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Machine Learning Hands-On for Developers and Technical Professionals by Jason Bell
Book Details :
LanguageEnglish
Pages407
FormatPDF
Size6.51 MB


Machine Learning Hands-On for Developers and Technical Professionals by Jason Bell



Main Contents of Machine Learning Hands-On for Developers and Technical Professionals eBook


- Chapter 1. What Is Machine Learning?

- Chapter 2. Planning for Machine Learning.

- Chapter 3. Working with Decision Trees.

- Chapter 4. Bayesian Networks.

- Chapter 5. Artificial Neural Networks.

- Chapter 6. Association Rules Learning.

- Chapter 7. Support Vector Machines.

- Chapter 8. Clustering.

- Chapter 9. Machine Learning in Real Time with Spring XD.

- Chapter 10. Machine Learning as a Batch Process.

- Chapter 11. Apache Spark.

- Chapter 12. Machine Learning with R.


Aims of Machine Learning Hands-On for Developers and Technical Professionals eBook


This book (Machine Learning Hands-On for Developers and Technical Professionals by Jason Bell) is about machine learning and not about Big Data. It’s about the various techniques used to gain insight from your data.

By the end of the book, you will have seen how various methods of machine learning work, and you will also have had some practical explanations on how the code is put together,

leaving you with a good idea of how you could apply the right machine learning techniques to your own problems.

There’s no right or wrong way to use this book (Machine Learning Hands-On for Developers and Technical Professionals by Jason Bell). You can start at the beginning and work your way through, or you can just dip in and out of the parts you need to know at the time you need to know them

“Hands-On” Means Hands-On


Many books on the subject of machine learning that I’ve read in the past have been very heavy on theory. That’s not a bad thing. If you’re looking for in-depth theory with really complex looking equations, I applaud your rigor. Me? I’m more hands-on with my approach to learning and to projects. My philosophy is quite simple:


■ Start with a question in mind.

■ Find the theory I need to learn.

■ Find lots of examples I can learn from.

■ Put them to work in my own projects.

As a software developer, I personally like to see lots of examples. As a teacher, I like to get as much hands-on development time as possible but also get the message across to students as simply as possible.

There’s something about fingers on keys, coding away on your IDE, and getting things to work that’s rather appealing, and it’s something that I want to convey in the book.

Everyone has his or her own learning styles. I believe this book (Machine Learning Hands-On for Developers and Technical Professionals by Jason Bell)covers the most common methods, so everybody will benefit. 

What Will You Have Learned by the End of Machine Learning Hands-On for Developers and Technical Professionals eBook?


Assuming that you’re reading the book from start to finish, you’ll learn the common uses for machine learning, different methods of machine learning, and how to apply real-time and batch processing.

There’s also nothing wrong with referencing a specific section that you want to learn.

The chapters and examples were created in such a way that there’s no dependency to learn one chapter over another.


The aim is to cover the common machine learning concepts in a practical manner. Using the existing free tools and libraries that are available to you, there’s little stopping you from starting to gain insight from the existing data that you have.

Outline of the Chapters of Machine Learning Hands-On for Developers and Technical Professionals eBook

Chapter 1 considers the question, “What is machine learning?” and looks at the definition of machine learning, where it is used, and what type of algorithmic challenges you’ll encounter.

I also talk about the human side of machine learning and the need for future proofing your models and work. Before any real coding can take place, you need to plan.

Chapter 2, “How to Plan for Machine Learning,” concentrates on planning for machine learning. Planning includes engaging with data science teams, processing, defining storage requirements, protecting data privacy, cleaning data, and understanding that there is rarely one solution that fits all elements of your task.

In Chapter 2 you also work through some handy Linux commands that will help you maintain the data before it goes for processing.

A decision tree is a common machine learning practice. Using results or observed behaviors and various input data (signals, features) in models, you can predict outcomes when presented with new data.

Chapter 3 looks at designing decision tree learning with data and coding an example using Weka. Bayesian networks represent conditional dependencies against a set of random variables.

In Chapter 4 you construct some simple examples to show you how Bayesian networks work and then look at some code to use. Inspired by the workings of the central nervous system, neural network models are still used in deep learning systems.

Chapter 5 looks at how this branch of machine learning works and shows you an example with inputs feeding information into a network.

If you are into basket analysis, then you’ll like Chapter 6 on association rule learning and finding relations within large data sets.

You’ll have a close look at the Apriori algorithm and how it’s used within the supermarket industry today. Support vector machines are a supervised learning method to analyze data and recognize patterns.

In Chapter 7 you look at text classification and other examples to see how it works. Chapter 8 covers clustering—grouping objects—which is perfect for the likes of segmentation analysis in marketing.

This approach is the best method of machine learning for attempting some trial-and-error suggestions during the initial learning phases. Chapters 9 and 10 are walk through tutorials.

The example in Chapter 9 concerns real-time processing. You use Spring XD, a “data ingesting engine,” and the streaming Twitter API to gather tweets as they happen.

In Chapter 10, you look at machine learning as a batch process. With the data acquired in Chapter 9, you set up a Hadoop cluster and run various jobs. You also look at the common issue of acquiring data from databases with Sqoop, performing customer recommendations with Mahout, and analyzing annual customer data with Hadoop and Pig.

Chapter 11 covers one of the newer entrants to the machine learning arena. The chapter looks at Apache Spark and also introduces you to the Scala language and performing SQL-like queries with in-memory data. For a long time the R language has been used by statistics people the world over.

Chapter 12 examines at the R language. With it you perform some of the machine learning algorithms covered in the previous chapters.


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