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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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Machine Learning in Biotechnology and Life Sciences

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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 : 9781801815673
Concepts :

What do you get with eBook?

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Product Details

Publication date : Jan 28, 2022
Length: 408 pages
Edition : 1st
Language : English
ISBN-13 : 9781801815673
Concepts :

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Frequently bought together


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Table of Contents

16 Chapters
Section 1: Getting Started with Data Chevron down icon Chevron up icon
Chapter 1: Introducing Machine Learning for Biotechnology Chevron down icon Chevron up icon
Chapter 2: Introducing Python and the Command Line Chevron down icon Chevron up icon
Chapter 3: Getting Started with SQL and Relational Databases Chevron down icon Chevron up icon
Chapter 4: Visualizing Data with Python Chevron down icon Chevron up icon
Section 2: Developing and Training Models Chevron down icon Chevron up icon
Chapter 5: Understanding Machine Learning Chevron down icon Chevron up icon
Chapter 6: Unsupervised Machine Learning Chevron down icon Chevron up icon
Chapter 7: Supervised Machine Learning Chevron down icon Chevron up icon
Chapter 8: Understanding Deep Learning Chevron down icon Chevron up icon
Chapter 9: Natural Language Processing Chevron down icon Chevron up icon
Chapter 10: Exploring Time Series Analysis Chevron down icon Chevron up icon
Section 3: Deploying Models to Users Chevron down icon Chevron up icon
Chapter 11: Deploying Models with Flask Applications Chevron down icon Chevron up icon
Chapter 12: Deploying Applications to the Cloud Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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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Top Reviews

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Faina Ryvkin Jan 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This new book offers a comprehensive introduction to machine learning tools and data mining in a context of modern biotechnology and data science terminology, presenting a classification of machine learning techniques, surveying the mathematical foundation and main software applications such as expert level of Phyton. It presents illustrative examples of machine learning applications in biology and biotechnology, as well as the challenges and opportunities in these fields. The book might be helpful in other fields as the adoption of data-intensive machine-learning methods can lead to more evidence-based decision-making across health care, education, financial modeling, and marketing. I found this book to be well-organized, logical and clear in the delivery of the material. It can be easily used for self-education as well as for the classroom teaching. I wish, however, the content was divided into parts clearly indicating the introductory and advanced materials to facilitate its reading for the beginners.
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
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
Ultimate Reviewer Jul 16, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a great guide for anyone who wants to get into Biotechnology and life sciences.Some more examples in image Data would have been great. Overall a good resource to learn.
Amazon Verified review Amazon
Nicholas Shepard Mar 17, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Alkhalifa sets out to provide a complete one stop shop for readers to get started with ML in the life sciences. Despite this ambitious of a goal, as a master's student in Computer Science working in the Clinical Data Management field, I can say he has done just that. The content of this book communicates a large breadth of concepts with such clarity that it has made me wish it could have been the first step in my journey instead of having just read it. I am truly in awe of his ability to present such difficult concepts in a way that readers can understand and implement immediately- whether it was something completely new to me or something I thought I already new quite a bit about. I know this book will be invaluable as a tool to continually upskill myself and a comprehensive reference going forward in the field.
Amazon Verified review Amazon
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