Statistical Methods for Machine Learning
Discover how to Transform Data into Knowledge with Python
Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it.
In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know.
Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what statistics is, the importance of statistical methods to machine learning, summary statistics, statistical hypothesis testing, nonparametric statistics, resampling methods, and much more.
About the Ebook:
- PDF format Ebook.
- 6 parts, covering the main topics.
- 29 step-by-step tutorials.
- 291 pages.
- 86 Python (.py) code files included.
Clear and Complete Examples.
Designed for Developers. Nothing Hidden.
Click to jump straight to the packages.
Why do we need Statistics?
Statistics is a collection of tools that you can use to get answers to important questions about data.
You can use descriptive statistical methods to transform raw observations into information that you can understand and share. You can use inferential statistical methods to reason from small samples of data to whole domains.
As a machine learning practitioner, you must have an understanding of statistical methods.
Raw observations alone are data, but they are not information or knowledge. Data raises questions, such as:
- What is the most common or expected observation?
- What are the limits on the observations?
- What does the data look like?
- What variables are most relevant?
- What is the difference between two experiments?
- Are the differences real or the result of noise in the data?
These Questions Are Important…
The results matter to the project, to stakeholders, and to effective decision making. Statistical methods are required to find answers to the questions that we have about data.
We can see that in order to both understand the data used to train a machine learning model and to interpret the results of testing different machine learning models, that statistical methods are required.
This is just the tip of the iceberg as each step in a predictive modeling project will require the use of a statistical method.
Why is Statistics Important to Machine Learning?
…it is needed at each step of a project
It would be fair to say that statistical methods are required to effectively work through a machine learning predictive modeling project.
Below are 10 examples of where statistical methods are used in an applied machine learning project.
- Problem Framing: Requires the use of exploratory data analysis and data mining.
- Data Understanding: Requires the use of summary statistics and data visualization.
- Data Cleaning. Requires the use of outlier detection, imputation and more.
- Data Selection. Requires the use of data sampling and feature selection methods.
- Data Preparation. Requires the use of data transforms, scaling, encoding and much more.
- Model Evaluation. Requires experimental design and resampling methods.
- Model Configuration. Requires the use of statistical hypothesis tests and estimation statistics.
- Model Selection. Requires the use of statistical hypothesis tests and estimation statistics.
- Model Presentation. Requires the use of estimation statistics such as confidence intervals.
- Model Predictions. Requires the use of estimation statistics such as prediction intervals.
The 3 Mistakes Made By Beginners
Once you discover the importance of statistics to machine learning, there are three key mistakes that beginners make:
1. Practitioners Don’t Know Stats
Developers don’t know statistics and this is a huge problem.
Programmers don’t need to know and use statistical methods in order to develop software. Software engineering and computer science courses generally don’t include courses on statistics, let alone advanced statistical tests. As such, it is common for machine learning practitioners coming from the computer science or developer tradition to not know and not value statistical methods.
This is a problem given the pervasive use of statistical methods and statistical thinking in the preparation of data, evaluation of learned models, and all other steps in a predictive modeling project.
2. Practitioners Study The Wrong Stats
Often, machine learning practitioners cotton-on to the need for skills in statistics.
This might start with a need to better interpret descriptive statistics or data visualizations and may progress to the need to start using sophisticated hypothesis tests. The problem is, they don’t seek out the statistical information they need.
Instead, they try to read through a text book on statistics or work through the material for an undergraduate course on statistics.
This approach is slow, it’s boring, and it covers a breadth and depth of material on statistics that is beyond the needs of the machine learning practitioner.
3. Practitioners Study Stats The Wrong Way
It’s worse than this.
Regardless of the medium used to learn statistics, be it books, videos, or course material, machine learning practitioners study statistics the wrong way.
Because the material is intended for undergraduate students that need to pass a test, the material is focused on the theory, on proofs, on derivations. This is great for testing students but terrible for practitioners that need results.
Practitioners need methods that clearly state when they are appropriate and instruction on how to interpret the result.
They need code examples that they can use immediately on their project.
A Better Way into Statistics
I am frustrated at seeing practitioner after practitioner diving into statistics textbooks and online courses designed for undergraduate students and giving up.
The bottom-up approach is hard, especially if you already have a full time job.
Statistics is not only important to machine learning, but it is also a lot of fun, or can be if it is approached in the right way.
I want to help you see the field the way I see it: as just another set of tools we can harness on our journey toward machine learning mastery.
Introducing My New Ebook:
“Statistical Methods for Machine Learning“
Welcome to the book: “Statistical Methods for Machine Learning“.
I designed this book to teach machine learning practitioners, like you, step-by-step the basics of statistical methods with concrete and executable examples in Python.
I set out to write a playbook for machine learning practitioners that gives you only those parts of statistics that they need to know in order to work through a predictive modeling project.
I set out to present statistical methods in the way that practitioners learn-that is with simple language and working code examples.
Statistics is important to machine learning, and I believe that if it is taught at the right level for practitioners, that it can be a fascinating, fun, directly applicable, and immeasurably useful area of study.
I hope that you agree.
Who Is This Book For?
…so is this book right for YOU?
This book is for developers that may know some applied machine learning.
Maybe you know how to work through a predictive modeling problem end-to-end, or at least most of the main steps, with popular tools.
The lessons in this book do assume a few things about you, such as:
- You may know your way around basic Python for programming.
- You may know some basic NumPy for array manipulation.
- You want to learn statistical methods to deepen your understanding and application of machine learning.
This guide was written in the top-down and results-first machine learning style that you’re used to from Machine Learning Mastery.
But, what if…?
What if I Am New to Machine Learning?
This book does not assume you have a background in machine learning.
That being said, I do recommend that you learn how to work through a predictive modeling problem first. It will give you the context for statistics. Otherwise the topic will feel too abstract.
What if I Am Just a Developer?
Perfect. I wrote this book for you.
What if My Math is Really Poor?
Maybe you learned statistics a long time ago back in school?
Maybe you never covered statistics before.
Perfect. This book is for you. I assume you know some basic arithmetic, and even then I give you a refresher.
What if I Am Not a Python Programmer?
You can handle this book if you are a programmer in another language, even if you are not experienced in Python.
Everything is demonstrated with a small code example that you can run directly.
All code is provided for you to play with, modify, and learn from.
The book even has an appendix to show you how to set up Python on your workstation.
What if I Am Working Through a Statistics Course at a University?
This book is not a substitute for an undergraduate course in statistics or a textbook for such a course, although it is a great complement to such materials.
About Your Outcomes
…so what will YOU know after reading this book?
After reading and working through this book, you will know:
- About the field of statistics, how it relates to machine learning, and how to harness statistical methods on a machine learning project.
- How to calculate and interpret common summary statistics and how to present data using standard data visualization techniques.
- Findings from mathematical statistics that underlie much of the field, such as the central limit theorem and the law of large numbers.
- How to evaluate and interpret the relationship between variables and the independence of variables.
- How to calculate and interpret parametric statistical hypothesis tests for comparing two or more data samples.
- How to calculate and interpret interval statistics for distributions, population parameters, and observations.
- How to use statistical resampling to make good economic use of available data in order to evaluate predictive models.
- How to calculate and interpret nonparametric statistical hypothesis tests for comparing two or more data samples that do not conform to the expectations of parametric tests.
This new basic understanding of statistical methods will impact your practice of machine learning in the following ways:
After reading this book, you will be able to:
- Use descriptive statistics and data visualizations to quickly and more deeply understand the shape and relationships in data.
- Use inferential statistical tests to quickly and effectively quantify the relationships between samples, such as the results of experiments with different predictive algorithms or differing configurations.
- Use estimation statistics to quickly and effectively quantify the confidence in estimated model skill and model predictions.
What Exactly Is in This Book?
…see the table of contents
This book was designed to be a crash course in statistical methods for machine learning practitioners. Ideally, those with a background as a developer.
This book was designed around major ideas and methods that are directly relevant to machine learning algorithms.
There are a lot of things you could learn about statistics, 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 things done with statistics. 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.
6-Part Book Overview
The tutorials are divided into six parts, they are:
- Part 1: Statistics. Provides a gentle introduction to the field of statistics, the relationship to machine learning, and the importance that statistical methods have when working through a predictive modeling problem.
- Part 2: Foundation. Introduction to descriptive statistics, data visualization, random numbers, and important findings in statistics such as the law of large numbers and the central limit theorem.
- Part 3: Hypothesis Testing. Covers statistical hypothesis tests for comparing populations of samples and the interpretation of tests with p-values and critical values.
- Part 4: Resampling. Covers methods from statistics used to economically use small samples of data to evaluate predictive models such as k-fold cross-validation and the bootstrap.
- Part 5: Estimation Statistics. Covers an alternative to hypothesis testing called estimation statistics, including tolerance intervals, confidence intervals, and prediction intervals.
- Part 6: Nonparametric Methods. Covers nonparametric statistical hypothesis testing methods for use when data does not meet the expectations of parametric tests.
Below is an overview of the 29 step-by-step tutorial lessons you will work through:
Each lesson was designed to be completed in about 30-to-60 minutes by an average developer.
Part I: Statistics
- Lesson 01: Introduction to Statistics
- Lesson 02: Statistics vs Machine Learning
- Lesson 03: Examples of Statistics in Machine Learning
Part II: Foundation
- Lesson 04: Gaussian and Summary Stats
- Lesson 05: Simple Data Visualization
- Lesson 06: Random Numbers
- Lesson 07: Law of Large Numbers
- Lesson 08: Central Limit Theorem
Part III: Hypothesis Testing
- Lesson 09: Statistical Hypothesis Testing
- Lesson 10: Statistical Distributions
- Lesson 11: Critical Values
- Lesson 12: Covariance and Correlation
- Lesson 13: Significance Tests
- Lesson 14: Effect Size
- Lesson 15: Statistical Power
Part IV: Resampling Methods
- Lesson 16: Introduction to Resampling
- Lesson 17: Estimation with Bootstrap
- Lesson 18: Estimation with Cross-Validation
Part V: Estimation Statistics
- Lesson 19: Introduction to Estimation Statistics
- Lesson 20: Tolerance Intervals
- Lesson 21: Confidence Intervals
- Lesson 22: Prediction Intervals
Part VI: Nonparametric Methods
- Lesson 23: Rank Data
- Lesson 24: Normality Tests
- Lesson 25: Make Data Normal
- Lesson 26: 5-Number Summary
- Lesson 27: Rank Correlation
- Lesson 28: Rank Significance Tests
- Lesson 29: Independence Test
- Appendix A: Getting Help
- Appendix B: How to Setup a Workstation for Python
- Appendix C: Basic Math Notation
You can see that each part targets a specific learning outcome, and so does each tutorial within each part. This acts as a filter to ensure you are only focused on the things you need to know to get to a specific result and do not get bogged down in the math or near-infinite number of digressions.
Table of Contents
The screenshot below was taken from the PDF Ebook. It provides you a full overview of the table of contents from the book.
The tutorials were not designed to teach you everything there is to know about each of the theories or techniques of statistics. They were designed to give you an understanding of how they work, how to use them, and how to interpret the results the fastest way I know how: to learn by doing.
Here’s Everything You’ll Get…
in Statistical Methods for Machine Learning
A digital download that contains everything you need, including:
- Clear descriptions to help you understand the statistical methods required for applied machine learning.
- Step-by-step Python tutorials to show you exactly how to apply each technique and algorithm.
- End-to-end self-contained examples that give you everything you need in each tutorial with out assuming prior knowledge.
- Python source code recipes for every example in the book so that you can run the tutorial code in seconds.
- Digital Ebook in PDF format so that you can have the book open side-by-side with the code and see exactly how each example works.
Resources you need to go deeper, when you need to, including:
- The best sources of information on the Python ecosystem including the SciPy, NumPy, Matplotlib, Statsmodels and scikit-learn libraries.
- The best places online where you can ask your challenging questions and actually get a response.
- The best books, research papers, and articles to learn more about each statistical method covered.
Background on the field of statistics to give you the context you need, including:
- How knowledge of statistics is a prerequisite for modern machine learning books an courses.
- Why statistics is required for transforming data into knowledge.
- How machine learning and statistics are two very closely related perspectives on the same tasks.
- How applied statistics must harness machine learning and machine learning must harness statistics.
- How the use of statistical methods is required at each step of a predictive modeling project.
Foundations required to understand and use statistical methods, including:
- The importance of the Gaussian distribution and how to summarize any Gaussian using two parameters.
- Simple charts and graphs to rapidly understand a sample of data.
- The role of randomness in machine learning and how to generate and use random numbers.
- The law of large numbers that supports the intuition that larger samples give more accurate statistics.
- The amazing finding of the central limit theorem that supports much of modern statistical methods.
Parametric statistical hypothesis testing methods required to associate and compare data samples, including:
- The role of statistical hypothesis testing and how to correctly interpret the p-value.
- Different data distributions and how to recognized their probability and cumulative density functions.
- Critical values in common data distributions and how to use them to interpret statistical tests.
- How to quantify the association between variables by calculating a correlation coefficient.
- How to check if samples are drawn from the same or different distributions using hypothesis tests.
- The quantification of the difference between data samples using effect size calculations.
- The quantifying of the statistical power of a test and its use in estimating sample sizes.
The efficient use of small samples in estimating statistical quantities, including:
- The important role and differences between data sampling and data resampling.
- The use of the bootstrap method to estimate population variables.
- The use of the k-fold cross-validation method to estimate the skill of predictive models on small samples.
The quantification of results through the use of estimation statistics, called the “new statistics“, including:
- The alternative and complementary methods to hypothesis testing called estimation statistics.
- The calculation of tolerance intervals to quantify expected range of values from a data distribution.
- The calculation of confidence intervals to quantify the expected range of values for a population parameter.
- The calculation of prediction intervals to quantify the expected range of values for a predicted point value.
Statistical hypothesis tests to use when data is not Gaussian or nonparametric, including:
- How to transform data using a ranking in order to work with distribution free statistical methods.
- Techniques that can be used to determine whether a data sample is likely drawn from a Gaussian distribution or not called normality testing.
- Methods that can be used to correct or transform a data sample that is nearly-Gaussian to be Gaussian.
- Summary statistics to describe a data sample for data with any distribution.
- How to calculate correlation coefficients for rank data.
- Nonparametric statistical hypothesis tests for comparing data samples regardless of their distribution.
- A statistical method to check whether a categorical input variable is associated with a categorical output variable.
What More Do You Need?
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.
BONUS: Statistical Methods Python Code Recipes
…you also get 86 fully working Python scripts
Sample Code Recipes
Each recipe presented in the book is standalone, meaning that you can copy and paste it into your project and use it immediately.
- You get one Python script (.py) for each example provided in the book.
This means that you can follow along and compare your answers to a known working implementation of each example in the provided Python files.
This helps a lot to speed up your progress when working through the details of a specific task, such as:
- Calculate descriptive statistics.
- Calculate the association between variables.
- Parametric statistical methods.
- Nonparametric statistical methods.
- Resampling statistical methods
The provided code was developed in a text editor and is intended to be run on the command line. No special IDE or notebooks are required.
All code examples were designed and tested with Python 3.6+.
All code examples will run on modest and modern computer hardware and were executed on a CPU.
Python Technical Details
This section provides some technical details about the code provided with the book.
- Python Version: You can use Python 3.6 or higher.
- SciPy: You will use NumPy, SciPy and Statsmodels APIs.
- 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 a step-by-step tutorial showing you exactly how to set up a Python machine learning environment.
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Are you a Student, Teacher or Retiree?
Do you have any Questions?
About The Author
Hi, I'm Jason Brownlee.
I live in Australia with my wife and son and love to write and code.
I have a computer science background as well as a Masters and Ph.D. degree in Artificial Intelligence.
I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones. (yes I have written tons of code that runs operationally)
I get a lot of satisfaction helping developers get started and get really good at 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 Use Training To 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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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.
After reading and working through the tutorials you are far more likely to use what you have learned.
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.
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.
That being said, the examples can be adapted for time series forecasting.
There is a section on showing you how load multivariate inputs and there is a tutorial showing you how to handle multivariate inputs.
There is also a section cautioning that to-date results of LSTMs on autoregression problems have not been stellar.
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:
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All prices are in US dollars (USD).
Books can be purchased with 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:
- Linear Algebra for Machine Learning
- Statistical Methods 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
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.
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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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.
The book “Long Short-Term Memory Networks With Python” focuses on how to implement different types of LSTM models.
The book “Deep Learning for Natural Language Processing” focuses on how to use a variety of different networks (including LSTMs) for text prediction problems.
The LSTM book can support the NLP book, but it is not a prerequisite.
Linux, MacOS, and Windows.
There are no code examples in “Master Machine Learning Algorithms“, therefore no programming language is used.
Algorithms are described and their working is summarized using basic arithmetic. The algorithm behavior is also demonstrated in excel spreadsheets, that are available with the book.
It is a great book for learning how algorithms work, without getting side-tracked with theory or programming syntax.
If you are interested in learning about machine learning algorithms by coding them from scratch (using the Python programming language), I would recommend a different book:
I write the content for the books (words and code) using a text editor, specifically sublime.
I typeset the books and create a PDF using LaTeX.
All of the books, except one, 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.
There is one book that required Python 2.7, that is:
This book will be updated for Python 3 soon.
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:
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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.