Saleh Alkhalifa - Machine Learning in Biotechnology and Life Sciences: Build machine learning models using Python and deploy them on the cloud
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Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide
Key Features- 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
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 scientists mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time.
Youll 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, youll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP.
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
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.
Table of Contents- Introducing Machine Learning for Biotechnology
- Introducing Python and the Command Line
- Getting Started with SQL and Relational Databases
- Visualizing Data with Python
- Understanding Machine Learning
- Unsupervised Machine Learning
- Supervised Machine Learning
- Understanding Deep Learning
- Natural Language Processing
- Exploring Time Series Analysis
- Deploying Models with Flask Applications
- Deploying Applications to the Cloud
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Build machine learning models using Python and deploy them on the cloud
Saleh Alkhalifa
BIRMINGHAMMUMBAI
Copyright 2022 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Product Manager: Ali Abidi
Senior Editor: David Sugarman
Content Development Editor: Nathanya Dias
Technical Editor: Rahul Limbachiya
Copy Editor: Safis Editing
Project Coordinator: Aparna Ravikumar Nair
Proofreader: Safis Editing
Indexer: Pratik Shirodkar
Production Designer: Joshua Misquitta
Marketing Coordinator: Abeer Riyaz Dawe
First published: January 2022
Production reference: 1221221
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-80181-191-0
www.packt.com
Saleh Alkhalifais a data scientist and manager in the biotechnology industry with 4 years of industry experience working and living in the Boston area. With a strong academic background in the applications of machine learning for discovery, prediction, forecasting, and analysis, he has spent the last 3 years developing models that touch all facets of business and scientific functions.
I would like to dedicate this book to my parents, Esam Alkhalifa and Anna Letz, without whose motivation and support I would not be the scientist I am today.
Indraneel Chakrabortyis a data science enthusiast with an open mindset and a passion for solving data problems with code and building cloud-based data apps. He has both academic and industrial skill sets that include experience in the curation and analysis of clinical trials registry data for insights into policy research and also experience working with biomedical data providing ML/AI-ready solutions. He enjoys coding in Python and R, with lots of Googling, of course!
Dr Neha Kallahas a PhD in biotechnology from Banasthali University, India. During her PhD, she spent time working as a project assistant in an Indo-Danish project and also worked at the Centre for Conservation and Utilization of BGA, Indian Agricultural Research Institute, India. After completing her PhD, she developed associations with some of the most renowned professors and scientists across India and at Cambridge University. Following a long stint in research and academia, she decided to proceed with her career as a data scientist. She is presently working as a senior data scientist in the sphere of AI, offering machine learning-based retail solutions in Germany.
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