Python for Machine Learning
Learn Python from Machine Learning Projects
We noticed that when people ask about issues in their machine learning project, very often it is not specifically a problem in machine learning but a problem in the programming language they use. It is sad to see someone distracted by the language, such as misunderstanding the error message that the Python interpreter gave. If we know more about working in the Python ecosystem, we can be much more efficient and focused on the machine learning problem itself.
If you already finished a book in Python but still don’t feel comfortable using the language for your project, 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 learn the underlying mechanics of the Python language, the tools in its ecosystem, tips and tricks, and much more.
About this Ebook:
- Read on all devices: PDF format Ebook, no DRM
- Tons of tutorials: 33 step-by-step lessons, 479 pages
- Foundations: Covering the language features in Python that you won’t find in another language, and more
- Show you the toolbox: A wide variety of topics to show you what’s in the Python ecosystem that can help your project, from debugging to deployment
- Working code: 308 Python (.py) code files included
- Bonus: A free NumPy cheat sheet in PDF format enclosed!
Show You the Bigger Picture in Python Programming.
Designed for Developers. Nothing Hidden.
…another Python book?
This one is different!
Python is an amazing programming language. But as a practitioner, you probably do not want to deep dive into the language but want to know just enough to get the job done. However, as Python’s ecosystem has become very large, it is difficult to tell what you should know and what you might skip.
This book is not intended to be your first book on Python. But it can be your second book. We want to learn about Python programming so you can get something done. Perhaps you can use Python to answer some of the questions on Project Euler or Leetcode. Then, this book tells you what’s out there that can help your machine learning project. It can be a third-party library. It can be a way to make your Python program easier to use by your colleagues. It can also offer some bells and whistles to make your project more attractive.
Frustrated with the Language?
Have you ever been frustrated in working with a Python program?
You copy a few lines of code from the web, and it works well. Then, it throws you errors when you modify a little, but you have no idea why. You have questions like:
- … what does this error message mean?
- … why does this code work, but the other gives you a different output?
- … how can I trace down the line that produced the wrong result?
Usually, these are not difficult questions to answer. But you need to know the right tools to use and the right techniques to tackle the problems.
Why Is Python Important to Machine Learning?
So, why must I learn Python if all I want is to develop my machine learning solutions?
You don’t. But you will find your life much easier if you are competent with Python.
Python is an amazing programming language. It is simple to read but also powerful enough that it can do a lot of things. Compared to other languages, it allows fast iteration. If you want to tweak your code a bit, you only need to change a line or two, and you can run the modified code right away. No need to update many places for small changes. No need to wait for minutes and hours to re-compile your code to run it.
Python is never meant to run fast. In fact, if execution speed is the concern, using a different language such as C++ or Java might be a better idea. However, rather than computer time, the human time might be more valuable. Python is the language that allows you to trade off computer time for developer’s time.
In machine learning projects, we never know the right solution at the start. We need many experiments and iterations to finalize our approach. Having a language that allows us to iterate fast means we can improve our solution faster. As a result, a lot of people are using Python. And a lot of libraries are written for Python. This virtuous cycle makes Python a mature language with a powerful ecosystem.
Python is important because it opens the door for us to:
- Use the amazing machine learning libraries such as scikit-learn, TensorFlow, and PyTorch
- Connect to other systems, such as web applications or file systems, easily
- Communicate our idea to other people through our code, even though they didn’t learn Python before
The Wrong Ways to Learn Python
Now you know the importance of Python. But when learning, it is easy to take the wrong route:
1. Deep dive into the language
A programming language is a science by itself. You may learn about its inner mechanics and how they are implemented.
However, you don’t need to be a car mechanic before you drive a car. Similarly, you don’t need to know how Python works under the hood before you program in Python. Moreover, there are thousands and thousands of libraries for Python. It is impossible to be familiar with every one of them.
It would be more exciting if you could get results from a project, such as a machine learning model. Then, we learn one thing at a time when we see it fits. This is the top-down approach. We learn only what is necessary.
2. Copy and paste without understanding
Python is a popular language. Therefore, you can find a lot of code on the Internet. Maybe you find it on a StackOverflow page. Maybe you copied it from a GitHub gist. It may work as you expected, but you didn’t fully understand why. You can move on to your next task, but you did not gain any skills.
It is common at the beginner level when you don’t know what the programming language or its ecosystem can provide to you. Only if you have a better picture of what is out there can you decide what to learn next and why that would be useful. You don’t need to know everything. But if you need to, you know what to learn. Or more importantly, you can identify what is not helpful to you so you can skip it.
A Bigger Picture of Python
We suggest you learn Python from other resources first. You cannot avoid spending hours and days building your knowledge of the programming language from the bottom-up. You need to know the basic syntax and some functions from the standard libraries.
After that, you need to know what else you have under your belt. A family doctor knows when to refer a patient to a specialist. A good Python programmer should know what tools to look for to tackle a problem, even if the tool is unfamiliar. We should get a sense of what Python can possibly provide so that we can harness it on our journey toward machine learning mastery.
3 Areas of Python to Focus On
You don’t need to know all of Python.
The three key areas of Python that we recommend you focus on are:
1. The Language Features
Python combines the language features from many other programming languages to make it an amazing one. You should know how to write Python as an imperative language like C or a functional language like Haskell. You should learn about the idea and syntax for object-oriented programming. You should learn about the map-reduce paradigm.
Most likely, you will borrow code from other people. You need to know what the code is doing. When other people mention that their code uses a decorator syntax or a generator, you should know what they mean.
2. Debugging Skills
When you work on a project, you need to test it. Even a skillful programmer will not write the code perfectly correct in the first shot. You need to know how to trial run your program. If it doesn’t work as expected, you should know how to pinpoint the wrong part and make corrections.
You should also know how to find problems in your program. Can I make it faster? How can I know my input is handled correctly by the program? How can I prevent my code from going wrong in an unexpected situation? Or most importantly, how can I work more efficiently in developing a Python program?
3. The Library
Python is not unique in its position as a programming language. But it stands out because of its ecosystem.
There are a tremendous amount of libraries developed for Python. Hence we save hours and days developing our solution by borrowing the tools from a library. The Python standard library that comes with every Python installation is feature-rich. But there is more we can achieve by using third-party libraries.
Introducing Our New Ebook: “Python for Machine Learning“
Welcome to “Python for Machine Learning”
This book is designed to teach machine learning practitioners like you to become better Python programmer. Even if you’re not interested in machine learning, this book is also suitable for you because you can learn some Python skills that you don’t see easily elsewhere. The examples are from machine learning projects, but you don’t need to understand machine learning to understand the Python tips.
This book was carefully designed to help you bring the knowledge of a wide variety of the tools and techniques of Python to your next project, machine learning or not.
The tutorials were designed to teach you these techniques the fastest and most effective way that we 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.
Programming skill is important to machine learning, and we 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.
We 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 people who know some Python.
Perhaps you already finished a primer on Python. Perhaps you can use Python to answer some of the questions on Project Euler or Leetcode. Then, the lessons this book shows you are the bells and whistles you can add to make your project more attractive.
The lessons in this book do assume a few things about you, such as:
- You know basic Python for programming.
- You can comfortably work with your IDE (such as Spyder or Visual Studio Code) or run Python on the command line.
- You are eager to learn the tips and tricks of Python as a tool for your project.
This guide was written in the top-down and results-first style that you’re used to from Machine Learning Mastery, even some of them are not directly related to machine learning.
What if I Am New to Machine Learning?
This book does not assume you have a background in machine learning. Sometimes, it is entirely unrelated to machine learning.
Machine learning at a high level is a broad topic. It can relate to how to work more efficiently by using the capability of your OS. It also needs to be deployed in a production environment. These are the generic skills that apply to all kinds of projects. You will learn how we can do these in Python.
What if I Am Just Learning Python?
Perfect. This book is written for you!
What if My Programming Skill is Really Poor?
You don’t need to be an expert in Python to read this book. If you still don’t feel comfortable, we have pointers for you to learn the basics of Python, either online or from another book.
You can benefit from this book even if you can barely code. You will know how to learn from other people’s code. You will know how to learn from your own mistakes!
What if I Am Not a Python Programmer?
This book is focused on Python. However, the skills you learn from this book can likely be applied to other languages too.
A modern debugger, for example, always has the features of skipping lines, stepping into a function, setting breakpoints, and watching a variable. We show you how these can help you debug a Python program with pdb, but you can apply what you learned to debug a C++ program in GDB too.
While you learn the vast amount of libraries available in Python and how to use them, you can possibly find their counterpart in your other language. Chances are that the APIs are very similar in the other world because we learn from each other.
If you are convinced, you may start programming in Python today. The appendix of this book shows you how to set up Python on your workstation.
What if I Am Not a Programmer at All?
Even if you do not program, learning what Python is about and what it can do helps you talk to the programmers. You don’t need to learn about the syntax if you don’t write code. But you will be empowered to understand the terminologies of the programming language. Knowing what a programming language can do may open up your eyes to see your computer differently.
About Your Outcomes
…so what will YOU know after reading this book?
After reading and working through this book, you will know:
- How you can communicate with your Python program, using command line arguments or using a config file
- The use of some helpful language constructs, such as enumerate and zip, decorators and generators, as well as Python’s functional programming tools
- What a debugger, profiler, and static analyzer are. How to use them, and how they can help your project
- How to write better, production-quality code
- How to make ad-hoc and hacky modifications to your code to test out your idea faster
- What are the tools to collect data for your machine learning projects
- How to use a database or even Google Sheets to supply data to your project, or even use it as a dashboard to keep track of the output from your Python code
- How to add a web interface to your Python project quickly
You should be able to learn a new idea or two from this book to bring your Python project to the next level.
After reading this book, you will be able to:
- Make your Python project more robust and well prepared for unexpected situations
- Tell how to speed up the Python code, or the strategy to make it more scalable
- Comfortably manage the Python module system and the package installed
What Exactly Is in This Book?
This book was designed to be a second course in Python for machine learning practitioners. Ideally, those with a programming background.
This book was designed around major building blocks of the Python ecosystem that are useful to machine learning projects.
There are a lot of things you could learn about Python, from language mechanics to the various libraries. Our goal is to take you straight to developing an intuition for the elements you can use in Python projects with laser-focused tutorials.
We designed the tutorials to focus on how to get things done with Python. 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 five parts:
- Part 1: Foundation. Discover what Python can provide to you, and how you can use the programming language.
- Part 2: Debugging, Profiling, and Linting. Discover the important tools to help you finish your project. These tools can help you write a correct, efficient, and idiomatic Python program.
- Part 3: Better Code, Better Software. Discover the many tricks to make a better program, or work faster. These include how to save your partial progress and resume later, how to use a decorator to make cleaner code, how to create tests to be confident the program works in different situations, and much more.
- Part 4: Furnish Your Library. Discover some famous libraries in Python to achieve various tasks. These include web scraping, accessing databases, accessing Excel files, plotting charts, and creating web applications, among others.
- Part 5: Platforms. We briefly mention how to make use of the free computing resources from Google Colab and Kaggle Notebooks. This includes how to connect your Python code to free storage from the platform!
Below is an overview of the 33 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: How to Learn Python for Machine Learning
- Lesson 02: Running and Passing Information to a Python Script
- Lesson 03: Some Language Features in Python
- Lesson 04: More Language Features in Python
- Lesson 05: Python Classes and Their Use in Keras
- Lesson 06: Functional Programming in Python
Debugging, Profiling, and Linting
- Lesson 07: Understanding Traceback
- Lesson 08: Profiling Python Code
- Lesson 09: Python Debugging Tools
- Lesson 10: Setting Breakpoints and Exception Hooks
- Lesson 11: Logging in Python
- Lesson 12: Comments, Docstrings, and Type Hints
- Lesson 13: Static Analyzers
Better Code, Better Software
- Lesson 14: An Introduction to Serialization
- Lesson 15: Easier Experimenting in Python
- Lesson 16: Command Line Arguments for Your Python Script
- Lesson 17: An Introduction to Decorators
- Lesson 18: Duck Typing, Scope, and Investigative Functions
- Lesson 19: An Introduction to Unit Testing
- Lesson 20: Techniques to Write Better Python Code
Furnish Your Library
- Lesson 21: Exploring the Python Ecosystem
- Lesson 22: Web Scraping in Python
- Lesson 23: Getting Datasets for Machine Learning
- Lesson 24: Obtaining Time Series Datasets
- Lesson 25: Managing Data for Machine Learning Projects
- Lesson 26: Data Visualization with matplotlib, Seaborn, and Bokeh
- Lesson 27: Scientific Functions in NumPy and SciPy
- Lesson 28: Massaging Data Using Pandas
- Lesson 29: Multiprocessing in Python
- Lesson 30: Web Frameworks for Your Python Projects
- Lesson 31: A First Course on Deploying Python Projects
- Lesson 32: Google Colab for Machine Learning Projects
- Lesson 33: Using Kaggle in Machine Learning Projects
- Appendix A: Python Books
- Appendix B: How to Set up a Workstation for Python
- Appendix C: Small Tricks
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 unrelated objectives.
The tutorials were not designed to teach you everything, nor to have you know everything about each topic. They were designed to help you understand how you can get something done, how to use a tool, and how to see 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
Click image to Enlarge.
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: Python Code to Do Great Things
…you also get 308 fully working Python scripts
and a NumPy cheat sheet
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:
- Add logging to your program
- Build an interactive chart using Bokeh
- Reading the stock market data into a Pandas dataframe
- Reading and writing data at an online spreadsheet in Google Sheets
- Speed up tasks by running functions in parallel
Most of 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 3.9+.
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 but Python 3.9 is recommended.
- 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.
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 Stay On Top Of Their Field
Get The Training You Need!
You don't want to fall behind or miss the opportunity.
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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. 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.
Yes, I offer a 90-day no questions asked money-back guarantee.
I stand behind my books. They contain my best knowledge on a specific machine learning topic, and each book as been read, tested and used by tens of thousands of readers.
Nevertheless, if you find that one of my Ebooks is a bad fit for you, I will issue a full refund.
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.
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
- Deep Learning with PyTorch
- 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.
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: 421867-1511
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.