Book Image

Machine Learning in Biotechnology and Life Sciences

By : Saleh Alkhalifa
Book Image

Machine Learning in Biotechnology and Life Sciences

By: Saleh Alkhalifa

Overview of this book

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.
Table of Contents (17 chapters)
1
Section 1: Getting Started with Data
6
Section 2: Developing and Training Models
13
Section 3: Deploying Models to Users

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in the text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(dfx.drop(columns = ["annotation"]))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

>>> heterogenousList[0]
dichloromethane
>>> heterogenousList[1]
3.14 

Any command-line input or output is written as follows:

$ mkdir machine-learning-biotech

Bold: Indicates a new term, an important word, or words that you see on screen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Select System info from the Administration panel."

Tips or Important notes

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