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 live in Australia with my wife and son and love to write and code.
I have a computer science background as well as a Masters and Ph.D. degree in Artificial Intelligence.
I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones. (yes I have written tons of code that runs operationally)
I get a lot of satisfaction helping developers get started and get really good at machine learning.
I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
I'm here to help if you ever have any questions. I want you to be awesome at machine learning.
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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:
...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!
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Frequently Asked Questions
What programming language is used? None. No programming is required. You will learn how to work through machine learning projects using the graphical user interfaces of the Weka platform.
Do I need to be a good programmer? Not at all. No programming is required, at all.
How much math do I need to know? No background in statistics, probability or linear algebra is required. We do not derive any equations.
How many pages it the Ebook? The Ebook is 248 pages.
Is there a hard copy physical book? Not at this stage. Ebook only.
How is it different to Witten, et al.’s Weka book? This Ebook is laser focused on how to use Weka to work through a predictive modeling problem from end-to-end, not teaching you machine learning theory.
Will I get updates? Yes. You will be notified about updates to the book that you can download for free.
How long will the Ebook take to complete? I recommend reading one chapter per day. With 18 lessons and 3 projects and moving fast through the intro and conclusions, you can finish in about 3 weeks. On the other hand, if you are keen you could work through all of the material in a weekend, and many readers in fact do this.
What if I need help? The final chapter is titled “Getting More Help” and points to resources that you can use to get more help with the Weka platform.
How much machine learning do I need to know? Only a little. You will be lead step-by-step through the process of working through predictive modeling problems. It would help if you were already familiar with concepts like cross validation.
Are there any additional downloads? No. Just the Ebook.
How is it different to the Weka MOOC? This Ebook is laser focused on how to use Weka to work through a predictive modeling problem from end-to-end, not teach you general machine learning or unrelated topics.
What operating systems are supported? You can work through the book using Linux, Mac OS X and Windows.
Is there any digital rights management (DRM)? No, there is no DRM.