Free Ebook , by Manuel Amunategui
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, by Manuel Amunategui
Free Ebook , by Manuel Amunategui
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Product details
File Size: 50427 KB
Print Length: 524 pages
Publisher: Apress; 1st ed. edition (September 12, 2018)
Publication Date: September 12, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07FKDQPSY
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Amazon Best Sellers Rank:
#505,307 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
*This book was a great purchase* - I am just starting to use ML and was not sure how to get started. Monetizing Machine Learning goes into details on how to turn your ideas into a real product using Machine Learning with your end customers in mind. This book goes into important ML concepts like different types of Regressions, TensorFlow, Forecasting, Naive Bayes, Jupyter Notebook and many others. It also goes over NLP and A/B testing which is of great importance while building ML projects. There are several good projects in this book such as, predicting bike rentals to real-time wine quality checker. Other projects included are, trading booth where you create a trading bot and also predicting a number drawn in a canvas. There are lots of other great exciting projects you can learn and build from this book.I am completely 100% satisfied with this book and I suggest everyone to get a copy and try it out. You will learn how to create real products, tackle real world ML problems using latest ML technologies that focuses on your end customers. The best part is that you can use this book and implement the ideas in your current projects easily.
This book is practical and a very good resource for any machine learning professional who want to translate his or her ideas into tangible products consumers can interact with. This kind of material is not taught in academic setting. If you have been building models and you want to serve your models to the end user, then this book is for you.
Monetizing Machine Learning by Manuel Amunategui and Mehdi Roopaei is a very interesting book in the machine learning practical applications arena.In my view, the book is a good match for:• engineers that want to create and rapidly deploy machine learning applications served as web applications,• Students of applied data science• Entrepreneurs trying to prototyping an application and rise capital• Technical and product managersThe distinct pros for reading this books are numerous:• The reader can acquire a variety of practical skills to develop applications beyond data science• Through consuming the content the reader can also understand the nuances of data preparation• The reader will learn practical criteria to select the proper algorithm• The reader will be exposed to the end to end applications design, therefore acquiring an overview on the process end to end and potentially a product view point as well.• The book can be useful to design education material for both technical practitioners and the more technical managers.Areas that may have been curated better:• As most of practical, receipt like books the book can be at times difficult to follow and to read organically if you are not planning on coding and want to get an overview on practical use cases.• More information around the business drivers of the use cases described would be preferable for the technical manager reader.
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