About this book
The booming field of biotechnology has seen drastic changes over the last few years. Pricing pressures from the government are driving drug prices down, thus increasing company interests in cost saving, which is currently being accomplished through data-driven decisions with machine learning. This book bridges the gap between the technical skillset and general mentality of data scientists, a must-have for scientists to contribute to the major cost-saving endeavors that companies have embarked on.
The book takes a hands-on approach to implementation that will help scientists and developers working with machine learning in biotechnology to get up and running and productive in no time. You'll begin by learning how to develop, tune, and deploy sophisticated machine learning and deep learning models from scratch for automation and cost savings in the biotechnology space. As you advance, the book will show you how to apply dimensionality reduction techniques to real-world data. Finally, you'll get to grips with production examples in the application of regression for property prediction and breast cancer prediction via classification techniques.
By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate and make predictions using Heroku and AWS efficiently.
- Publication date:
- January 2022