# Probability for Machine Learning

## Discover How To Harness Uncertainty With Python

Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it.

Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know.

Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.

About this Ebook:

**Read on all devices**: English PDF format EBook, no DRM.**Tons of tutorials**: 28 step-by-step lessons, 312 pages.**Foundations**: distributions, estimation, more.**Working code**: 74 Python (.py) code files included.

#### Clear, Complete End-to-End Examples.

Convinced?

Click to jump straight to the packages.

## Machine Learning DOES NOT MAKE SENSE Without Probability

### What is Probability?

*…it’s about handling uncertainty*

Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world.

Handling uncertainty is typically described using everyday words like chance, luck, and risk.

Probability is a field of mathematics that gives us the language and tools to quantify the uncertainty of events and reason in a principled manner.

We can assign and quantify the likelihood of things we care about, such as outcomes, events, or numerical values.

### Why is Probability Important to Machine Learning?

*…it is needed at each step of a project*

It would be fair to say that probability is required to effectively work through a machine learning predictive modeling project.

Machine learning is about developing predictive models from uncertain data. Uncertainty means working with imperfect or incomplete information.

Uncertainty is fundamental to the field of machine learning, yet it is one of the aspects that causes the most difficulty for beginners, especially those coming from a developer background.

There are three main sources of uncertainty in machine learning, they are: noisy data, incomplete coverage of the problem domain and imperfect models.

**Nevertheless, we can manage uncertainty using the tools of probability**.

As machine learning practitioners, we must have an understanding of probability in order to manage the uncertainty we see in each project.

*in fact…*

Probability is the Bedrock of Machine Learning

- Classification models must predict a probability of class membership.
- Algorithms are designed using probability (e.g. Naive Bayes).
- Learning algorithms will make decisions using probability (e.g. information gain).
- Sub-fields of study are built on probability (e.g. Bayesian networks).
- Algorithms are trained under probability frameworks (e.g. maximum likelihood).
- Models are fit using probabilistic loss functions (e.g. log loss and cross entropy).
- Model hyperparameters are configured with probability (e.g. Bayesian optimization).
- Probabilistic measures are used to evaluate model skill (e.g. brier score, ROC).
*…the list could go on*

## The 3 Mistakes Made By Beginners

Once you discover the importance of probability to machine learning, there are three key mistakes that beginners make:

#### 1. Beginners Don’t Understand Probability

Developers don’t know probability and this is a huge problem.

Programmers don’t need to know and use probability in order to develop software. Software engineering and computer science courses focus on deterministic programs, with inputs, outputs, and no randomness or noise.

As such, it is common for machine learning practitioners coming from the computer science or developer tradition to not know and not value probabilistic thinking.

This is a problem given the bedrock of a predictive modeling project is probability.

#### 2. Beginners Study The Wrong Probability

Eventually, machine learning practitioners realize the need for skills in probability.

This might start with a need to better interpret descriptive statistics and may progress to the need to understand the probabilistic frameworks behind many popular machine learning algorithms.

The problem is, they don’t seek out the probability information they need. Instead, they try to read through a textbook on probability or work through the material for an undergraduate course on probabilistic methods.

This approach is slow, it’s boring, and it covers the breadth and depth of material on probability that is beyond the needs of the machine learning practitioner.

#### 3. Beginners Study Probability The Wrong Way

It’s worse than this.

Regardless of the medium used to learn probability, be it books, videos, or course material, machine learning practitioners study probability the wrong way.

Because the material is intended for undergraduate students that need to pass a test, the material is focused on the math, theory, proofs, and 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 intuitions behind the complex equations. They need code examples that they can use immediately on their project.

### A Better Way into Probability

I am frustrated at seeing practitioner after practitioner diving into probability 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.

Probability 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:

“*Probability for Machine Learning*“

Welcome to the EBook: **Probability for Machine Learning**.

I designed this book to teach machine learning practitioners, like you, step-by-step the basics of probability 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 probability that you need to know in order to work through a predictive modeling project.

I set out to present techniques from probability in the way that practitioners learn-that is with *simple language and ** working code examples*.

Probability 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.

**Convinced?**

Click to jump straight to the packages.

## But, *what if…?*

Do you have some doubts? Let me see if I can help.

**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 probability. Otherwise the topic may 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 probability a long time ago back in school?

Maybe you never covered probability 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 Probability Course at a University?**

Excellent!

This book is not a substitute for an undergraduate course in probability 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 probability, how it relates to machine learning, and how to harness probabilistic thinking on a machine learning project.
- How to calculate different types of probability, such as joint, marginal, and conditional probability.
- How to consider data in terms of random variables and how to recognize and sample common discrete and continuous probability distribution functions.
- How to frame learning as maximum likelihood estimation and how this important probabilistic framework is used for regression, classification and clustering machine learning algorithms.
- How to use probabilistic methods to evaluate machine learning models directly without evaluating their performance on a test dataset.

- How to consider probability from the Bayesian perspective and to calculate conditional probability with Bayes theorem for common scenarios.
- How to use Bayes theorem for classification with Naive Bayes, optimization with Bayesian Optimization, and graphical models with Bayesian Networks.
- How to quantity uncertainty using measures of information and entropy from the field of information theory and calculate quantiles such as cross entropy and mutual information.
- How to develop and evaluate naive classifiers using a probabilistic framework.
- How to evaluate classification models that predict probabilities and calibrate predictions.

This new basic understanding of probability will impact your practice of machine learning in the following ways:

After reading this book, you will be able to:

- Confidently calculate and wield both frequentist probability (counts) and Bayesian probability (beliefs) generally and within the context of machine learning datasets.
- Confidently select and use loss functions and performance measures when training machine learning algorithms, backed by a knowledge of the underlying probabilistic framework (e.g. maximum likelihood estimation) and the relationships between metrics (e.g. cross entropy and negative log likelihood).
- Confidently evaluate classification predictive models including establishing a robust baseline in performance, probabilistic performance measures and calibrated predicted probabilities.

## What Exactly Is in This Book?

*…see the table of contents*

This book was designed to be a crash course in probability 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 probability, 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 probability. They give you the tools to both rapidly understand and apply each technique or operation.

Each tutorial is designed to take you less than 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.

### 7-Part Book Overview

**Part 1: Foundations**. Discover a gentle introduction to the field of probability, the relationship to machine learning and the importance that probability has when working through predictive modeling problems.**Part 2: Basics**. Discover the different types of probability such as marginal, joint, and conditional, and worked examples that develop an intuition for how to calculate each.**Part 3: Distributions**. Discover probability distributions that the likelihood of events and common distribution functions for discrete and continuous random variables.**Part 4: Maximum Likelihood**. Discover the maximum likelihood estimation probabilistic framework that underlies how the parameters of many machine learning algorithms are fit on training data.**Part 5: Bayesian Probability**. Discover Bayes theorem and some of the most important uses in applied machine learning such as the naive Bayes algorithm and Bayesian optimization.**Part 6: Information Theory**. Discover the relationships between probability and information theory and some of the most important concepts to applied machine learning such as cross entropy and information gain.**Part 7: Classification**. Discover the relationships between classification and probability, including models that predict probabilities for class labels, evaluation metrics, and probability calibration.

### Lessons Overview

Below is an overview of the 28 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: Background

**Lesson 01**: What is Probability**Lesson 02**: Uncertainty in Machine Learning**Lesson 03**: Why Learn Probability for Machine Learning

#### Part II: Foundations

**Lesson 04**: Joint, Marginal, and Conditional Probability**Lesson 05**: Worked Examples of Probability**Lesson 06**: Advanced Examples of Probability

#### Part III: Distributions

**Lesson 07**: Probability Distributions**Lesson 08**: Discrete Probability Distributions**Lesson 09**: Continuous Probability Distributions**Lesson 10**: Density Estimation

#### Part IV: Maximum Likelihood

**Lesson 11**: Maximum Likelihood Estimation**Lesson 12**: Regression with Maximum Likelihood**Lesson 13**: Classification with Maximum Likelihood**Lesson 14**: Expectation Maximization**Lesson 15**: Probabilistic Model Evaluation

#### Part V: Bayesian Probability

**Lesson 16**: Bayes Theorem**Lesson 17**: Bayes Theorem and Machine Learning**Lesson 18**: Naive Bayes Classifier**Lesson 19**: Bayesian Optimization**Lesson 20**: Bayesian Belief Networks

#### Part VI: Information Theory

**Lesson 21**: Entropy**Lesson 22**: KL Divergence**Lesson 23**: Cross-entropy**Lesson 24**: Information Gain

#### Part VII: Classification

**Lesson 25**: Naive Classifiers**Lesson 26**: Probability Scores**Lesson 27**: ROC and Precision-Recall Curves**Lesson 28**: Calibrate Predicted Probabilities

### Appendix

**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.

The tutorials were not designed to teach you everything there is to know about each of the theories or techniques of probability. 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.

### EBook 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.

## Here’s Everything You’ll Get…

in “*Probability for Machine Learning*“

**A digital download that contains everything you need, including:**

- Clear descriptions to help you understand the probability 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 without 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, and scikit-learn libraries.
- The best places online where you can ask your challenging questions and actually get a response.
- The best books, and articles to learn more about each probabilistic method covered.

**Background on the field of probability to give you the context you need, including:**

- How probability is a field of mathematics concerned with quantifying and harnessing uncertainty.
- How there are two schools of probability, Frequentist Probability and Bayesian Probability.
- How there are three main sources of uncertainty in machine learning, noisy data, incomplete coverage, and imperfect models.
- How probability provides the tools in applied machine learning for managing the uncertainty.
- How probability is used in each step of a predictive modeling project from understanding data, training models to predicting probabilities.

**Foundations required to understand and calculate probability, including:**

- That probability is assigned to events for a random variable.
- How to calculate the joint probability between events.
- How to calculate the marginal probability for events.
- How to calculate the conditional probability for events given the occurrence of other events.
- The difference between dependent and independent probability.
- How to calculate probability for mutually exclusive events.
- How to put the calculation of probability into practice with worked examples.

**Continuous and discrete probability distributions for relating the occurrence of events with probabilities, including:**

- How to tell the difference between discrete, boolean and continuous random variables.
- How to describe the difference between probability distribution functions, probability mass functions, and cumulative distribution functions.
- How to sample discrete probability distributions like the Bernoulli, Binomial, Multinoulli and Multinomial distributions.
- How to sample continuous probability distributions like the Normal (Gaussian), Exponential and Pareto distributions.
- How to summarize the probability density with a histogram and a kernel density estimation (KDE) model.

**How to use the maximum likelihood estimation probabilistic framework to fit machine learning models, including:**

- How maximum likelihood estimation is a framework for optimizing a distribution function and parameters to best describe observed data.
- How maximum likelihood underlies many popular machine learning algorithms including artificial neural networks.
- How to optimize a linear regression model under maximum likelihood estimation.
- How to optimize a logistic regression model for classification under maximum likelihood estimation.
- How to optimize a density estimation model with latent variables under maximum likelihood estimation.
- How to use probabilistic measures like AIC, BIC, and MLD to evaluate a model without a test dataset.

**How to understand and harness the power of Bayes theorem for a range of tasks, including:**

- How to use Bayes theorem to calculate conditional probability for machine learning.
- How full Bayesian classification model can be dramatically simplified, called Naive Bayes, and still be remarkably effective.
- How Bayes theorem can be used to solve challenging optimization problems like tuning the hyperparameters of machine learning algorithms.
- How Bayes theorem can be used as the basis for developing probabilistic graphical models for inference, called Bayesian Belief Networks.
- How the Maximum a Posteriori (MAP) Bayesian probabilistic framework can be used as an alternative to maximum likelihood for fitting models.

**How information theory is built upon probability and how techniques from the field are used in machine learning, including:**

- How information quantifies the amount of surprise for an event, and entropy quantifies the information content of a random variable.
- How information gain calculates the reduction in the surprise of a variable and can be used in the construction of decision trees and for feature selection where it is called mutual information.
- How cross entropy calculates the average total bits required to encode a random variable with one distribution compared to another distribution, and how it is the same as the negative log likelihood from maximum likelihood estimation.
- How KL divergence calculates the average extra bits required to encode a random variable with one distribution compared to another, and is often referred to as relative entropy.

**How probability provides is a required property when working on classification predictive modeling projects, including:**

- How to develop a range of different naive classification models and evaluate their expected performance using a probabilistic framework.
- How to evaluate the performance of a classification model that predicts probabilities using metrics like log loss and Brier score.
- How to evaluate and choose the threshold using ROC curves for a classification model that predicts probabilities.
- How to review predicted probabilities with a reliability diagram and calibrate predicted probabilities for a classification model.

**What More Do You Need?**

## Take a Sneak Peek Inside The EBook

*Click an image to Enlarge.*

## BONUS: Probability Python Code Recipes

*…you also get 74 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:

- Sample and plot probability distributions.
- Develop a Naive Bayes classifier from scratch.
- Develop a Bayesian optimization from scratch.
- Calculate cross entropy from scratch.
- Calculate information gain from scratch.
- Evaluate models with AIC and BIC metrics from scratch.
- Develop and evaluate naive classifier models.
- Calculate metrics like brier score and ROC curves.
- Calibrate predicted probabilities.

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 scikit-learn APIs.**OS**: You can use Windows, Linux, or Mac OS X.**Hardware**: A standard modern workstation will do.**Editor**: You can use a text editor and run the example from the command line.

**Don’t have a Python environment?**

No problem!

The appendix contains a step-by-step tutorial showing you exactly how to set up a Python machine 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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## What Are Skills in Machine Learning Worth?

Your boss asks you:

*Hey, can you build a predictive model for this?*

#### Imagine you had the skills and confidence to say:

"*YES!*"

...and follow through.

I have been there. It feels great!

### How much is that worth to you?

The industry is demanding skills in machine learning.

The market wants people that can deliver results, not write academic papers.

Business knows what these skills are worth and are paying 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.*

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## 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...

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Generally no.

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.

No.

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.

No.

My books do not cover the theory or derivations of machine learning methods.

This is by design.

My books are focused on the practical concern of applied machine learning. Specifically, how algorithms work and how to use them effectively with modern open source tools.

If you are interested in the theory and derivations of equations, I recommend a machine learning textbook. Some good examples of machine learning textbooks that cover theory include:

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.

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.

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.

Mini-courses are:

**Short**, typically 7 days or 14 days in length.**Terse**, typically giving one tip or code snippet per lesson.**Limited**, typically narrow in scope to a few related areas.

Ebooks are provided on many of the same topics providing full training courses on the topics.

Ebooks are:

**Longer**, typically 25+ complete tutorial lessons, each taking up to an hour to complete.**Complete**, providing a gentle introduction into each lesson and includes full working code and further reading.**Broad**, covering all of the topics required on the topic to get productive quickly and bring the techniques to your own projects.

The **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.

Generally, no.

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.

### Discounts

I do offer discounts to students, teachers and retirees.

Please contact me to find out more.

### Free Material

I offer a ton of free content on my blog, you can get started with my best free material here:

### About my Books

My books are playbooks.

They are intended for developers who want to know how to use a specific library to actually solve problems and deliver value at work.

- My books guide you only through the elements you need to know in order to get results.
- My books are in PDF format and come with code and datasets, specifically designed for you to read and 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.

Yes.

All updates to the book or books in your purchase are free.

Books are usually updated once every few months to fix bugs, typos and keep abreast of API changes.

Contact me anytime and check if there have been updates. Let me know what version of the book you have (version is listed on the copyright page).

Yes.

Please contact me anytime with questions about machine learning or the books.

One question at a time please.

Also, each book has a final chapter on getting more help and further reading and points to resources that you can use to get more help.

Yes, the books can help you get a job, but indirectly.

Getting a job is up to you.

It is a matching problem between an organization looking for someone to fill a role and you with your skills and background.

That being said, there are companies that are more interested in the value that you can provide to the business than the degrees that you have. Often, these are smaller companies and 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.