Optimization for Machine Learning
Finding Function Optima with Python
Optimization happens everywhere. Machine learning is one example of such and gradient descent is probably the most famous algorithm for performing optimization. Optimization means to find the best value of some function or model. That can be the maximum or the minimum according to some metric.
Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will learn how to find the optimum point to numerical functions confidently using modern optimization algorithms.
About this Ebook:
- Read on all devices: English PDF format EBook, no DRM.
- Tons of tutorials: 30 step-by-step lessons, 412 pages.
- Foundations: functional optimization, how to choose an algorithm, and more.
- Many algorithms: Nelder-Mead, BFGS, simulated annealing, RMSProp, and more.
- Working code: 104 Python (.py) code files included.
Clear, Complete End-to-End Examples.
…so What is Function Optimization?
Function optimization is to find the maximum or minimum value of a function. The function may have any structure as long as it produces numerical values.
If we got a function as a blackbox how can we find its maximum or minimum? We can check out every single possible input to see which one will give the best output, or we can assume the function might behave in certain manner, such as output is continuous to its input, and exploit this by, for example, hill-climbing. There are vast amount of optimization algorithms, each was proposed together with certain assumptions or heuristics. The most suitable one for a particular function may not fit another.
…so Why do we care about Function Optimization?
In applied machine learning, we can construct a function that is a blackbox model with a predefined set of training and test data. The input to this function are the model’s hyperparameters and the output is the evaluation score. So we are looking for what hyperparameters can produce the best score. Or, the function can be the model itself, and we are looking for what weight parameters to produce the lowest error rate for that given dataset.
These are just a few examples of how function optimization is related to machine learning. In fact, when the computer busy working on training the machine learning model, it is the optimization algorithm in action. When human is involved, to decide on what kind of model to use and how to configure or set up the model, we are also doing an optimization at the higher level without noticing it.
Nevertheless, there are 2 main reasons to we need to learn function optimization, they are:
The ability to apply function optimization freely allows us to go to a new level in various stages of machine learning. We may find better models by hyperparameter tuning. We may also produce better input data by feature selection in preprocessing stage.
Optimization algorithms allow us to use machine learning to its potential.
Machine learning is doing a lot of optimization behind the scenes. When new algorithm invented or new technique proposed, it is inevitable to explain them in terms of optimization. Hence we also need to understand them from optimization perspectives.
Knowledgable in optimization algorithms allow us to communicate the action of machine learning better.
The best illustration of these is from the history of development in neural network models. When we started with gradient descent and later we have Adam algorithm to use, it is only possible to understand the reason for this progression if you understand function optimization.
Introducing My New EBook:
“Optimization for Machine Learning“
Welcome to the EBook: Optimization for Machine Learning.
I designed this book to teach machine learning practitioners, like you, step-by-step how to use the most common function optimization algorithms with examples in Python.
This book was carefully designed to help you bring a wide variety of the proven and powerful optimization algorithms to your next project.
The tutorials were designed to teach you these techniques the fastest and most effective way that I know how: to learn by doing. With executable code that you can run to develop the intuitions required, and that you can copy-and-paste into your project and immediately get a result.
Function optimization is important to machine learning, and I believe that if it is taught at the right level for practitioners, it can be a fascinating, fun, directly applicable, and immeasurably useful toolbox of techniques.
I hope that you agree.
Click to jump straight to the packages.
…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 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.
This guide was written in the top-down and results-first machine learning style that you’re used to from Machine Learning Mastery.
The lessons in this book do assume a few things about you, such as:
You need to know:
- You know your way around basic Python for programming.
- You may know some basic NumPy for array manipulation.
- You may know some basic scikit-learn for modeling.
You do NOT need to be:
- You do not need to be a math wiz!
- You do not need to be a master programmer!
- You do not need to be a machine learning expert!
About Your Outcomes
…so what will YOU know after reading this book?
After reading and working through this book, you will know:
- The intuition behind optimizing a function without knowing the details of how the function computed its value.
- How the nature of an objective function affects the applicability of an optimization algorithm.
- The trade-offs made in applying different optimization algorithm.
- The difference between local and global optimization, and between deterministic and stochastic optimization.
- Different techniques to monitor the progress of optimization algorithm in action.
- How use SciPy to optimize your own objective function using various optimization algorithms.
- How to develop and apply heuristic optimization algorithms such as hill climbing and simulated annealing.
- How to develop and apply evolutionary optimization such as genetic algorithms.
- How to implement gradient descent and its many variations and apply it on your own objective function.
- How the optimization algorithms can help developing a machine learning solution in various aspects.
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 the challenge of optimization.
What if I Am Just a Developer?
Perfect. I wrote this book for you.
What if My Math is Really Poor?
Perfect. This book is for you.
No complicated math used, only the concept of functions and some basic arithmetic.
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 another Machine Learning Course?
This book is not a substitute for an undergraduate course in machine learning or a textbook for such a course, although it is a great complement to such materials.
What Exactly Is In This Book?
…see the table of contents
This book was designed around major function optimization techniques that are directly relevant to real-world problems.
There are a lot of things you could learn about function optimization, 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.
The tutorials were designed to teach you how to get results with function optimization algorithms. As such, the tutorials give you the tools to both rapidly understand and apply each technique or operation. There is a mixture of both tutorial lessons and projects to both introduce the methods and give plenty of examples and opportunities to practice using them.
Each of the tutorials is designed to take you about one hour to read through and complete, excluding the extensions and further reading.
You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle.
I would recommend picking a schedule and sticking to it.
The tutorials are divided into six parts; they are:
- Part 1: Foundation: Discover the nature of function optimization, why they are important to machine learning and how to develop an intuition for what is being optimized.
- Part 2: Background: Discover the background required for understanding the process the outcome of function optimization, including the broad categories of optimization algorithms, and how to visualize the progress.
- Part 3: Local Optimization: Discover various local optimization techniques, and the difference in the requirements of several algorithms.
- Part 4: Global Optimization: Discover several global optimization algorithms that would not be trapped by “local optima” but rather to have potential to look for the optimal solution in the entire function domain.
- Part 5: Gradient Descent: Discover gradient descent, the most famous optimization algorithm in machine learning, with its weakness and the many variations that aimed to alleviate it.
- Part 6: Projects: Discover the way function optimization can be used in practice through examples.
Below is an overview of the 30 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 1: Foundation
- Lesson 01: What is Function Optimization
- Lesson 02: Optimization and Machine Learning
- Lesson 03: How to Choose an Optimization Algorithm
Part 2: Background
- Lesson 04: No Free Lunch Theorem for Machine Learning
- Lesson 05: Local Optimization vs. Global Optimization
- Lesson 06: Premature Convergence
- Lesson 07: Creating Visualization for Function Optimization
- Lesson 08: Stochastic Optimization Algorithms
- Lesson 09: Random Search and Grid Search
Part 3: Local Optimization
- Lesson 10: What is a Gradient in Machine Learning?
- Lesson 11: Univariate Function Optimization
- Lesson 12: Pattern Search: Nelder-Mead Optimization Algorithm
- Lesson 13: Second Order: The BFGS and L-BFGS-B Optimization Algorithms
- Lesson 14: Least Square: Curve Fitting with SciPy
- Lesson 15: Stochastic Hill Climbing
- Lesson 16: Iterated Local Search
Part 4: Global Optimization
- Lesson 17: Simple Genetic Algorithm from Scratch
- Lesson 18: Evolution Strategies
- Lesson 19: Differential Evolution
- Lesson 20: Simulated Annealing from Scratch
Part 5: Gradient Descent
- Lesson 21: Gradient Descent Optimization from Scratch
- Lesson 22: Gradient Descent with Momentum
- Lesson 23: Gradient Descent with AdaGrad
- Lesson 24: Gradient Descent with RMSProp
- Lesson 25: Gradient Descent with Adadelta
- Lesson 26: Adam Optimization Algorithm
Part 6: Projects
- Lesson 27: Use Optimization Algorithms to Manually Fit Regression Models
- Lesson 28: Optimize Neural Network Models
- Lesson 29: Feature Selection using Stochastic Optimization
- Lesson 30: Manually Optimize Machine Learning Model Hyperparameters
- Appendix A: Getting help
- Appendix B: How to Setup Your Python Environment
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. 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 “Optimization for Machine Learning“
A digital download that contains everything you need, including:
- Clear descriptions to help you understand optimization algorithms 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, 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 technique covered.
Foundations required for developing and using optimization algorithms, including:
- The scope of optimization and its limitation in applied machine learning.
- The choice between local optimization and global optimization.
- The problem of premature convergence and how to address it.
- Use exhaustive search as baseline for performance evaluation of other optimization algorithms.
- Techniques of stochastic optimization.
- How visualization can be used as a proof of optimization is being done.
Specific optimization algorithms that we can use from SciPy, including:
- Brent’s method.
- Nelder-Mead algorithm.
- BGFS and L-BGFS-B algorithms.
- Curve fitting.
Techniques in implementing optimization algorithms, including:
- Hill climbing algorithm and its extension to use iterated local search.
- Genetic algorithm and its application to discrete and continuous functions.
- Evolution strategies and differential evolution.
- Simulated annealing.
- Gradient descent, and its variations including momentum, AdaGrad, RMSProp, Adadelta, and Adam.
Details on the technical aspects of gradient descent, including:
- Gradient descent as a first-order method that applicable to differentiable functions.
- What gradient tells about the optimal value of a function.
- How gradient descent depends on the initial point, step size, and stopping criteria.
- The problem of gradient descent applied to functions with noisy gradient.
- The use of momentum to get out of local optimal in gradient descent.
- How to make the step size in gradient descent adaptive to the curvature.
- The use of movement estimation to further improve the adaptive step size in gradient descent.
Experiences in applying optimization algorithms, including:
- Implementing and fitting a regression model.
- Implementing and fitting a multilayer perceptron model with various transfer functions.
- Performing feature selection for a machine learning model.
- Performing hyperparameter optimization with bounds constraints for a machine learning model.
What More Do You Need?
Take a Sneak Peek Inside The EBook
Click an image to Enlarge.
BONUS: Optimization Algorithms With Python Code Recipes
….you also get 104 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:
- Plotting a function to visually see its shape and the optimization progress.
- Random search and grid search.
- Nelder-Mead, BFGS and L-BFGS-B algorithms.
- Hill-climbing algorithms and its different variations.
- Genetic algorithms and the evolution strategies.
- Simulated annealing.
- Gradient descent, and its variations including momentum, AdaGrad, RMSProp, Adadelta, and Adam.
- Implementing regression from scratch.
- Training a neural network model from scratch.
- Tuning hyperparameters from scratch.
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 MacOS.
- 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.
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 2.4 (or higher) running on top of TensorFlow version 2.2 (or higher).
All tutorials on the blog have been updated to use standalone Keras running on top of Tensorflow 2.
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I do not recommend using Keras as part of TensorFlow 2 yet (e.g. tf.keras). It is too new, new things have issues, and I am waiting for the dust to settle. Standalone Keras has been working for years and continues to work extremely well.
There is one case of tutorials that do not support TensorFlow 2 because the tutorials make use of third-party libraries that have not yet been updated to support TensorFlow 2. Specifically tutorials that use Mask-RCNN for object recognition. Once the third party library has been updated, these tutorials too will be updated.
The book “Long Short-Term Memory Networks with Python” is not focused on time series forecasting, instead, it is focused on the LSTM method for a suite of sequence prediction problems.
The book “Deep Learning for Time Series Forecasting” shows you how to develop MLP, CNN and LSTM models for univariate, multivariate and multi-step time series forecasting problems.
Mini-courses are free courses offered on a range of machine learning topics and made available via email, PDF and blog posts.
- Short, typically 7 days or 14 days in length.
- Terse, typically giving one tip or code snippet per lesson.
- Limited, typically narrow in scope to a few related areas.
Ebooks are provided on many of the same topics providing full training courses on the topics.
- Longer, typically 25+ complete tutorial lessons, each taking up to an hour to complete.
- Complete, providing a gentle introduction into each lesson and includes full working code and further reading.
- Broad, covering all of the topics required on the topic to get productive quickly and bring the techniques to your own projects.
The mini-courses are designed for you to get a quick result. If you would like more information or fuller code examples on the topic then you can purchase the related Ebook.
The book “Master Machine Learning Algorithms” is for programmers and non-programmers alike. It teaches you how 10 top machine learning algorithms work, with worked examples in arithmetic, and spreadsheets, not code. The focus is on an understanding on how each model learns and makes predictions.
The book “Machine Learning Algorithms From Scratch” is for programmers that learn by writing code to understand. It provides step-by-step tutorials on how to implement top algorithms as well as how to load data, evaluate models and more. It has less on how the algorithms work, instead focusing exclusively on how to implement each in code.
The two books can support each other.
The books are a concentrated and more convenient version of what I put on the blog.
I design my books to be a combination of lessons and projects to teach you how to use a specific machine learning tool or library and then apply it to real predictive modeling problems.
The books get updated with bug fixes, updates for API changes and the addition of new chapters, and these updates are totally free.
I do put some of the book chapters on the blog as examples, but they are not tied to the surrounding chapters or the narrative that a book offers and do not offer the standalone code files.
With each book, you also get all of the source code files used in the book that you can use as recipes to jump-start your own predictive modeling problems.
My books are playbooks. Not textbooks.
They have no deep explanations of theory, just working examples that are laser-focused on the information that you need to know to bring machine learning to your project.
There is little math, no theory or derivations.
My readers really appreciate the top-down, rather than bottom-up approach used in my material. It is the one aspect I get the most feedback about.
My books are not for everyone, they are carefully designed for practitioners that need to get results, fast.
A code file is provided for each example presented in the book.
Dataset files used in each chapter are also provided with the book.
The code and dataset files are provided as part of your .zip download in a code/ subdirectory. Code and datasets are organized into subdirectories, one for each chapter that has a code example.
If you have misplaced your .zip download, you can contact me and I can send an updated purchase receipt email with a link to download your package.
Ebooks can be purchased from my website directly.
- First, find the book or bundle that you wish to purchase, you can see the full catalog here:
- Click on the book or bundle that you would like to purchase to go to the book’s details page.
- Click the “Buy Now” button for the book or bundle to go to the shopping cart page.
- Fill in the shopping cart with your details and payment details, and click the “Place Order” button.
- After completing the purchase you will be emailed a link to download your book or bundle.
All prices are in US dollars (USD).
Books can be purchased with PayPal or Credit Card.
All prices on Machine Learning Mastery are in US dollars.
Payments can be made by using either PayPal or a Credit Card that supports international payments (e.g. most credit cards).
You do not have to explicitly convert money from your currency to US dollars.
Currency conversion is performed automatically when you make a payment using PayPal or Credit Card.
After filling out and submitting your order form, you will be able to download your purchase immediately.
Your web browser will be redirected to a webpage where you can download your purchase.
You will also receive an email with a link to download your purchase.
If you lose the email or the link in the email expires, contact me and I will resend the purchase receipt email with an updated download link.
After you complete your purchase you will receive an email with a link to download your bundle.
The download will include the book or books and any bonus material.
To use a discount code, also called an offer code, or discount coupon when making a purchase, follow these steps:
1. Enter the discount code text into the field named “Discount Coupon” on the checkout page.
Note, if you don’t see a field called “Discount Coupon” on the checkout page, it means that that product does not support discounts.
2. Click the “Apply” button.
3. You will then see a message that the discount was applied successfully to your order.
Note, if the discount code that you used is no longer valid, you will see a message that the discount was not successfully applied to your order.
There are no physical books, therefore no shipping is required.
All books are EBooks that you can download immediately after you complete your purchase.
I recommend reading one chapter per day.
Momentum is important.
Some readers finish a book in a weekend.
Most readers finish a book in a few weeks by working through it during nights and weekends.
You will get your book immediately.
After you complete and submit the payment form, you will be immediately redirected to a webpage with a link to download your purchase.
You will also immediately be sent an email with a link to download your purchase.
What order should you read the books?
That is a great question, my best suggestions are as follows:
- Consider starting with a book on a topic that you are most excited about.
- Consider starting with a book on a topic that you can apply on a project immediately.
Also, consider that you don’t need to read all of the books, perhaps a subset of the books will get you the skills you need or want.
Nevertheless, one suggested order for reading the books is as follows:
- Probability for Machine Learning
- Statistical Methods for Machine Learning
- Linear Algebra for Machine Learning
- Optimization for Machine Learning
- Calculus for Machine Learning
- Master Machine Learning Algorithms
- Machine Learning Algorithms From Scratch
- Python for Machine Learning
- Machine Learning Mastery With Weka
- Machine Learning Mastery With Python
- Machine Learning Mastery With R
- Data Preparation for Machine Learning
- Imbalanced Classification With Python
- Time Series Forecasting With Python
- Ensemble Learning Algorithms With Python
- XGBoost With Python
- Deep Learning With Python
- Deep Learning with PyTorch
- Long Short-Term Memory Networks with Python
- Deep Learning for Natural Language Processing
- Deep Learning for Computer Vision
- Deep Learning for Time Series Forecasting
- Better Deep Learning
- Generative Adversarial Networks with Python
- Building Transformer Models with Attention
I hope that helps.
Sorry, I do not have a license to purchase my books or bundles for libraries.
The books are for individual use only.
Multi-seat licenses create a bit of a maintenance nightmare for me, sorry. It takes time away from reading, writing and helping my readers.
If you have a big order, such as for a class of students or a large team, please contact me and we will work something out.
I update the books frequently and you can access the latest version of a book at any time.
In order to get the latest version of a book, contact me directly with your order number or purchase email address and I can resend your purchase receipt email with an updated download link.
I do not maintain a public change log or errata for the changes in the book, sorry.
There are no physical books, therefore no delivery is required.
All books are Ebooks in PDF format that you can download immediately after you complete your purchase.
You will receive an email with a link to download your purchase. You can also contact me any time to get a new download link.
I support purchases from any country via PayPal or Credit Card.
My best advice is to start with a book on a topic that you can use immediately.
Baring that, pick a topic that interests you the most.
If you are unsure, perhaps try working through some of the free tutorials to see what area that you gravitate towards.
Generally, I recommend focusing on the process of working through a predictive modeling problem end-to-end:
I have three books that show you how to do this, with three top open source platforms:
- Master Machine Learning With Weka (no programming)
- Master Machine Learning With R (caret)
- Master Machine Learning With Python (pandas and scikit-learn)
These are great places to start.
You can always circle back and pick-up a book on algorithms later to learn more about how specific methods work in greater detail.
Thanks for your interest.
You can see the full catalog of my books and bundles here:
Thanks for asking.
I try not to plan my books too far into the future. I try to write about the topics that I am asked about the most or topics where I see the most misunderstanding.
If you would like me to write more about a topic, I would love to know.
Contact me directly and let me know the topic and even the types of tutorials you would love for me to write.
Contact me and let me know the email address (or email addresses) that you think you used to make purchases.
I can look up what purchases you have made and resend purchase receipts to you so that you can redownload your books and bundles.
All prices are in US Dollars (USD).
All currency conversion is handled by PayPal for PayPal purchases, or by Stripe and your bank for credit card purchases.
It is possible that your link to download your purchase will expire after a few days.
This is a security precaution.
Please contact me and I will resend you purchase receipt with an updated download link.
The book “Deep Learning With Python” could be a prerequisite to”Long Short-Term Memory Networks with Python“. It teaches you how to get started with Keras and how to develop your first MLP, CNN and LSTM.
The book “Long Short-Term Memory Networks with Python” goes deep on LSTMs and teaches you how to prepare data, how to develop a suite of different LSTM architectures, parameter tuning, updating models and more.
Both books focus on deep learning in Python using the Keras library.
The book “Long Short-Term Memory Networks in Python” focuses on how to develop a suite of different LSTM networks for sequence prediction, in general.
The book “Deep Learning for Time Series Forecasting” focuses on how to use a suite of different deep learning models (MLPs, CNNs, LSTMs, and hybrids) to address a suite of different time series forecasting problems (univariate, multivariate, multistep and combinations).
The LSTM book teaches LSTMs only and does not focus on time series. The Deep Learning for Time Series book focuses on time series and teaches how to use many different models including LSTMs.
The book “Long Short-Term Memory Networks With Python” focuses on how to implement different types of LSTM models.
The book “Deep Learning for Natural Language Processing” focuses on how to use a variety of different networks (including LSTMs) for text prediction problems.
The LSTM book can support the NLP book, but it is not a prerequisite.
You may need a business or corporate tax number for “Machine Learning Mastery“, the company, for your own tax purposes. This is common in EU companies for example.
The Machine Learning Mastery company is operated out of Puerto Rico.
As such, the company does not have a VAT identification number for the EU or similar for your country or regional area.
The company does have a Company Number. The details are as follows:
- Company Name: Zeus LLC
- Company Number: 421867-1511
Linux, MacOS, and Windows.
There are no code examples in “Master Machine Learning Algorithms“, therefore no programming language is used.
Algorithms are described and their working is summarized using basic arithmetic. The algorithm behavior is also demonstrated in excel spreadsheets, that are available with the book.
It is a great book for learning how algorithms work, without getting side-tracked with theory or programming syntax.
If you are interested in learning about machine learning algorithms by coding them from scratch (using the Python programming language), I would recommend a different book:
I write the content for the books (words and code) using a text editor, specifically sublime.
I typeset the books and create a PDF using LaTeX.
All of the books have been tested and work with Python 3 (e.g. 3.5 or 3.6).
Most of the books have also been tested and work with Python 2.7.
Where possible, I recommend using the latest version of Python 3.
After you fill in the order form and submit it, two things will happen:
- You will be redirected to a webpage where you can download your purchase.
- You will be sent an email (to the email address used in the order form) with a link to download your purchase.
The redirect in the browser and the email will happen immediately after you complete the purchase.
You can download your purchase from either the webpage or the email.
If you cannot find the email, perhaps check other email folders, such as the “spam” folder?
If you have any concerns, contact me and I can resend your purchase receipt email with the download link.
I do test my tutorials and projects on the blog first. It’s like the early access to ideas, and many of them do not make it to my training.
Much of the material in the books appeared in some form on my blog first and is later refined, improved and repackaged into a chapter format. I find this helps greatly with quality and bug fixing.
The books provide a more convenient packaging of the material, including source code, datasets and PDF format. They also include updates for new APIs, new chapters, bug and typo fixing, and direct access to me for all the support and help I can provide.
I believe my books offer thousands of dollars of education for tens of dollars each.
They are months if not years of experience distilled into a few hundred pages of carefully crafted and well-tested tutorials.
I think they are a bargain for professional developers looking to rapidly build skills in applied machine learning or use machine learning on a project.
Also, what are skills in machine learning worth to you? to your next project? and you’re current or next employer?
Nevertheless, the price of my books may appear expensive if you are a student or if you are not used to the high salaries for developers in North America, Australia, UK and similar parts of the world. For that, I am sorry.
I do offer discounts to students, teachers and retirees.
Please contact me to find out more.
I offer a ton of free content on my blog, you can get started with my best free material here:
About my Books
My books are playbooks.
They are intended for developers who want to know how to use a specific library to actually solve problems and deliver value at work.
- My books guide you only through the elements you need to know in order to get results.
- My books are in PDF format and come with code and datasets, specifically designed for you to read and work-through on your computer.
- My books give you direct access to me via email (what other books offer that?)
- My books are a tiny business expense for a professional developer that can be charged to the company and is tax deductible in most regions.
Very few training materials on machine learning are focused on how to get results.
The vast majority are about repeating the same math and theory and ignore the one thing you really care about: how to use the methods on a project.
Comparison to Other Options
Let me provide some context for you on the pricing of the books:
There are free videos on youtube and tutorials on blogs.
- Great, I encourage you to use them, including my own free tutorials.
There are very cheap video courses that teach you one or two tricks with an API.
- My books teach you how to use a library to work through a project end-to-end and deliver value, not just a few tricks
A textbook on machine learning can cost $50 to $100.
- All of my books are cheaper than the average machine learning textbook, and I expect you may be more productive, sooner.
A bootcamp or other in-person training can cost $1000+ dollars and last for days to weeks.
- A bundle of all of my books is far cheaper than this, they allow you to work at your own pace, and the bundle covers more content than the average bootcamp.
Sorry, my books are not available on websites like Amazon.com.
I carefully decided to not put my books on Amazon for a number of reasons:
- Amazon takes 65% of the sale price of self-published books, which would put me out of business.
- Amazon offers very little control over the sales page and shopping cart experience.
- Amazon does not allow me to contact my customers via email and offer direct support and updates.
- Amazon does not allow me to deliver my book to customers as a PDF, the preferred format for my customers to read on the screen.
I hope that helps you understand my rationale.
I am sorry to hear that you’re having difficulty purchasing a book or bundle.
I use Stripe for Credit Card and PayPal services to support secure and encrypted payment processing on my website.
Some common problems when customers have a problem include:
- Perhaps you can double check that your details are correct, just in case of a typo?
- Perhaps you could try a different payment method, such as PayPal or Credit Card?
- Perhaps you’re able to talk to your bank, just in case they blocked the transaction?
I often see customers trying to purchase with a domestic credit card or debit card that does not allow international purchases. This is easy to overcome by talking to your bank.
If you’re still having difficulty, please contact me and I can help investigate further.
When you purchase a book from my website and later review your bank statement, it is possible that you may see an additional small charge of one or two dollars.
The charge does not come from my website or payment processor.
Instead, the charge was added by your bank, credit card company, or financial institution. It may be because your bank adds an additional charge for online or international transactions.
This is rare but I have seen this happen once or twice before, often with credit cards used by enterprise or large corporate institutions.
My advice is to contact your bank or financial institution directly and ask them to explain the cause of the additional charge.
If you would like a copy of the payment transaction from my side (e.g. a screenshot from the payment processor), or a PDF tax invoice, please contact me directly.
I give away a lot of content for free. Most of it in fact.
It is important to me to help students and practitioners that are not well off, hence the enormous amount of free content that I provide.
You can access the free content:
I have thought very hard about this and I sell machine learning Ebooks for a few important reasons:
- I use the revenue to support the site and all the non-paying customers.
- I use the revenue to support my family so that I can continue to create content.
- Practitioners that pay for tutorials are far more likely to work through them and learn something.
- I target my books towards working professionals that are more likely to afford the materials.
All updates to the book or books in your purchase are free.
Books are usually updated once every few months to fix bugs, typos and keep abreast of API changes.
Contact me anytime and check if there have been updates. Let me know what version of the book you have (version is listed on the copyright page).
Please contact me anytime with questions about machine learning or the books.
One question at a time please.
Also, each book has a final chapter on getting more help and further reading and points to resources that you can use to get more help.
Yes, the books can help you get a job, but indirectly.
Getting a job is up to you.
It is a matching problem between an organization looking for someone to fill a role and you with your skills and background.
That being said, there are companies that are more interested in the value that you can provide to the business than the degrees that you have. Often, these are smaller companies and start-ups.
You can focus on providing value with machine learning by learning and getting very good at working through predictive modeling problems end-to-end. You can show this skill by developing a machine learning portfolio of completed projects.
My books are specifically designed to help you toward these ends. They teach you exactly how to use open source tools and libraries to get results in a predictive modeling project.
Do you have another question?