Machine Learning in Biotechnology And Life Sciences: 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.

SECTION 1: Getting Started with Data -- 1: Introducing Machine Learning for Biotechnology -- 2: Introducing Python and the Command Line -- 3: Getting Started with SQL and Relational Databases -- 4: Visualizing Data with Python -- SECTION 2: Developing and Training Models -- 5: Understanding Machine Learning -- 6: Unsupervised Machine Learning -- 7: Supervised Machine Learning -- 8: Understanding Deep Learning -- 9: Natural Language Processing -- 10: Exploring Time Series Analysis -- SECTION 3: Deploying Models to Users -- 11: Deploying Models with Flask Applications -- 12: Deploying Applications to the Cloud