
I get a lot of satisfaction helping developers get started and get really good at applied 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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The industry is demanding skills in machine learning.
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I recommend using standalone Keras version 2.4 (or higher) running on top of TensorFlow version 2.2 (or higher).
All tutorials on the blog have been updated to use standalone Keras running on top of Tensorflow 2.
All books have been updated to use this same combination.
I do not recommend using Keras as part of TensorFlow 2 yet (e.g. tf.keras). It is too new, new things have issues, and I am waiting for the dust to settle. Standalone Keras has been working for years and continues to work extremely well.
There is one case of tutorials that do not support TensorFlow 2 because the tutorials make use of third-party libraries that have not yet been updated to support TensorFlow 2. Specifically tutorials that use Mask-RCNN for object recognition. Once the third party library has been updated, these tutorials too will be updated.
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.
The book “Deep Learning for Time Series Forecasting” shows you how to develop MLP, CNN and LSTM models for univariate, multivariate and multi-step time series forecasting problems.
Mini-courses are free courses offered on a range of machine learning topics and made available via email, PDF and blog posts.
- Short, typically 7 days or 14 days in length.
- Terse, typically giving one tip or code snippet per lesson.
- Limited, typically narrow in scope to a few related areas.
Ebooks are provided on many of the same topics providing full training courses on the topics.
- Longer, typically 25+ complete tutorial lessons, each taking up to an hour to complete.
- Complete, providing a gentle introduction into each lesson and includes full working code and further reading.
- Broad, covering all of the topics required on the topic to get productive quickly and bring the techniques to your own projects.
The mini-courses are designed for you to get a quick result. If you would like more information or fuller code examples on the topic then you can purchase the related Ebook.
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.
A code file is provided for each example presented in the book.
Dataset files used in each chapter are also provided with the book.
The code and dataset files are provided as part of your .zip download in a code/ subdirectory. Code and datasets are organized into subdirectories, one for each chapter that has a code example.
If you have misplaced your .zip download, you can contact me and I can send an updated purchase receipt email with a link to download your package.
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.
All prices on Machine Learning Mastery are in US dollars.
Payments can be made by using either PayPal or a Credit Card that supports international payments (e.g. most credit cards).
You do not have to explicitly convert money from your currency to US dollars.
Currency conversion is performed automatically when you make a payment using PayPal or Credit Card.
After filling out and submitting your order form, you will be able to download your purchase immediately.
Your web browser will be redirected to a webpage where you can download your purchase.
You will also receive an email with a link to download your purchase.
If you lose the email or the link in the email expires, contact me and I will resend the purchase receipt email with an updated download link.
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.
To use a discount code, also called an offer code, or discount coupon when making a purchase, follow these steps:
1. Enter the discount code text into the field named “Discount Coupon” on the checkout page.
Note, if you don’t see a field called “Discount Coupon” on the checkout page, it means that that product does not support discounts.
2. Click the “Apply” button.
3. You will then see a message that the discount was applied successfully to your order.
Note, if the discount code that you used is no longer valid, you will see a message that the discount was not successfully applied to your order.
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.
What order should you read the books?
That is a great question, my best suggestions are as follows:
- Consider starting with a book on a topic that you are most excited about.
- Consider starting with a book on a topic that you can apply on a project immediately.
Also, consider that you don’t need to read all of the books, perhaps a subset of the books will get you the skills you need or want.
Nevertheless, one suggested order for reading the books is as follows:
- Probability for Machine Learning
- Statistical Methods for Machine Learning
- Linear Algebra for Machine Learning
- Optimization for Machine Learning
- Calculus for Machine Learning
- Master Machine Learning Algorithms
- Machine Learning Algorithms From Scratch
- Python for Machine Learning
- Machine Learning Mastery With Weka
- Machine Learning Mastery With Python
- Machine Learning Mastery With R
- Data Preparation for Machine Learning
- Imbalanced Classification With Python
- Time Series Forecasting With Python
- Ensemble Learning Algorithms With Python
- XGBoost With Python
- Deep Learning With Python
- Long Short-Term Memory Networks with Python
- Deep Learning for Natural Language Processing
- Deep Learning for Computer Vision
- Deep Learning for Time Series Forecasting
- Better Deep Learning
- Generative Adversarial Networks with Python
- Building Transformer Models with Attention
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.
I update the books frequently and you can access the latest version of a book at any time.
In order to get the latest version of a book, contact me directly with your order number or purchase email address and I can resend your purchase receipt email with an updated download link.
I do not maintain a public change log or errata for the changes in the book, sorry.
There are no physical books, therefore no delivery is required.
All books are Ebooks in PDF format that you can download immediately after you complete your purchase.
You will receive an email with a link to download your purchase. You can also contact me any time to get a new download link.
I support purchases from any country via PayPal or Credit Card.
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.
Both books focus on deep learning in Python using the Keras library.
The book “Long Short-Term Memory Networks in Python” focuses on how to develop a suite of different LSTM networks for sequence prediction, in general.
The book “Deep Learning for Time Series Forecasting” focuses on how to use a suite of different deep learning models (MLPs, CNNs, LSTMs, and hybrids) to address a suite of different time series forecasting problems (univariate, multivariate, multistep and combinations).
The LSTM book teaches LSTMs only and does not focus on time series. The Deep Learning for Time Series book focuses on time series and teaches how to use many different models including LSTMs.
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 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.
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 for Credit Card 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 or debit 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.
When you purchase a book from my website and later review your bank statement, it is possible that you may see an additional small charge of one or two dollars.
The charge does not come from my website or payment processor.
Instead, the charge was added by your bank, credit card company, or financial institution. It may be because your bank adds an additional charge for online or international transactions.
This is rare but I have seen this happen once or twice before, often with credit cards used by enterprise or large corporate institutions.
My advice is to contact your bank or financial institution directly and ask them to explain the cause of the additional charge.
If you would like a copy of the payment transaction from my side (e.g. a screenshot from the payment processor), or a PDF tax invoice, please contact me directly.
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.