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Machine Learning in Biotechnology and Life Sciences
Machine Learning in Biotechnology and Life Sciences

Machine Learning in Biotechnology and Life Sciences: Build machine learning models using Python and deploy them on the cloud

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Profile Icon Alkhalifa
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£9.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (17 Ratings)
Paperback Jan 2022 408 pages 1st Edition
eBook
£32.99
Paperback
£41.99
Subscription
Free Trial
Renews at £9.99p/m
Arrow left icon
Profile Icon Alkhalifa
Arrow right icon
£9.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (17 Ratings)
Paperback Jan 2022 408 pages 1st Edition
eBook
£32.99
Paperback
£41.99
Subscription
Free Trial
Renews at £9.99p/m
eBook
£32.99
Paperback
£41.99
Subscription
Free Trial
Renews at £9.99p/m

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Key benefits

  • Learn the applications of machine learning in biotechnology and life science sectors
  • Discover exciting real-world applications of deep learning and natural language processing
  • Understand the general process of deploying models to cloud platforms such as AWS and GCP

Description

The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You’ll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP.

Who is this book for?

This book is for data scientists and scientific professionals looking to transcend to the biotechnology domain. Scientific professionals who are already established within the pharmaceutical and biotechnology sectors will find this book useful. A basic understanding of Python programming and beginner-level background in data science conjunction is needed to get the most out of this book.

What you will learn

  • Get started with Python programming and Structured Query Language (SQL)
  • Develop a machine learning predictive model from scratch using Python
  • Fine-tune deep learning models to optimize their performance for various tasks
  • Find out how to deploy, evaluate, and monitor a model in the cloud
  • Understand how to apply advanced techniques to real-world data
  • Discover how to use key deep learning methods such as LSTMs and transformers

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 28, 2022
Length: 408 pages
Edition : 1st
Language : English
ISBN-13 : 9781801811910
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Product Details

Publication date : Jan 28, 2022
Length: 408 pages
Edition : 1st
Language : English
ISBN-13 : 9781801811910
Languages :
Concepts :
Tools :

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Frequently bought together


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Total £ 119.97
Bioinformatics with Python Cookbook
£43.99
Machine Learning in Biotechnology and Life Sciences
£41.99
Deep Learning for Genomics
£33.99
Total £ 119.97 Stars icon
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Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(17 Ratings)
5 star 64.7%
4 star 29.4%
3 star 5.9%
2 star 0%
1 star 0%
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Keith Baillargeon Feb 08, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The author's scientific background offers invaluable insight and structure for practically employing machine learning and data science concepts to a wide variety of applications. The text, organization, and step-by-step tutorials (with images!) make the content extremely accessible—and more importantly—digestible.If you're in academia or industry and looking to move beyond the basic principles of programming, then follow along with Saleh as he guides you through complex concepts with ease.Keith Baillargeon, PhD
Amazon Verified review Amazon
Connor Clancy Jan 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Not only has Saleh done a great job of introducing readers to Machine Learning, but he has also done a great job of tying in its application in the Biotechnology and Life Sciences fields. Most authors default to standard learning data sets for new students (Iris, Boston Housing Dataset, Titanic Dataset, etc.), but this book has novel, new, real-word examples that will keep you engaged and solving problems you haven't seen in before. Highly recommend this book to anyone looking to learn more about Machine Learning or Data Science!
Amazon Verified review Amazon
allen price Feb 11, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides a great overview of methods in ML as they apply to life sciences/biotechnology. It covers not only the major packages, but provides a practical guide to understanding the methods, including lots of tips. Also included is a review of the basics: installing packages and getting started in coding. To get the most out of this book, I recommend you have some background in Python coding/development, as the text serves more as a review or reference rather than a complete introduction for the novice. The book covers all the most useful packages for applications of data science to biotechnology. There are several useful tables listing all the major ones (a great reference!). I like particularly the author's discussion on his philosophy on data visualization (chapter 4). The text has some minor issues, which are typical for a first edition, but these should not get in the way of benefiting from this book, especially for the reader who already has some background in the material. A useful reference for any data scientist in the life sciences!
Amazon Verified review Amazon
Christopher J Garvin Jan 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
For anyone new to data science, I highly recommend this book. It will serve as an instruction manual at first, and then as a reference guide and trusted companion over time. The book provides a comprehensive, detailed look at how to initiate, execute, and evaluate a data science project, with specific examples that tie the contents to their application in the life sciences industry. It more than lives up to its "one stop shop" claim, leaving very few stones unturned in its delivery of data science basics. While there is nothing particularly unique about the content, it's the simple yet complete packaging of information that makes this book novel. And it's not devoid of personality, either, as the author's enthusiasm for the subject matter is injected throughout the text. (And as a colleague of his, this does not surprise me at all.)
Amazon Verified review Amazon
Jose Manuel Otero Jan 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have personally worked with Saleh on a meaningful drug development/discovery effort, and have benefited first-hand from his insights, analysis, and expertise. While I am not in expert in the development and/or applications of machine learning in biotechnology/biopharmaceutical research, I'm quite confident that Saleh is representing the current cutting edge methods being employed in world-class industrial research settings. If you don't have the opportunity to work with Saleh, as I have, this book is the next best option.Jose M. Otero, PhDChief Technology Officer at Turnstone Biologics, IncAdvisor and Consultant to various Life Science Organizations
Amazon Verified review Amazon
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