Deep Learning for Time Series Forecasting
Predict the Future with MLPs, CNNs and LSTMs in Python
Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.
In this new Ebook written in the friendly Machine Learning Mastery style that you’re used to, skip the math and jump straight to getting results.
With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.
About this Ebook:
- Read on all devices: PDF format Ebook, no DRM.
- Tons of tutorials: 5 parts, 25 step-by-step lessons, 575 pages.
- Real-world projects: 2 large end-to-end tutorial projects.
- Many datasets: Univariate, multivariate, multi-step, and more.
- Working code: 131 Python (.py) code files included.
Clear, Complete End-to-End Examples.
Click to jump straight to the packages.
…why deep learning?
The Promise of Deep Learning for Time Series Forecasting
Traditionally, time series forecasting has been dominated by linear methods because they are well understood and effective on many simpler forecasting problems.
Deep learning neural networks are able to automatically learn arbitrary complex mappings from inputs to outputs and support multiple inputs and outputs.
Multilayer Perceptrons (MLPs)
Generally, neural networks like Multilayer Perceptrons or MLPs provide capabilities that are offered by few algorithms, such as:
- Robust to Noise. Neural networks are robust to noise in input data and in the mapping function and can even support learning and prediction in the presence of missing values.
- Nonlinear. Neural networks do not make strong assumptions about the mapping function and readily learn linear and nonlinear relationships.
- Multivariate Inputs. An arbitrary number of input features can be specified, providing direct support for multivariate forecasting.
- Multi-step Forecasts. An arbitrary number of output values can be specified, providing
direct support for multi-step and even multivariate forecasting.
For these capabilities alone, feedforward neural networks may be useful for time series forecasting.
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks or CNNs are a type of neural network that was designed to efficiently handle image data.
The ability of CNNs to learn and automatically extract features from raw input data can be applied to time series forecasting problems. A sequence of observations can be treated like a one-dimensional image that a CNN model can read and distill into the most salient elements.
- Feature Learning. Automatic identification, extraction and distillation of salient features from raw input data that pertain directly to the prediction problem that is being modeled.
CNNs get the benefits of Multilayer Perceptrons for time series forecasting, namely support for multivariate input, multivariate output and learning arbitrary but complex functional relationships, but do not require that the model learn directly from lag observations. Instead, the model can learn a representation from a large input sequence that is most relevant for the prediction problem.
Long Short-Term Memory Networks (LSTMs)
Recurrent neural networks like the Long Short-Term Memory network or LSTM add the explicit handling of order between observations when learning a mapping function from inputs to outputs, not offered by MLPs or CNNs. They are a type of neural network that adds native support for input data comprised of sequences of observations.
- Native Support for Sequences. Recurrent neural networks directly add support for input sequence data.
This capability of LSTMs has been used to great effect in complex natural language processing problems such as neural machine translation where the model must learn the complex interrelationships between words both within a given language and across languages in translating form one language to another.
- Learned Temporal Dependence. The most relevant context of input observations to the expected output is learned and can change dynamically.
The model both learns a mapping from inputs to outputs and learns what context from the input sequence is useful for the mapping, and can dynamically change this context as needed.
How do you Apply Deep Learning Methods to Time Series Data?
Deep learning methods are trained using supervised learning and expect data in the form of samples with inputs and outputs.
Time series are long sequences of numbers.
Methods like Convolutional Neural Networks and Long Short-Term Memory networks expect samples to have a three-dimensional structure.
Once you have the data in the right format, you must design a deep learning model for the problem. This is where things get really interesting.
Perhaps one of the topics that I am asked the most about is how to use deep learning methods for time series forecasting.
I have carefully designed a suite of tutorials to address these specific questions.
“Deep Learning for Time Series Forecasting“
This book was designed to show you exactly how to apply deep learning methods to time series forecasting problems.
In writing this book, I imagined that you were provided with a dataset and a desire to use deep learning methods to address it. I designed the chapters to walk you through the process of first establishing a baseline of performance with naive and classical methods. I then provide step-by-step tutorials to show exactly how to develop a suite of different types of neural network models for time series forecasting. After we cover these basics, I then hammer home how to use them on real-world datasets with example after example on larger projects.
This is not a book for beginners.
The focus on deep learning methods means that we don’t focus on many other important areas of time series forecasting, such as data visualization, how classical methods work, the development of machine learning solutions, or even depth and details on how the deep learning methods work. I assume that you are familiar with these introductory topics.
In addition to providing a playbook to show you how to develop deep learning models for your own time series forecasting problems, I designed this book to highlight the areas where deep learning methods may show the most promise. Deep learning may be the future of complex and challenging time series forecasting and I think this book will help you get started and make rapid progress on your own forecasting problems. I hope that you agree and are as excited as I am about the journey ahead.
Develop Practical Skills for Time Series Forecasting
That You Can Apply Immediately
You will work through 5 different types of time series forecasting problems.
- Univariate. A single series of observations over time.
- Multivariate. Multiple inter-related observations over time.
- Multi-step. Forecast multiple time steps into the future.
- Multivariate Multi-step. Forecast multiple time steps into the future for multiple different series.
- Classification. Predict a discrete class given a sequence of observations over time.
Deep Learning Algorithms
You will discover 4 deep learning methods that you can use to develop defensible time series forecasting methods.
- MLPs. The classical neural network architecture including how to grid search model hyperparameters.
- CNNs. Simple CNN models as well as multi-channel models and advanced multi-headed and multi-output models.
- LSTMs. Simple LSTM models, Stacked LSTMs, Bidirectional LSTMs and Encoder-Decoder models for sequence-to-sequence learning.
- Hybrids. Hybrids of MLP, CNN and LSTM models such as CNN-LSTMs, ConvLSTMs and more.
You will discover 3 methods that you can use to develop robust baselines for your time series forecasting problems.
- Simple forecast methods. Methods such as naive or persistence forecasting and averaging methods, as well as how to optimize their performance
- Autoregressive forecasting methods. Methods such as ARIMA and Seasonal ARIMA (SARIMA) and how to grid search their hyperparameters.
- Exponential smoothing forecasting methods. Methods such single, double and triple exponential smoothing also called ETS and how to grid search their hyperparameters.
You will work through 3 main types of real-world practical time series forecasting projects.
- Univariate Datasets. Forecast a range of datasets such as sales, births, temperature and more.
- Household Power Usage. Weekly forecasts of the total amount of electricity consumed by a single household.
- Human Activity Recognition. Predict the specific type of movement based on smartphone accelerometer data.
…so is this book right for YOU?
Who Is This Book For?
Let’s make sure you are in the right place.
This book is for developers that know some applied machine learning and some deep learning. This is not a beginners book.
Maybe you want or need to start using deep learning for time series on your research project or on a project at work. This book was written to help you do that quickly and efficiently by compressing years worth of knowledge and experience into a laser-focused course of hands-on tutorials.
This guide was written in the top-down and results-first style that you’re used to from Machine Learning Mastery.
The lessons in this book assume a few things about you.
You need to know:
- You need to know the basics of Python programming.
- You need to know the basics of working with time series data.
- You need to know the basics of deep learning methods.
You do NOT need to know:
- You do not need to be a math wiz!
- You do not need to be a deep learning expert!
- You do not need to be a master of time series forecasting!
…so what will YOU know after reading it?
About Your Learning Outcomes
This book will teach you how to get results.
After reading and working through this book,
you will know:
- About the promise of neural networks and deep learning methods in general for time series forecasting.
- How to transform time series data in order to train a supervised learning algorithm, such as deep learning methods.
- How to develop baseline forecasts using naive and classical methods by which to determine whether forecasts from deep learning models have skill or not.
- How to develop Multilayer Perceptron, Convolutional Neural Network, Long Short-Term Memory Networks, and hybrid neural network models for time series forecasting.
- How to forecast univariate, multivariate, multi-step, and multivariate multi-step time series forecasting problems in general.
- How to transform sequence data into a three-dimensional structure in order to train convolutional and LSTM neural network models.
- How to grid search deep learning model hyperparameters to ensure that you are getting good performance from a given model.
- How to prepare data and develop deep learning models for forecasting a range of univariate time series problems with different temporal structures.
- How to prepare data and develop deep learning models for multi-step forecasting a real-world household electricity consumption dataset.
- How to prepare data and develop deep learning models for a real-world human activity recognition project.
This book will NOT teach you how to be a research scientist and all the theory behind why specific methods work. It will teach you how to get results and deliver value on your time series forecasting projects.
This new understanding of applied deep learning methods will impact your practice of working through time series forecasting problems in the following ways:
- Confidently use naive and classical methods like SARIMA and ETS to quickly develop robust baseline models for a range of different time series forecasting problems, the performance of which can be used to challenge whether more elaborate machine learning and deep learning models are adding value.
- Transform native time series forecasting data into a form for fitting supervised learning algorithms and confidently tune the amount of lag observations and framing of the prediction problem.
- Develop MLP, CNN, RNN, and hybrid deep learning models quickly for a range of different time series forecasting problems, and confidently evaluate and interpret their performance.
This book is not a substitute for an undergraduate course in deep learning or time series forecasting, nor is it a textbook for such courses, although it could be a useful complement. For a good list of top courses, textbooks, and other resources, see the Further Reading section at the end of each tutorial.
… so what is in the Ebook?
25 Step-by-Step Tutorials to Transform you into a
Deep Learning Time Series Practitioner
This book was designed around major deep learning techniques that are directly relevant to time series forecasting.
There are a lot of things you could learn about deep learning and time series forecasting, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials
I designed the tutorials to focus on how to get results with deep learning methods. The tutorials give you the tools to both rapidly understand and apply each technique or operation
Each of the lessons are 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 was intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.
The tutorials are divided into six parts:
- Part 1: Foundations. Provides a gentle introduction to the promise of deep learning methods for time series forecasting, a taxonomy of the types of time series forecasting problems, how to prepare time series data for supervised learning, and a high-level procedure for getting the best performing model on time series forecasting problems in general.
- Part 2: Deep Learning Modeling. Provides a step-by-step introduction to deep learning methods applied to different types of time series forecasting problems with additional tutorials to better understand the 3D-structure required for some models.
- Part 3: Univariate Forecasting. Provides a methodical approach to univariate time series forecasting with a focus on naive and classical methods that are generally known to out-perform deep learning methods and how to grid search deep learning model hyperparameters.
- Part 4: Multi-step Forecasting. Provides a step-by-step series of tutorials for working through a challenging multi-step time series forecasting problem for predicting household electricity consumption using classical and deep learning methods.
- Part 5: Time Series Classification. Provides a step-by-step series of tutorials for working through a challenging time series classification problem for
Table of Contents
Below is an overview of the 25 step-by-step tutorial lessons you will complete:
Each lesson was designed to be completed in about 30-to-60 minutes by the average developer.
Part 1: Foundations
- Lesson 01: Promise of Deep Learning for Time Series Forecasting
- Lesson 02: Taxonomy of Time Series Forecasting Problems
- Lesson 03: How to Develop a Skillful Forecasting Model
- Lesson 04: How to Transform Time Series to a Supervised Learning Problem
- Lesson 05: How to Prepare Time Series Data for CNNs and LSTMs
Part 2: Deep Learning Modeling
- Lesson 06: How to Prepare Time Series Data for CNNs and LSTMs
- Lesson 07: How to Develop MLPs for Time Series Forecasting
- Lesson 08: How to Develop CNNs for Time Series Forecasting
- Lesson 09: How to Develop LSTMs for Time Series Forecasting
Part 3: Univariate Forecasting
- Lesson 10: Review of Top Methods For Univariate Time Series Forecasting
- Lesson 11: How to Develop Simple Methods for Univariate Forecasting
- Lesson 12: How to Develop ETS Models for Univariate Forecasting
- Lesson 13: How to Develop SARIMA Models for Univariate Forecasting
- Lesson 14: How to Develop MLPs, CNNs and LSTMs for Univariate Forecasting
- Lesson 15: How to Grid Search Deep Learning Models for Univariate Forecasting
Part 4: Multi-step Forecasting
- Lesson 16: How to Load and Explore Household Energy Usage Data
- Lesson 17: How to Develop Naive Models for Multi-step Energy Usage Forecasting
- Lesson 18: How to Develop ARIMA Models for Multi-step Energy Usage Forecasting
- Lesson 19: How to Develop CNNs for Multi-step Energy Usage Forecasting
- Lesson 20: How to Develop LSTMs for Multi-step Energy Usage Forecasting
Part 5: Time Series Classification
- Lesson 21: Review of Deep Learning Models for Human Activity Recognition
- Lesson 22: How to Load and Explore Human Activity Data
- Lesson 23: How to Develop ML Models for Human Activity Recognition
- Lesson 24: How to Develop CNNs for Human Activity Recognition
- Lesson 25: How to Develop LSTMs for Human Activity Recognition
You can see that each part targets a specific learning outcome, and so does each tutorial within each part. This acts as a filter to ensure you are only focused on the things you need to know to get to a specific result and do not get bogged down in the math or near-infinite number of configuration parameters.
The tutorials were not designed to teach you everything there is to know about each of the techniques or time series forecasting problems. They were designed to give you an understanding of how they work, how to use them on your projects the fastest way I know how: to learn by doing.
Take a Sneak Peek Inside The Ebook
Click image to Enlarge.
…you’ll also get 131 fully working Python scripts
BONUS: Deep Learning Time Series Forecasting Code Recipes
Sample Code Recipes
Each recipe presented in the book is standalone, meaning that you can copy and paste it into your project and use it immediately.
- You get one Python script (.py) for each example provided in the book.
This means that you can follow along and compare your answers to a known working implementation of each example in the provided Python files.
This helps a lot to speed up your progress when working through the details of a specific task, such as:
- Preparing Data.
- Transforming Data.
- Defining Models.
- Fitting Models.
- Evaluating Models.
- Making Predictions.
The provided code was developed in a text editor and intended to be run on the command line. No special IDE or notebooks are required.
All code examples were tested with Python 3 and Keras 2.
All code examples will run on modest and modern computer hardware and were executed on a CPU. No GPUs are required to run the presented examples, although a GPU would make the code run faster.
Python Technical Details
This section provides some technical details about the code provided with the book.
- Python Version: You can use Python 3.
- SciPy: You will use NumPy, Pandas and scikit-learn.
- Keras: You will need Keras version 2 with either a Theano or TensorFlow backend.
- Operating System: You can use Windows, Linux or Mac OS X.
- Hardware: A standard modern workstation will do, no GPUs required.
- 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 setup a Python deep learning environment.
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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I recommend using standalone Keras version 2.3 (or higher) running on top of TensorFlow version 2.0 (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 are two cases 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 MTCNN for face detection and tutorials that use Mask-RCNN for object recognition. Once the third party libraries have 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.
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 you complete your purchase you will receive an email with a link to download your bundle.
The download will include the book or books and any bonus material.
There are no physical books, therefore no shipping is required.
All books are EBooks that you can download immediately after you complete your purchase.
I recommend reading one chapter per day.
Momentum is important.
Some readers finish a book in a weekend.
Most readers finish a book in a few weeks by working through it during nights and weekends.
You will get your book immediately.
After you complete and submit the payment form, you will be immediately redirected to a webpage with a link to download your purchase.
You will also immediately be sent an email with a link to download your purchase.
Generally, I would recommend starting with the book or topic that most interests you.
Nevertheless, one suggested order for reading the books is as follows:
- Probability for Machine Learning
- Statistical Methods for Machine Learning
- Linear Algebra for Machine Learning
- Master Machine Learning Algorithms
- Machine Learning Algorithms From Scratch
- Machine Learning Mastery With Weka
- Machine Learning Mastery With Python
- Machine Learning Mastery With R
- Time Series Forecasting With Python
- XGBoost With Python
- Deep Learning With Python
- Long Short-Term Memory Networks with Python
- Deep Learning for Natural Language Processing
- Deep Learning for Computer Vision
- Deep Learning for Time Series Forecasting
- Better Deep Learning
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.
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 registered and operated out of Australia.
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 an Australian Company Number or ACN. The details are as follows:
- Trading Name: Machine Learning Mastery Pty Ltd
- ACN: 626 223 336
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 and PayPal services to support secure and encrypted payment processing on my website.
Some common problems when customers have a problem include:
- Perhaps you can double check that your details are correct, just in case of a typo?
- Perhaps you could try a different payment method, such as PayPal or Credit Card?
- Perhaps you’re able to talk to your bank, just in case they blocked the transaction?
I often see customers trying to purchase with a domestic credit card that does not allow international purchases. This is easy to overcome by talking to your bank.
If you’re still having difficulty, please contact me and I can help investigate further.
I give away a lot of content for free. Most of it in fact.
It is important to me to help students and practitioners that are not well off, hence the enormous amount of free content that I provide.
You can access the free content:
I have thought very hard about this and I sell machine learning Ebooks for a few important reasons:
- I use the revenue to support the site and all the non-paying customers.
- I use the revenue to support my family so that I can continue to create content.
- Practitioners that pay for tutorials are far more likely to work through them and learn something.
- I target my books towards working professionals that are more likely to afford the materials.
All updates to the book or books in your purchase are free.
Books are usually updated once every few months to fix bugs, typos and keep abreast of API changes.
Contact me anytime and check if there have been updates. Let me know what version of the book you have (version is listed on the copyright page).
Please contact me anytime with questions about machine learning or the books.
One question at a time please.
Also, each book has a final chapter on getting more help and further reading and points to resources that you can use to get more help.
Yes, the books can help you get a job, but indirectly.
Getting a job is up to you.
It is a matching problem between an organization looking for someone to fill a role and you with your skills and background.
That being said, there are companies that are more interested in the value that you can provide to the business than the degrees that you have. Often, these are smaller companies and start-ups.
You can focus on providing value with machine learning by learning and getting very good at working through predictive modeling problems end-to-end. You can show this skill by developing a machine learning portfolio of completed projects.
My books are specifically designed to help you toward these ends. They teach you exactly how to use open source tools and libraries to get results in a predictive modeling project.
Do you have another question?