- Paperback: 468 pages
- Publisher: Packt Publishing Limited (28 January 2016)
- Language: English
- ISBN-10: 178439968X
- ISBN-13: 978-1784399689
- Product Dimensions: 19 x 2.7 x 23.5 cm
- Average Customer Review: 3 customer reviews
- Amazon Bestsellers Rank: #2,29,183 in Books (See Top 100 in Books)
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Practical Machine Learning Paperback – Import, 28 Jan 2016
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About the Author
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.
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Top customer reviews
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It covers multiple languages, framework (Python, Julia, R and more).
If you want to begin learning Machine Learning or expertise your skills this book is definitely for you!
Most helpful customer reviews on Amazon.com
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.
The explanations were pretty good in most places, although you can tell the author is not a native English speaker. I liked that there were introductory explanations to most topics, and the author did a good job of giving high level descriptions for how algorithms or tools are used.
In the later chapters the book gives code samples in 5 different frameworks which are Mahout, R, Spark, Python, and Julia. The code samples are not actually in the book but links are given for where to go. I would have preferred to see some code inside of the book as well.
I have the print version and many of the images are not high quality. In some places the author refers to the color of the diagram, although all the images are black and white... I am guessing the Kindle version is in color? There were a few obvious typos in the book as well.
Overall, the author knows the material very well and provides good explanations. The book needs a lot of polishing though to correct formatting, and provide better quality images. I am also thinking the title of the book is slightly misleading as the content is a lot broader than Machine Learning.