Basics of Linear Algebra for Machine Learning
Discover the Mathematical Language of Data in Python
$27 USD
Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it.
In this new laserfocused Ebook written in the friendly Machine Learning Mastery style that you’re used to, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know.
Using clear explanations, standard Python libraries, and stepbystep tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.
About the Ebook:
 Read on all devices: English PDF format EBook, no DRM.
 Tons of tutorials: 19 stepbystep lessons, 264 pages.
 Foundations: vectors, matrix calculations, much more.
 Working code: 108 Python (.py) code files included.
Clear and Complete Examples.
Designed for Developers. Nothing Hidden.
Convinced?
Why Linear Algebra?
Linear algebra is a subfield of mathematics concerned with vectors, matrices, and operations on these data structures. It is absolutely key to machine learning.
As a machine learning practitioner, you must have an understanding of linear algebra.
It’s a huge field of study that has made an impact on other fields, such as statistics, as well as engineering and physics. Thankfully, we don’t need to know the breadth and depth of the field of linear algebra 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, and you are stopped in your tracks with questions like:
 … what do the terms mean?
 … why are there no operators between terms?
 … what does this Greek letter mean?
Unless you have a basic knowledge of linear algebra, you will not be able to read and understand even the most basic equations.
Tensors!?
Have you heard of TensorFlow, Google’s Python library for deep learning?
Did you know that “tensor” is a term taken directly from the field of linear algebra and it simply means an array with more than twodimensions?
Why Is Linear Algebra Important to Machine Learning?
So, why is linear algebra used so much to describe machine learning algorithms?
Linear algebra is about vectors and matrices and in machine learning we are always working with vectors and matrices (arrays) of data.
Linear algebra is essentially the mathematics of data.
It provides useful shortcuts for describing data as well as operations on data that we need to perform in machine learning methods.
Linear algebra is not magic
And, linear algebra is not trying to be exclusive or opaque.
As a first step, think of linear algebra as a shortcut language or notation to make describing some operations compact.
There are common machine learning techniques that can only be understood via their linear algebra descriptions because they come from the field. Techniques such as:
 SingularValue Decomposition, or SVD.
 Principal Component Analysis.
 Linear Least Squares for Linear Regression.
Without a basic understanding of matrices and matrix operations, an understanding of these techniques will elude you.
The 3 Mistakes Made By Beginners
Once you discover the importance of linear algebra to machine learning, there are three key mistakes that beginners make:
1. Beginners Study Linear Algebra Too Early
If you ask how to get started in machine learning, you will very likely be told to start with linear algebra.
We know that knowledge of linear algebra is critically important, but it does not have to be the place to start. Learning linear algebra first, then calculus, probability, statistics, and eventually machine learning theory is a long and slow bottomup path.
A better fit for developers is to start with systematic procedures that get results, and work back to the deeper understanding of theory, using working results as a context. I call this the topdown, or resultsfirst, approach to machine learning, and linear algebra is not the first step, but perhaps the second or third.
2. Beginners Study Too Much Linear Algebra
When practitioners do circle back to study linear algebra, they learn far more of the field than is required for or relevant to machine learning.
Linear algebra is a large field of study that has tendrils into engineering, physics, and quantum physics. There are also theorems and derivations for nearly everything, most of which will not help you get better skill from, or a deeper understanding of, your machine learning model.
Only a specific subset of linear algebra is required, though you can always go deeper once you have the basics.
3. Beginners Study Linear Algebra Wrong
Linear algebra textbooks will teach you linear algebra in the classical university bottomup approach. This is too slow (and painful) for your needs as a machine learning practitioner.
Like learning machine learning itself, take the topdown approach. Rather than starting with theorems and abstract concepts, you can learn the basics of linear algebra in a concrete way with data structures and worked examples of operations on those data structures. It’s so much faster (and fun).
Once you know how operations work, you can circle back and learn how they were derived. If you’re interested.
A Better Way into Linear Algebra
I am frustrated at seeing practitioner after practitioner diving into linear algebra textbooks and online courses designed for undergraduate students and giving up. The bottomup approach is hard, especially if you already have a full time job.
Linear algebra is not only important to machine learning, but it is also a lot of fun, or can be if it is approached in the right way.
I want to help you see the field the way I see it: as just another set of tools we can harness on our journey toward machine learning mastery.
5 Areas of Linear Algebra to Focus On
You don’t need to know all of linear algebra.
The five key areas of linear algebra that I recommend you focus on are:
1. Learn Linear Algebra Notation
You need to be able to read and write vector and matrix notation.
Algorithms are described in books, papers, and on websites using vector and matrix notation.
Linear algebra is the mathematics of data and the notation allows you to describe operations on data precisely with specific operators.
You need to be able to read and write this notation.
2. Learn Linear Algebra Arithmetic
In partnership with the notation of linear algebra are the arithmetic operations performed.
You need to know how to add, subtract, and multiply scalars, vectors, and matrices.
A challenge for newcomers to the field of linear algebra are operations such as matrix multiplication and tensor multiplication that are not implemented as the direct multiplication of the elements of these structures, and at first glance appear nonintuitive.
3. Learn Linear Algebra for Statistics
You must learn linear algebra in order to be able to learn statistics. Especially multivariate statistics.
Statistics is concerned with describing and understanding data. As the mathematics of data, linear algebra has left its fingerprint on many related fields of mathematics, including statistics.
In order to be able to read and interpret statistics, you must learn the notation and operations of linear algebra.
4. Learn Matrix Factorization
Building on notation and arithmetic is the idea of matrix factorization, also called matrix decomposition.
You need to know how to factorize a matrix and what it means.
Matrix factorization is a key tool in linear algebra and used widely as an element of many more complex operations in both linear algebra (such as the matrix inverse) and machine learning (least squares, PCA, SVD, and more).
5. Learn Linear Least Squares
You need to know how to use matrix factorization to solve linear least squares.
Linear algebra was originally developed to solve systems of linear equations. These are equations where there are more equations than there are unknown variables. As a result, they are challenging to solve arithmetically because there is no single solution as there is no line or plane that can fit the data without some error.
Problems of this type can be framed as the minimization of squared error, called least squares, and can be recast in the language of linear algebra, called linear least squares.
Bonus Reason
If I could give one more reason, it would be: because it is fun.
Seriously.
Learning linear algebra, at least the way I teach it with practical examples and executable code, is a lot of fun. Once you can see how the operations work on real data, it is hard to avoid developing a strong intuition for the methods.
Introducing My New Ebook:”Basics of Linear Algebra for Machine Learning“
Welcome to the “Basics of Linear Algebra for Machine Learning”
I designed this book to teach machine learning practitioners, like you, stepbystep the basics of linear algebra with concrete and executable examples in Python.
This book was carefully designed to help you bring the knowledge of a wide variety of the tools and techniques of linear algebra 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. With executable code that you can run to develop the intuitions required, and that you can copyandpaste into your project and immediately get a result.
Linear Algebra 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.
Convinced?
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 endtoend, 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 NumPy for array manipulation.
 You want to learn linear algebra to deepen your understanding and application of machine learning.
This guide was written in the topdown and resultsfirst 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, I do recommend that you learn how to work through a predictive modeling problem first. It will give you the context for linear algebra. Otherwise the topic will feel too abstract.
What if I Am Just a Developer?
Perfect. I wrote this book for you.
What if My Math is Really Poor?
Maybe you learned linear algebra a long time ago back in school?
Maybe you never covered linear algebra 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.
I even show you how to manipulate NumPy arrays from first principles, because that is how we do linear algebra in Python.
The book even has an appendix to show you how to set up Python on your workstation.
What if I Am Working Through a Linear Algebra Course at a University?
Excellent!
This book is not a substitute for an undergraduate course in linear algebra 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 linear algebra is and why it is relevant and important to machine learning.
 How to create, index, and generally manipulate data in NumPy arrays.
 What a vector is and how to perform vector arithmetic and calculate vector norms.
 What a matrix is and how to perform matrix arithmetic, including matrix multiplication.
 A suite of types of matrices, their properties, and advanced operations involving matrices.
 What a tensor is and how to perform basic tensor arithmetic.
 Matrix factorization methods, including the eigendecomposition and singularvalue decomposition.
 How to calculate and interpret basic statistics using the tools of linear algebra.
 How to implement methods using the tools of linear algebra such as principal component analysis and linear least squares regression.
This new basic understanding of linear algebra will impact your practice of machine learning.
After reading this book, you will be able to:
 Read the linear algebra mathematics in machine learning papers.
 Implement the linear algebra descriptions of machine learning algorithms.
 Describe your machine learning models using the notation and operations of linear algebra.
What Exactly Is in This Book?
This book was designed to be a crash course in linear algebra for machine learning practitioners. Ideally, those with a background as a developer.
This book was designed around major data structures, operations, and techniques in linear algebra that are directly relevant to machine learning algorithms.
There are a lot of things you could learn about linear algebra, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laserfocused tutorials.
I designed the tutorials to focus on how to get things done with linear algebra. 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 linear algebra and the relationship it has with the field of machine learning.
 Part 2: NumPy. Discover NumPy tutorials that show you how to create, index, slice, and reshape NumPy arrays, the main data structure used in machine learning and the basis for linear algebra examples in this book.
 Part 3: Matrices. Discover the key structures for holding and manipulating data in linear algebra in vectors, matrices, and tensors.
 Part 4: Factorization. Discover a suite of methods for decomposing a matrix into its constituent elements in order to make numerical operations more efficient and more stable.
 Part 5: Statistics. Discover statistics through the lens of linear algebra and its application to principal component analysis.
 Part 6: Projects. Put what you learned into practice by solving linear regression, visualizing dataset, building recommender system, and so on.
Lessons Overview
Below is an overview of the 24 stepbystep tutorial lessons you will work through:
Each lesson was designed to be completed in about 30to60 minutes by the average developer.
Foundation
 Lesson 01: Introduction to Linear Algebra
 Lesson 02: Linear Algebra and Machine Learning
 Lesson 03: Examples of Linear Algebra in Machine Learning
NumPy
 Lesson 04: Introduction to NumPy Arrays
 Lesson 05: Index, Slice, and Reshape NumPy Arrays
 Lesson 06: NumPy Array Broadcasting
 Lesson 07: Set Axis for Rows and Columns
Matrices
 Lesson 08: Vectors and Vector Arithmetic
 Lesson 09: Vector Norms
 Lesson 10: Matrices and Matrix Arithmetic
 Lesson 11: Types of Matrices
 Lesson 12: Matrix Operations
 Lesson 13: Sparse Matrices
 Lesson 14: Tensors and Tensor Arithmetic
Factorization
 Lesson 15: Matrix Decompositions
 Lesson 16: Eigendecomposition
 Lesson 17: Singular Value Decomposition
Statistics
 Lesson 18: Introduction to Multivariate Statistics
 Lesson 19: Principal Component Analysis
Projects
 Lesson 20: Linear Regression
 Lesson 21: Principal Component Analysis for Visualization
 Lesson 22: Vector Space Models
 Lesson 23: Using Singular Value Decomposition to Build a Recommender System
 Lesson 24: Face Recognition using Principal Component Analysis
Appendix
 Appendix A: Getting Help
 Appendix B: How to Set up a Workstation for Python
 Appendix C: Linear Algebra Cheat Sheet
 Appendix D: Basic Math Notation
You can see that each part targets a specific learning outcome, and so does each tutorial within each part. This acts as a filter to ensure you are only focused on the things you need to know to get to a specific result and do not get bogged down in the math or nearinfinite number of digressions.
The tutorials were not designed to teach you everything there is to know about each of the theories or techniques of linear algebra. 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 a full overview of the table of contents from the book.
Take a Sneak Peek Inside The Ebook
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Download Your Sample Chapter
Do you want to take a closer look at the book? Download a free sample chapter PDF.
Enter your email address and your sample chapter will be sent to your inbox.
Click Here to Get My Sample Chapter
BONUS: Linear Algebra Python Code Recipes
…you also get 92 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 a lot to speed up your progress when working through the details of a specific task, such as:
 Creating and manipulating NumPy arrays.
 Arithmetic with vectors and matrices.
 Advanced matrix operations.
 Matrix factorization methods
 Statistical methods with matrices.
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 3.6+.
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 3.6 or higher.
 SciPy: You will use NumPy and scikitlearn.
 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?
No problem!
The appendix contains stepbystep 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 topdown and resultsfirst 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.
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:
"YES!"
...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 skyhigh 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, mathheavy and you'll probably never finish it.
(2) An Onsite 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.
OR...
For the HandsOn Skills You Get...
And the Speed of Results You See...
And the Low Price You Pay...
Machine Learning Mastery Ebooks are
Amazing Value!
And they work. That's why I offer the moneyback 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.
Bottomup 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 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 (78)
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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.
No.
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:
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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 textbased tutorials are the best way of achieving this. With textbased tutorials you must read, implement and run the code.
With videos, you are passively watching and not required to take any action. Videos are entertainment or infotainment instead of productive learning and work.
After reading and working through the tutorials you are far more likely to use what you have learned.
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Yes.
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 thirdparty libraries that have not yet been updated to support TensorFlow 2. Specifically tutorials that use MaskRCNN for object recognition. Once the third party library has been updated, these tutorials too will be updated.
The book “Long ShortTerm 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 multistep time series forecasting problems.
Minicourses are free courses offered on a range of machine learning topics and made available via email, PDF and blog posts.
Minicourses are:
 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.
Ebooks are:
 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 minicourses 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 nonprogrammers 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 stepbystep 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 jumpstart your own predictive modeling problems.
My books are playbooks. Not textbooks.
They have no deep explanations of theory, just working examples that are laserfocused 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 topdown, rather than bottomup 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
 Deep Learning with PyTorch
 Long ShortTerm 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
 Productivity with ChatGPT (this book can be read in any order)
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.
Generally, no.
Multiseat 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 endtoend:
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 scikitlearn)
These are great places to start.
You can always circle back and pickup 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 ShortTerm 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 ShortTerm 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 ShortTerm 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 ShortTerm 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.
You may need a business or corporate tax number for “Machine Learning Mastery“, the company, for your own tax purposes. This is common in EU companies for example.
The Machine Learning Mastery company is operated out of Puerto Rico.
As such, the company does not have a VAT identification number for the EU or similar for your country or regional area.
The company does have a Company Number. The details are as follows:
 Company Name: Zeus LLC
 Company Number: 4218671511
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 sidetracked 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 welltested 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.
Discounts
I do offer discounts to students, teachers and retirees.
Please contact me to find out more.
Free Material
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 workthrough 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 endtoend 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 inperson 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 selfpublished 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 nonpaying 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.
Yes.
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).
Yes.
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 startups.
You can focus on providing value with machine learning by learning and getting very good at working through predictive modeling problems endtoend. 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.