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
Book 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.
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
Who this book is 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.
