Machine Learning Mastery With Weka
Discover How To Build Predictive Models In Minutes
Without The Code, Without The Math and Without the Confusion
Machine learning is not just for professors.
Weka is a top machine learning platform that provides an easy-to-use graphical interface and state-of-the-art algorithms.
In this mega Ebook is written in the friendly Machine Learning Mastery style, learn exactly how to get started with applied machine learning using the Weka platform. You’ll get:
- 248 Page PDF Ebook.
- 18 Step-by-Step Lessons.
- 3 End-to-End Projects.
Discover How To Apply Machine Learning
Click to jump straight to the packages.
Getting Started in Applied Machine Learning is Hard
…it’s hard for more reasons than you even know
When you start out in applied machine learning, there is so much to learn.
- There are the algorithms.
- There is the data.
- There is the specific problem you are working on.
- There is the mathematics behind it all.
- There is the tool you plan to use.
Often you need to learn a new programming language, like python or more esoteric languages like Matlab or R.
This does not have to be the case.
It is so much easier to learn one thing well, rather than try and possibly fail to learn a host of new things.
Get Past Overwhelm and Focus on Learning Just One Thing
…how to deliver results using applied machine learning
The answer is to focus on one thing.
The one thing to learn when you are starting in machine learning is how to deliver a result.
That is, given a problem, how to work through it and deliver a set of predictions or how to deliver a model that can generate predictions.
Not just predictions, but accurate predictions that can be delivered robustly and reliably, that you can put your name or your company’s name against and in which you can feel confident.
You can learn how to deliver a result in applied machine learning by using a systematic process.
Learn the Process of Applied Machine Learning
…the systematic process you can use to deliver results again and again
The systematic process of applied machine learning is the way you can learn how to deliver a result.
It is comprised of 5 steps that that you can use from beginning to end:
- Defining your problem.
- Preparing your data.
- Evaluate a suite of algorithms.
- Improve your results with tuning and ensembles.
- Finalize your model and present results.
Here’s the trap that you will avoid by using a systemic process:
If you follow the advice on blogs and online course, you will end up spending months or years learning the intricate details of the math behind just a handful of machine learning algorithms, but you will have no idea about how to use these algorithms as a part of a much bigger predictive modeling project.
By jumping straight to the process, you skip years of frustration, waiting to learn how to these powerful tools into practice. You can start building models for real problems straight away, and come back to the details of how the algorithms work later, in the context of actually delivering a result.
Weka is the Best Platform for Practicing Applied Machine Learning
…because there is no code, no math, and the tool guides you through the process
The best tool to learn this process is the Weka machine learning workbench.
There are 3 main reasons why this is the case:
- Speed: you can work through your problem fast, giving you more time to try lots of ideas.
- Focus: it is just you and your problem, the tool gets out of your way.
- Coverage: it provides lots of state-of-the-art algorithms to choose from.
It saves you from the cruft that you can encounter with other platforms.
You do not need to spend weeks learning a new language or API, and can focus on learning how to work through problems efficiently and effectively.
You can focus on the one valuable thing you need to learn: the process of applied machine learning and delivering a result. Later, you can learn how to use more and different tools.
Introducing the Ebook “Machine Learning Mastery With Weka”
…your ticket to applied machine learning
Downloading Weka is not enough…
- You need to know how to map the tasks of an applied machine learning project onto the platform.
- You need to know the best practices for working through each task in the process.
- You also need to know strategies to practice and build a portfolio of completed projects that you can use to demonstrate your developing skills in applied machine learning.
Introducing the Ebook: Machine Learning Mastery With Weka
This Ebook was designed for you as a developer to rapidly get up to speed in applied machine learning using the Weka platform.
A step-by-step tutorial approach is used throughout the 18 lessons and 3 end-to-end projects, showing you exactly what to click and exactly what results to expect.
The goal is to get you using Weka to create your first models as quickly as possible, then guide you through the finer points of developing predictive models for classification and regression predictive modeling problems.
This Ebook is your guide to learning the in-demand skills you need to deliver results using applied machine learning on your own projects.
Let’s take a closer look at the breakdown of the lessons and projects you will discover inside this Ebook.
Everything You Need To Know to Develop Your Own Predictive Models
You Will Get:
18 Lessons on Applied Machine Learning With Weka
3 Project Tutorials that Tie it All Together
This ebook was written around two themes designed to get you started and using applied machine learning effectively and quickly.
These are Lessons and Projects:
- Lessons: Learn how the subtasks of a applied machine learning project map onto the Weka platform and the best practice way of working through each task.
- Projects: Tie together all of the knowledge from the lessons by working through case study predictive modeling problems.
Here is an overview of the 18 step-by-step lessons you will complete:
- Lesson 01: How to install Weka.
- Lesson 02: How to navigate the Weka interface.
- Lesson 03: How to load your machine learning datasets.
- Lesson 04: How to load standard machine learning datasets.
- Lesson 05: How to understand your data with visualization.
- Lesson 06: How to rescale your data for modeling.
- Lesson 07: How to transform your data for modeling.
- Lesson 08: How to handle missing data for modeling.
- Lesson 09: How to select the best features for modeling.
- Lesson 10: How to work with machine learning algorithms.
- Lesson 11: How to estimate the performance of a model.
- Lesson 12: How to develop a baseline performance for a model.
- Lesson 13: How to develop models to predict categorical values.
- Lesson 14: How to develop models to predict real-values.
- Lesson 15: How to combine the predictions of multiple models.
- Lesson 16: How to compare the performance of algorithms.
- Lesson 17: How to improve model performance by tuning.
- Lesson 18: How to finalize a model and make predictions.
Each lesson was designed to be completed by you in about 30 minutes.
Here is an overview of the 3 end-to-end projects you will complete:
- Project 01: Multi-class classification project predicting flower species from measurements.
- Project 02: Binary class classification project predicting the onset of diabetes from medical details.
- Project 03: Regression project predicting house price from suburb details.
Each project was designed to be completed by you in less than 60 minutes.
Here’s Everything You’ll Get…
in Machine Learning Mastery With Weka
A digital download that contains everything you need, including:
- Clear descriptions that help you to understand the Weka platform for machine learning.
- Step-by-step Weka tutorials to show you exactly how to apply each technique and algorithm.
- End-to-end Weka projects that show you exactly how to tie the pieces together and get a result.
- Digital Ebook in PDF format so that you can have the book open side-by-side with the tool and see exactly how each example works.
Gentle introduction to the platform and how to make best use of it, including:
- The fact that applied machine learning is hard and how Weka can make it easy and even fun.
- The Weka machine learning workbench and the 2 environments you must focus on using.
- The benefit of a machine learning portfolio and how to demonstrate your growing skills in applied machine learning.
Foundation tutorials for getting started and data preparation, including:
- The download and installation of Weka for each major platform (Windows, Linux and Mac)
- The main interfaces of the Weka machine learning workbench, what they are for and what to expect.
- The loading of data from CSV and ARFF formated files and the important foundation this lays for loading your own data.
- The standard machine learning datasets and why they are so important when practicing in Weka.
- The calculation of descriptive statistics and data visualization and why you must understand your data before modeling.
- The scaling of data to meet the expectations of machine learning algorithms and the 2 most popular methods.
- The powerful data transform capabilities of Weka and the 2 methods you will probably need on your problem.
- The problem that missing data can have when modeling and how to fill in the gaps.
- The requirement that some algorithms have to select the most important features in your data and 4 templates that you can copy.
Lessons on applied machine learning with the Weka platform, including:
- The large variety of machine learning algorithms offered in Weka and the 10 to focus on for best results.
- The importance of estimating model performance on unseen data and 4 techniques you need to do so.
- The need for estimating a baseline performance on a predictive modeling problem and how Weka makes that easy.
- The necessity of not assuming a solution, the spot checking method and the linear and nonlinear algorithm recipes you can use immediately.
- The improvement of results with ensemble methods and the 5 main techniques you can use on your projects.
- The comparison and selection of trained models and the Experimenter interface that helps you choose.
- The tuning of machine learning algorithm hyperparameters and the recipe you can use on any algorithm.
- The finalization of a trained model to save it to file and later load it to make new predictions on unseen data.
Projects that tie together the lessons into end-to-end sequence to deliver a result, including:
- The first machine learning project in Weka for multi-class classification that provides a gentle guide as to how the lessons tie together.
- The binary classification problem that predicts the onset of diabetes showing the judicious use of data analysis and data preparation.
- The regression project to predict house prices that shows the improvements of data transforms, tuning and ensemble methods.
Resources you need to go deeper, when you need to, including:
- The best sources of information on the Weka platform, in case you are craving more information.
- The best places online where you can ask your challenging questions and actually get a response.
What More Do You Need?
Take a Sneak Peek Inside The Ebook
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 Ph.D. degrees in Artificial Intelligence with a focus on stochastic optimization.
I've written books on algorithms, won and ranked well in competitions, consulted for startups, and spent years in industry. (Yes, I have spend a long time building and maintaining REAL operational systems!)
I get a lot of satisfaction helping developers get started and get really good at applied machine learning.
I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
I'm here to help if you ever have any questions. I want you to be awesome at machine learning.
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Are you a Student, Teacher or Retiree?
Do you have any Questions?
What Are Skills in Machine Learning Worth?
Your boss asks you:
Hey, can you build a predictive model for this?
Imagine you had the skills and confidence to say:
...and follow through.
I have been there. It feels great!
How much is that worth to you?
The industry is demanding skills in machine learning.
The market wants people that can deliver results, not write academic papers.
Business knows what these skills are worth and are paying sky-high starting salaries.
A Data Scientists Salary Begins at:
$100,000 to $150,000.
A Machine Learning Engineers Salary is Even Higher.
What Are Your Alternatives?
You made it this far.
You're ready to take action.
But, what are your alternatives? What options are there?
(1) A Theoretical Textbook for $100+
...it's boring, math-heavy and you'll probably never finish it.
(2) An On-site Boot Camp for $10,000+
...it's full of young kids, you must travel and it can take months.
(3) A Higher Degree for $100,000+
...it's expensive, takes years, and you'll be an academic.
For the Hands-On Skills You Get...
And the Speed of Results You See...
And the Low Price You Pay...
Machine Learning Mastery Ebooks are
And they work. That's why I offer the money-back guarantee.
You're A Professional
The field moves quickly,
...how long can you wait?
You think you have all the time in the world, but...
- New methods are devised and algorithms change.
- New books get released and prices increase.
- New graduates come along and jobs get filled.
Right Now is the Best Time to make your start.
Bottom-up is Slow and Frustrating,
...don't you want a faster way?
Can you really go on another day, week or month...
- Scraping ideas and code from incomplete posts.
- Skimming theory and insight from short videos.
- Parsing Greek letters from academic textbooks.
Targeted Training is your Shortest Path to a result.
Professionals 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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Customer Questions (66)
Thanks for your interest.
Sorry, I do not support third-party resellers for my books (e.g. reselling in other bookstores).
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- Jason Brownlee, Machine Learning Algorithms in Python, Machine Learning Mastery, Available from https://machinelearningmastery.com/machine-learning-with-python/, accessed April 15th, 2018.
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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.
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I have books that do not require any skill in programming, for example:
Other books do have code examples in a given programming language.
You must know the basics of the programming language, such as how to install the environment and how to write simple programs. I do not teach programming, I teach machine learning for developers.
You do not need to be a good programmer.
That being said, I do offer tutorials on how to setup your environment efficiently and even crash courses on programming languages for developers that may not be familiar with the given language.
My books do not cover the theory or derivations of machine learning methods.
This is by design.
My books are focused on the practical concern of applied machine learning. Specifically, how algorithms work and how to use them effectively with modern open source tools.
If you are interested in the theory and derivations of equations, I recommend a machine learning textbook. Some good examples of machine learning textbooks that cover theory include:
I generally don’t run sales.
If I do have a special, such as around the launch of a new book, I only offer it to past customers and subscribers on my email list.
I do offer book bundles that offer a discount for a collection of related books.
I do offer a discount to students, teachers, and retirees. Contact me to find out about discounts.
Sorry, I don’t have videos.
I only have tutorial lessons and projects in text format.
This is by design. I used to have video content and I found the completion rate much lower.
I want you to put the material into practice. I have found that text-based tutorials are the best way of achieving this. With text-based tutorials you must read, implement and run the code.
With videos, you are passively watching and not required to take any action.
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.
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.
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.
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
- Deep Learning for Time Series Forecasting
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.
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.
Linux, MacOS, and Windows.
There are no code examples in “Master Machine Learning Algorithms“, therefore no programming language is used.
Algorithms are described and their working is summarized using basic arithmetic. The algorithm behavior is also demonstrated in excel spreadsheets, that are available with the book.
It is a great book for learning how algorithms work, without getting side-tracked with theory or programming syntax.
If you are interested in learning about machine learning algorithms by coding them from scratch (using the Python programming language), I would recommend a different book:
I write the content for the books (words and code) using a text editor, specifically sublime.
I typeset the books and create a PDF using LaTeX.
All of the books have been tested and work with Python 3 (e.g. 3.5 or 3.6).
Most of the books have also been tested and work with Python 2.7.
Where possible, I recommend using the latest version of Python 3.
After you fill in the order form and submit it, two things will happen:
- You will be redirected to a webpage where you can download your purchase.
- You will be sent an email (to the email address used in the order form) with a link to download your purchase.
The redirect in the browser and the email will happen immediately after you complete the purchase.
You can download your purchase from either the webpage or the email.
If you cannot find the email, perhaps check other email folders, such as the “spam” folder?
If you have any concerns, contact me and I can resend your purchase receipt email with the download link.
I do test my tutorials and projects on the blog first. It’s like the early access to ideas, and many of them do not make it to my training.
Much of the material in the books appeared in some form on my blog first and is later refined, improved and repackaged into a chapter format. I find this helps greatly with quality and bug fixing.
The books provide a more convenient packaging of the material, including source code, datasets and PDF format. They also include updates for new APIs, new chapters, bug and typo fixing, and direct access to me for all the support and help I can provide.
I believe my books offer thousands of dollars of education for tens of dollars each.
They are months if not years of experience distilled into a few hundred pages of carefully crafted and well-tested tutorials.
I think they are a bargain for professional developers looking to rapidly build skills in applied machine learning or use machine learning on a project.
Also, what are skills in machine learning worth to you? to your next project? and you’re current or next employer?
Nevertheless, the price of my books may appear expensive if you are a student or if you are not used to the high salaries for developers in North America, Australia, UK and similar parts of the world. For that, I am sorry.
I do offer discounts to students, teachers and retirees.
Please contact me to find out more.
I offer a ton of free content on my blog, you can get started with my best free material here:
About my Books
My books are playbooks.
They are intended for developers who want to know how to use a specific library to actually solve problems and deliver value at work.
- My books guide you only through the elements you need to know in order to get results.
- My books are in PDF format and come with code and datasets, specifically designed for you to read and work-through on your computer.
- My books give you direct access to me via email (what other books offer that?)
- My books are a tiny business expense for a professional developer that can be charged to the company and is tax deductible in most regions.
Very few training materials on machine learning are focused on how to get results.
The vast majority are about repeating the same math and theory and ignore the one thing you really care about: how to use the methods on a project.
Comparison to Other Options
Let me provide some context for you on the pricing of the books:
There are free videos on youtube and tutorials on blogs.
- Great, I encourage you to use them, including my own free tutorials.
There are very cheap video courses that teach you one or two tricks with an API.
- My books teach you how to use a library to work through a project end-to-end and deliver value, not just a few tricks
A textbook on machine learning can cost $50 to $100.
- All of my books are cheaper than the average machine learning textbook, and I expect you may be more productive, sooner.
A bootcamp or other in-person training can cost $1000+ dollars and last for days to weeks.
- A bundle of all of my books is far cheaper than this, they allow you to work at your own pace, and the bundle covers more content than the average bootcamp.
Sorry, my books are not available on websites like Amazon.com.
I carefully decided to not put my books on Amazon for a number of reasons:
- Amazon takes 65% of the sale price of self-published books, which would put me out of business.
- Amazon offers very little control over the sales page and shopping cart experience.
- Amazon does not allow me to contact my customers via email and offer direct support and updates.
- Amazon does not allow me to deliver my book to customers as a PDF, the preferred format for my customers to read on the screen.
I hope that helps you understand my rationale.
I am sorry to hear that you’re having difficulty purchasing a book or bundle.
I use Stripe and PayPal services to support secure and encrypted payment processing on my website.
Some common problems when customers have a problem include:
- Perhaps you can double check that your details are correct, just in case of a typo?
- Perhaps you could try a different payment method, such as PayPal or Credit Card?
- Perhaps you’re able to talk to your bank, just in case they blocked the transaction?
I often see customers trying to purchase with a domestic credit card that does not allow international purchases. This is easy to overcome by talking to your bank.
If you’re still having difficulty, please contact me and I can help investigate further.
I give away a lot of content for free. Most of it in fact.
It is important to me to help students and practitioners that are not well off, hence the enormous amount of free content that I provide.
You can access the free content:
I have thought very hard about this and I sell machine learning Ebooks for a few important reasons:
- I use the revenue to support the site and all the non-paying customers.
- I use the revenue to support my family so that I can continue to create content.
- Practitioners that pay for tutorials are far more likely to work through them and learn something.
- I target my books towards working professionals that are more likely to afford the materials.
All updates to the book or books in your purchase are free.
Books are usually updated once every few months to fix bugs, typos and keep abreast of API changes.
Contact me anytime and check if there have been updates. Let me know what version of the book you have (version is listed on the copyright page).
Please contact me anytime with questions about machine learning or the books.
One question at a time please.
Also, each book has a final chapter on getting more help and further reading and points to resources that you can use to get more help.
Yes, the books can help you get a job, but indirectly.
Getting a job is up to you.
It is a matching problem between an organization looking for someone to fill a role and you with your skills and background.
That being said, there are companies that are more interested in the value that you can provide to the business than the degrees that you have. Often, these are smaller companies and start-ups.
You can focus on providing value with machine learning by learning and getting very good at working through predictive modeling problems end-to-end. You can show this skill by developing a machine learning portfolio of completed projects.
My books are specifically designed to help you toward these ends. They teach you exactly how to use open source tools and libraries to get results in a predictive modeling project.
Do you have another question?