Machine Learning Algorithms From Scratch
Discover How to Code Machine Algorithms in Python (Without Libraries)
You must understand algorithms to get good at machine learning.
The problem is that they are only ever explained using Math. No longer.
In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.
- Read on all devices: English PDF format EBook, no DRM.
- Tons of tutorials: 18 step-by-step lessons, 237 pages.
- Top Algorithms: 12 top algorithms described.
- Working code: 66 Python (.py) code files included.
No Math. No Libraries. No Hidden Details.
You Learn Best By Implementing Algorithms From Scratch
…But You Need Help With The First Step
Developers Learn Best By Trying Things Out…
If you’re like me, you don’t really understand something until you can implement it from scratch.
You need to understand each piece before you can understand the whole thing.
The same applies to machine learning algorithms.
You won’t feel like you really understand machine learning algorithms until you can put them together yourself.
Without the tools and fancy libraries.
Math Can Really Slow You Down…
The problem is, machine learning algorithms are only ever described using math.
It’s all Greek letters, it’s a pain to read and understand and this can really sap your motivation.
You simply don’t have the time to study 4 years of advanced math just to implement some algorithms.
You don’t need a degree in computer science to implement bubble sort and understand how it works. You just read a simple explanation and implement it in a few lines of code.
Why can’t implementing machine learning algorithms be the same?
You Need Clear Step-By-Step Tutorials…
What is missing is a set of step-by-step tutorials that lay it all out.
You don’t need to know the mathematical reason why machine learning algorithms work. You need a clear explanation of how they work so you can turn that into code.
You need each step of the learning and prediction spelled out super simple so you can write a small function that does the same thing and write a little test to confirm it works.
It’s the same way we learn anything when programming. Piece-by-piece.
Put a few of these pieces together and you have a world-class machine learning algorithm.
Introducing: “Machine Learning Algorithms From Scratch“
This is the book that I wish I had when starting out.
It is designed for exactly the way developers like you learn.
The book works through how to write small functions to load data and prepare it for learning.
There are tutorials on how to evaluate predictions and evaluate the performance of machine learning models.
Then there’s a suite of tutorials on how to implement linear, nonlinear and even ensemble machine learning algorithms from scratch.
- Each tutorial is written in Python. This is the growing and soon to be the dominant programming language for applied machine learning and data science.
- Each tutorial uses the standard library. There is no NumPy, no SciPy, no Scikit-Learn and no other advanced libraries to hide the details.
- Each tutorial is standalone. Everything you need to understand and run an algorithm is right there, no flipping back and forth through the book to piece it together yourself.
- Each tutorial is super simple. Code does not use fancy Python tricks that are hard to read and even harder to understand. I’ve opted for simple loops and the simplest data structures to help even Python novices see exactly what is going on.
- Each tutorial actually works. Each function is added one at a time with full explanation and spot testing. Nothing is sprung on you and all the code works with sample output for you to compare to.
‘Machine Learning Algorithms From Scratch‘ is for Python Programmers
…with NO background in Math
…and BIG enthusiasm for machine learning
Machine Learning Algorithms From Scratch was designed for you.
This is the book that you have been looking for.
The book that finally unlocks how machine learning algorithms work.
- You don’t need the math. Everything is explained in simple words, and we work in the language you do know: code.
- You don’t need to be a Python master. All the code examples are clear and simple and no confusing Python tricks and clever shortcuts are used.
- You don’t need to know machine learning. That is why you need this book, to make your start and finally discover how machine learning algorithms actually work.
- You don’t need a ton of time. Each tutorial was designed for you to complete in 30 minutes to 60 minutes, and you could easily work through the book in 2 weeks at nights and weekend (or one power weekend as some like to do).
- You don’t need to break the bank. Put back the $100+ machine learning textbooks and get started with a book designed for you in your language of tutorials, explanations and working code.
Let’s take a closer look at the breakdown of what you will discover inside this EBook.
Everything You Need To Know to Code
Machine Learning Algorithms From Scratch With Python
The tutorials were designed to cover the topics needed for applied machine learning projects.
They are presented in 4 main sections:
1. Data Preparation
- Load Data: How to load and manipulate data from the CSV standard file format.
- Data Scaling: How to prepare numerical data for learning algorithms.
- Algorithm Evaluation: Techniques for estimating the performance of algorithms on unseen data.
- Evaluation Metrics: Scoring methods to evaluate the skill of predictions made on new data.
- Baseline Models: Techniques that can establish the best worst case from which to improve on a problem.
2. Linear Algorithms
- Algorithm Test Harness: Drawing together the elements from the previous section to consistently and objectively evaluate different techniques on the same problem.
- Simple Linear Regression: For predicting numerical values when there is only a single input.
- Multivariate Linear Regression: For predicting numerical values with more than one input
(trained using StochasticGradient Descent).
- Logistic Regression: For predicting a class value on 2 class problems
(trained using Stochastic Gradient Descent).
- Perceptron: The simplest type of neural network for classification problems
(trained using StochasticGradient Descent).
3. Nonlinear Algorithms
- Classification and Regression Trees: Decision trees, in this case applied to classification problems.
- Naive Bayes: The very simple application of Bayes’ Theorem to classification problems.
- k-Nearest Neighbors: For predicting numerical or categorical outcomes directly from training data.
- Learning Vector Quantization: A type of neural network that is more efficient than k-Nearest Neighbors.
- Backpropagation: The most widely used type of artificial neural network that underlies the broader field of deep learning.
4. Ensemble Algorithms
- Bootstrap Aggregation: Also known as bagging that involves an ensemble of decision trees.
- Random Forest: An extension of bagging that results in faster training and better performance.
- Stacked Generalization: An ensemble method also known as stacking or blending that learns how to best combine the perdictions from multiple models.
Below is a snapshot of the complete Table of Contents from the Ebook.
Each Algorithm is Demonstrated 2 Ways
1. Small Contrived Dataset
All algorithms are first developed and demonstrated on a small contrived dataset.
This is so that algorithms can be understood and demonstrated in isolation in a controlled environment.
2. Small Real-World Dataset
Each algorithm is then demonstrated on a small real world dataset from a range of different domains.
Problems were carefully selected from the UCI Machine Learning repository and all datasets are distributed with the book.
What More Do You Need?
Take a Sneak Peek Inside The Ebook
Click image to Enlarge.
BONUS: Machine Learning Algorithm Code Recipes
…you also get 66 fully working machine learning algorithm scripts
You also get a copy of all the code used in the book.
- Every small example as we develop machine learning algorithms.
- Each end-of-tutorial complete working example applied to a real world dataset.
Each code example in the book is standalone.
This means that it will run, as is, with nothing additional required.
The datasets used in each tutorial are also provided with the code, so no hunting down data from the web.
- You always have a version that works.
- You can compare your tutorial code with the finished working version.
- You can compare your results to the expected results as you work.
- You have a basis for developing your own extensions to the algorithms.
- You can adapt code and use them in your own projects immediately.
This is the beginning of your own Machine Learning Code Library, that you can develop further and leverage on your future projects.
- You get one Python code file (.py) for each example in the book.
- You get the real world dataset (.csv) used in each example in the book.
About The Author
Hi, I'm Jason Brownlee. I run this site and I wrote and published this book.
I live in Australia with my wife and sons. I love to read books, write tutorials, and develop systems.
I have a computer science and software engineering background as well as Masters and PhD degrees in Artificial Intelligence with a focus on stochastic optimization.
I've written books on algorithms, won and ranked well in competitions, consulted for startups, and spent years in industry. (Yes, I have spend a long time building and maintaining REAL operational systems!)
I get a lot of satisfaction helping developers get started and get really good at applied machine learning.
I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
I'm here to help if you ever have any questions. I want you to be awesome at machine learning.
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Are you a Student, Teacher or Retiree?
Do you have any Questions?
What Are Skills in Machine Learning Worth?
Your boss asks you:
Hey, can you build a predictive model for this?
Imagine you had the skills and confidence to say:
...and follow through.
I have been there. It feels great!
How much is that worth to you?
The industry is demanding skills in machine learning.
The market wants people that can deliver results, not write academic papers.
Business knows what these skills are worth and are paying sky-high starting salaries.
A Data Scientists Salary Begins at:
$100,000 to $150,000.
A Machine Learning Engineers Salary is Even Higher.
What Are Your Alternatives?
You made it this far.
You're ready to take action.
But, what are your alternatives? What options are there?
(1) A Theoretical Textbook for $100+
...it's boring, math-heavy and you'll probably never finish it.
(2) An On-site Boot Camp for $10,000+
...it's full of young kids, you must travel and it can take months.
(3) A Higher Degree for $100,000+
...it's expensive, takes years, and you'll be an academic.
For the Hands-On Skills You Get...
And the Speed of Results You See...
And the Low Price You Pay...
Machine Learning Mastery Ebooks are
And they work. That's why I offer the money-back guarantee.
You're A Professional
The field moves quickly,
...how long can you wait?
You think you have all the time in the world, but...
- New methods are devised and algorithms change.
- New books get released and prices increase.
- New graduates come along and jobs get filled.
Right Now is the Best Time to make your start.
Bottom-up is Slow and Frustrating,
...don't you want a faster way?
Can you really go on another day, week or month...
- Scraping ideas and code from incomplete posts.
- Skimming theory and insight from short videos.
- Parsing Greek letters from academic textbooks.
Targeted Training is your Shortest Path to a result.
Professionals 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)
Thanks for your interest.
Sorry, I do not support third-party resellers for my books (e.g. reselling in other bookstores).
My books are self-published and I think of my website as a small boutique, specialized for developers that are deeply interested in applied machine learning.
As such I prefer to keep control over the sales and marketing for my books.
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Sorry, I don’t sell hard copies of my books.
All of the books and bundles are Ebooks in PDF file format.
This is intentional and I put a lot of thought into the decision:
- The books are full of tutorials that must be completed on the computer.
- The books assume that you are working through the tutorials, not reading passively.
- The books are intended to be read on the computer screen, next to a code editor.
- The books are playbooks, they are not intended to be used as references texts and sit the shelf.
- The books are updated frequently, to keep pace with changes to the field and APIs.
I hope that explains my rationale.
If you really do want a hard copy, you can purchase the book or bundle and create a printed version for your own personal use. There is no digital rights management (DRM) on the PDF files to prevent you from printing them.
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I will then organize a refund for you.
I would love to hear why the book is a bad fit for you.
Anything that you can tell me to help improve my materials will be greatly appreciated.
I have a thick skin, so please be honest.
Sample chapters are provided for each book.
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Sorry, I no longer distribute evaluation copies of my books due to some past abuse of the privilege.
If you are a teacher or lecturer, I’m happy to offer you a student discount.
Contact me directly and I can organize a discount for you.
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The books are only available in PDF file format.
This is by design and I put a lot of thought into it. My rationale is as follows:
- I use LaTeX to layout the text and code to give a professional look and I am afraid that EBook readers would mess this up.
- The increase in supported formats would create a maintenance headache that would take a large amount of time away from updating the books and working on new books.
- Most critically, reading on an e-reader or iPad is antithetical to the book-open-next-to-code-editor approach the PDF format was chosen to support.
My materials are playbooks intended to be open on the computer, next to a text editor and a command line.
They are not textbooks to be read away from the computer.
Sorry, all of my books are self-published and do not have ISBNs.
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If you fall into one of these groups and would like a discount, please contact me and ask.
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There is no digital rights management (DRM) on the PDFs to prevent you from printing them.
Please do not distribute printed copies of your purchased books.
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I provide two copies of the table of contents for each book on the book’s page.
- A written summary that lists the tutorials/lessons in the book and their order.
- A screenshot of the table of contents taken from the PDF.
If you are having trouble finding the table of contents, search the page for the section titled “Table of Contents”.
I only support payment via PayPal or Credit Card.
If you purchase a book or bundle and later decide that you want to upgrade to the super bundle, I can arrange it for you.
Contact me and let me know that you would like to upgrade and what books or bundles you have already purchased and which email address you used to make the purchases.
I will create a special offer code that you can use to get the price of books and bundles purchased so far deducted from the price of the super bundle.
I am happy for you to use parts of my material in the development of your own course material, such as lecture slides for an in person class or homework exercises.
I am not happy if you share my material for free or use it verbatim. This would be copyright infringement.
All code on my site and in my books was developed and provided for educational purposes only. I take no responsibility for the code, what it might do, or how you might use it.
If you use my material to teach, please reference the source, including:
- The Name of the author, e.g. “Jason Brownlee”.
- The Title of the tutorial or book.
- The Name of the website, e.g. “Machine Learning Mastery”.
- The URL of the tutorial or book.
- The Date you accessed or copied the code.
- Jason Brownlee, Machine Learning Algorithms in Python, Machine Learning Mastery, Available from https://machinelearningmastery.com/machine-learning-with-python/, accessed April 15th, 2018.
Also, if your work is public, contact me, I’d love to see it out of general interest.
Thanks for asking.
I prefer to keep complete control over my content for now.
My books are self-published and are only available from my website.
I don’t have exercises or assignments in my books.
I do have end-to-end projects in some of the books, but they are in a tutorial format where I lead you through each step.
The book chapters are written as self-contained tutorials with a specific learning outcome. You will learn how to do something at the end of the tutorial.
Some books have a section titled “Extensions” with ideas for how to modify the code in the tutorial in some advanced ways. They are like self-study exercises.
Sorry, I do not offer a certificate of completion for my books or my email courses.
Sorry, new books are not included in your super bundle.
I release new books every few months and develop a new super bundle at those times.
All existing customers will get early access to new books at a discount price.
Note, that you do get free updates to all of the books in your super bundle. This includes bug fixes, changes to APIs and even new chapters sometimes. I send out an email to customers for major book updates or you can contact me any time and ask for the latest version of a book.
I have books that do not require any skill in programming, for example:
Other books do have code examples in a given programming language.
You must know the basics of the programming language, such as how to install the environment and how to write simple programs. I do not teach programming, I teach machine learning for developers.
You do not need to be a good programmer.
That being said, I do offer tutorials on how to setup your environment efficiently and even crash courses on programming languages for developers that may not be familiar with the given language.
My books do not cover the theory or derivations of machine learning methods.
This is by design.
My books are focused on the practical concern of applied machine learning. Specifically, how algorithms work and how to use them effectively with modern open source tools.
If you are interested in the theory and derivations of equations, I recommend a machine learning textbook. Some good examples of machine learning textbooks that cover theory include:
I generally don’t run sales.
If I do have a special, such as around the launch of a new book, I only offer it to past customers and subscribers on my email list.
I do offer book bundles that offer a discount for a collection of related books.
I do offer a discount to students, teachers, and retirees. Contact me to find out about discounts.
Sorry, I don’t have videos.
I only have tutorial lessons and projects in text format.
This is by design. I used to have video content and I found the completion rate much lower.
I want you to put the material into practice. I have found that text-based tutorials are the best way of achieving this. With text-based tutorials you must read, implement and run the code.
With videos, you are passively watching and not required to take any action. 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
- 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.
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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.
Do you have another question?