Machine Learning in Biotechnology and Life Sciences: Build machine learning models using Python and deploy them on the cloud
Author: Saleh Alkhalifa
Publisher finelybook 出版社: Packt Publishing (January 28,2022)
Language 语言: English
Print Length 页数: 408 pages
ISBN-10: 1801811911
ISBN-13: 9781801811910
Book Description
By finelybook
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 scientist’s mindset Author: 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.
Author: 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.
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
