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About This Book
- Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
- Comprehensive practical solutions taking you into the future of machine learning
- Go a step further and integrate your machine learning projects with Hadoop
This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.
What You Will Learn- Implement a wide range of algorithms and techniques for tackling complex data
- Get to grips with some of the most powerful languages in data science, including R, Python, and Julia
- Harness the capabilities of Spark and Mahout used in conjunction with Hadoop to manage and process data successfully
- Apply the appropriate Machine learning technique to address a real-world problem
- Get acquainted with deep learning and find out how neural networks are being used at the cutting edge of Machine learning
- Explore the future of Machine learning and dive deeper into polyglot persistence, semantic data, and more
This book explores an extensive range of Machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While Machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.
We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for modern data scientists who want to get to grips with Machine learning's real-world application.
The book also explores cutting-edge advances in Machine learning, with worked examples and guidance on Deep learning and Reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced Machine learning methodologies.
- Sales Rank: #696438 in eBooks
- Published on: 2016-01-30
- Released on: 2016-01-30
- Format: Kindle eBook
About the Author
Sunila Gollapudi
Sunila Gollapudi works as Vice President Technology with Broadridge Financial Solutions (India) Pvt. Ltd., a wholly owned subsidiary of the US-based Broadridge Financial Solutions Inc. (BR). She has close to 14 years of rich hands-on experience in the IT services space. She currently runs the Architecture Center of Excellence from India and plays a key role in the big data and data science initiatives. Prior to joining Broadridge she held key positions at leading global organizations and specializes in Java, distributed architecture, big data technologies, advanced analytics, Machine learning, semantic technologies, and data integration tools. Sunila represents Broadridge in global technology leadership and innovation forums, the most recent being at IEEE for her work on semantic technologies and its role in business data lakes. Sunila's signature strength is her ability to stay connected with ever changing global technology landscape where new technologies mushroom rapidly, connect the dots and architect practical solutions for business delivery. A post graduate in computer science, her first publication was on Big Data Datawarehouse solution, Greenplum titled Getting Started with Greenplum for Big Data Analytics, Packt Publishing. She's a noted Indian classical dancer at both national and international levels, a painting artist, in addition to being a mother, and a wife.
Most helpful customer reviews
29 of 30 people found the following review helpful.
Extremely disappointed
By Alberto
I was looking for a book that covered Machine Learning from a practical, hands on perspective, and the "Practical Machine Learning" title certainly looked promising. Unfortunately, the only thing practical I could find in this book was the title.
I read the first five chapters and stopped; it was a huge waste of my time and my money. The book merely scratches the surface of the real topics; on the other hand, it spends a considerable amount of pages covering instructions on how to download and install software packges like Hadoop, R, Julia, etc. Most of the instructions are a poor copy and paste job from the original web sites. I don't need an entire chapter explaining how to install Hadoop - the Apache/Hadoop site has better instructions that are specific to my target operating system.
The illustrations are laughable, to say the least - very naive diagrams that were probably put together by someone who just learned how to use Power Point. They do nothing to explain concepts.
There is also quite a bit of material about ML tools that looks like it was extracted directly from the original web sites. I was looking for concrete problems, formulae, algorithms, code, etc. Couldn't find any.
Bottom line, this is a huge and expensive copy and paste scam. Stay away from it.
0 of 1 people found the following review helpful.
Good coverage on wide scope of algorithms with source code
By Christine A
Thoroughly impressed by the structure of the book and the way the algorithms are introduced from the concept to application. The only book that explores 5 different frameworks for implementing machine learning. Additionally, a detailed information on the code snippets in addition to the code documentation would have been a fantastic bonus to the content.
4 of 5 people found the following review helpful.
Keep looking for something better
By Pedro Rodriguez
NOTE: I haven't bought the book through Amazon. This is a review I made after learning ML using this book as reference.
I used this book to get introduced into the Machine Learning world. If you want a theoric guide, this book is totally recommended. The examples explained here are good enough for a beginner (you will need to look further for more definitions, though) and are pretty clear. Here, I would put 4 stars because it isn't the best guide out there.
Well, now, the reason why I reduced its rating comes in the part of configuration and code. I am using a Linux distro, so some instructions were clear. But the problem is that when configuring Hadoop and Mahout, the instructions were made by different people, so there were unclear or done steps. Ok, this is tolerable, even though it is a bad practice.
But after chapter 5, where the code examples are important, comes the nightmare. I tested the R examples of forests and few issues about importing some required libraries appeared. Ok, no problems here, just download and import it. But when I tried the rest, I only got errors. Reading the Python code, it was referencing local files from the author's computer, using deprecated functions from Scikit-learn and the code was pretty unclear to at least fix the problem. Until now I learned how to use Mahout on Eclipse (an IDE that I despise so much, but at least has a "good" integration with Maven), but still, the project has too many errors that are unclear for me, even looking through another sources. I lost too much time doing this, which I think it is not fair.
So I gave up and tried another books and sources. This book at least give a nice introduction to what kind of problems ML tries to solve, but after that, do not keep reading.
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