Machine Learning in Biotechnology and Life Sciences

Machine Learning in Biotechnology and Life Sciences

eBook Details:

  • Paperback: 408 pages
  • Publisher: WOW! eBook (January 28, 2022)
  • Language:¬†English
  • ISBN-10: 1801811911
  • ISBN-13: 978-1801811910

eBook Description:

Machine Learning in Biotechnology and Life Sciences: Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide

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 Machine Learning in Biotechnology and Life Sciences book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data.

  • 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

By the end of this Machine Learning in Biotechnology and Life Sciences book, you’ll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP.

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