This is the code repository for Machine Learning in Biotechnology and Life Sciences, published by Packt.
Build machine learning models using Python and deploy them on the cloud
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(dfx.drop(columns = ["annotation"]))
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-12).
| Chapter | Software required | OS required |
|---|---|---|
| 1-12 | Python 3 | Windows, macOS, or Linux |
| 1-12 | Jupyter Notebook | Windows, macOS, or Linux |
| 1-12 | MySQL | Windows, macOS, or Linux |
| 1-12 | Anaconda Individual Edition | Windows, macOS, or Linux |
| 1-12 | AWS Account | Any modern-day web browser |
| 1-12 | GCP Account | Any modern-day web browser |
| 1-12 | Git | Windows, macOS, or Linux |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Saleh Alkhalifa is 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.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.
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