Read Machine Learning in Biotechnology and Life Sciences

Machine Learning in Biotechnology and Life Sciences

Saleh Alkhalifa

4.9/5 (22002 ratings)

Description:Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guideKey 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 DescriptionThe 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 forThis 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 CloudAbout the AuthorSaleh 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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Machine Learning in Biotechnology and Life Sciences. To get started finding Machine Learning in Biotechnology and Life Sciences, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: 408

Format: PDF, EPUB & Kindle Edition

Publisher: ‎Packt Publishing

Release: 2022

ISBN: 1801811911

Description: Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guideKey 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 DescriptionThe 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 forThis 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 CloudAbout the AuthorSaleh 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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Machine Learning in Biotechnology and Life Sciences. To get started finding Machine Learning in Biotechnology and Life Sciences, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Machine Learning in Biotechnology and Life Sciences

Machine Learning in Biotechnology and Life Sciences

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