Machine Learning in Biotechnology and Life Sciences: Build machine learning models using Python and deploy them on the cloud

Explore all the tools and templates needed for data scientists to drive  success in their biotechnology careers with this comprehensive guide
Key  FeaturesLearn the applications of machine learning in biotechnology and  life science sectorsDiscover exciting real-world applications of deep  learning and natural language processingUnderstand the general process  of deploying models to cloud platforms such as AWS and GCPBook  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 learnGet started with Python programming and Structured Query  Language (SQL)Develop a machine learning predictive model from scratch  using PythonFine-tune deep learning models to optimize their performance  for various tasksFind out how to deploy, evaluate, and monitor a model  in the cloudUnderstand how to apply advanced techniques to real-world  dataDiscover how to use key deep learning methods such as LSTMs and  transformersWho 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.
Table  of ContentsIntroducing Machine Learning for BiotechnologyIntroducing  Python and the Command LineGetting Started with SQL and Relational  DatabasesVisualizing Data with PythonUnderstanding Machine  LearningUnsupervised Machine LearningSupervised Machine  LearningUnderstanding Deep LearningNatural Language ProcessingExploring  Time Series AnalysisDeploying Models with Flask ApplicationsDeploying  Applications to the Cloud