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Building Machine Learning Powered Applications: Going from Idea to Product Paperback – 31 January 2020
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About the Author
Emmanuel Ameisen has worked for years as a Data Scientist. He implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Recently, Emmanuel has led Insight Data Science's AI program where he oversaw more than a hundred machine learning projects. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France's top schools.
- Publisher : O'Reilly Media, Inc, USA; 1st edition (31 January 2020)
- Language : English
- Paperback : 250 pages
- ISBN-10 : 149204511X
- ISBN-13 : 978-1492045113
- Dimensions : 17.78 x 1.4 x 23.34 cm
- Best Sellers Rank: 94,741 in Books (See Top 100 in Books)
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The best analogy I can give about this book is its like going to a restaurant seeing "beef stew" on the menu and ordering it. When it arrives, you realize it is just beef broth and when you complain to the waiter they tell you "beef was stewed in it, but you have to pay extra for the actual beef". Hence the title of my review.
Chapter after chapter I kept waiting for him to dive into the python scripts and explaining how they build the model. In this 250 page book, maybe 30 of the pages are dedicated to explaining the model and pipeline with the rest dedicated to superficially explaining DS/ML concepts.
It doesn't go deep enough for anyone who has an intermediate level of knowledge DS/ML. On the other hand, it doesn't explain enough for people who might be beginners. For example, it just assumes you understand when to apply XGBoost versus using Scikit-Learn. But then on the next page it tries to explain the K Nearest Neighbors algorithm. Like you are expecting the reader to understand how different Machine Learning libraries affect computational needs, but then assume they don't know the most basic clustering algorithm? What???
To me it feels more like a hastily written, half white paper/half wiki article about DS/ML algorithms, Computer Science, and how Machine Learning is actually bad for humanity.
Also, he interviews people who have DS/ML experience, which is a good idea and cool in theory, but some of the interviews just feel like sales pitches for their products. Like, I haven't used StictchFix, and it might be a great product, but I will go to their website to learn about it. I don't want to pay to read a sales pitch.
I wish I could return this book, but have already highlighted it from from to back. Please don't buy this book unless you fall into whatever very niche group this author targeted the book towards. Instead buy Hands on Machine Learning if you want to learn more about DS/ML. If you want to know how to deploy your models, maybe try Applied Data Science 2.0, but due to version updates and dependencies I couldn't get it to deploy, but the reference on how to build the pipeline is useful.
To me this book felt like a lot of bad Medium or Towards Data Science articles stacked on top of each other.