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 Features:

• Learn the applications of machine learning in biotechnology and life science sectors
• Discover exciting real-world applications of deep learning and natural language processing
• Understand the general process of deploying models to cloud platforms such as AWS and GCP

Book 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 Learn:

• 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

Who 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.

探索這本全面指南中為生物科技領域的資料科學家提供成功所需的所有工具和範本。

主要特點：
- 學習機器學習在生物科技和生命科學領域的應用
- 發現深度學習和自然語言處理的令人興奮的實際應用
- 了解將模型部署到AWS和GCP等雲平台的一般過程

書籍描述：
生物科技和生命科學領域正在蓬勃發展，近年來發生了巨大變化。隨著競爭在各個角落不斷增加，全球各地的公司都在尋求數據驅動的方法，如機器學習，以優化流程並降低成本。本書通過實踐學習機器學習的應用，幫助實驗室科學家、工程師和管理人員培養資料科學家的思維方式，以提高生產力和效率。

您將從Python、SQL和數據科學的速成課程開始，從頭開始開發和調整複雜模型，以自動化流程並在生物科技和生命科學領域進行預測。隨著進一步的學習，本書涵蓋了機器學習、深度學習和自然語言處理的一些高級技術，並使用實際數據進行應用。

通過閱讀本書，您將能夠使用AWS和GCP建立和部署自己的機器學習模型，以自動化流程並進行預測。

您將學到：
- 開始使用Python編程和結構化查詢語言（SQL）
- 使用Python從頭開始開發機器學習預測模型
- 微調深度學習模型以優化各種任務的性能
- 了解如何在雲端部署、評估和監控模型
- 理解如何將高級技術應用於實際數據
- 發現如何使用LSTMs和transformers等關鍵深度學習方法

本書適合對象：
本書適合資料科學家和科學專業人士，希望轉向生物科技領域。已在製藥和生物科技行業中建立起基礎的科學專業人士也會發現本書有用。需要具備Python編程的基本理解和初級的數據科學背景，以充分利用本書。