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

Description

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 Cover Title Page Copyright and Credits Contributors Table of Contents Preface Section 1: Getting Started with Data Chapter 1: Introducing Machine Learning for Biotechnology Understanding the biotechnology field Combining biotechnology and machine learning Exploring machine learning software Python (programming language) MySQL (database) AWS and GCP (Cloud Computing) Summary Chapter 2: Introducing Python and the Command Line Technical requirements Introducing the command line Creating and running Python scripts Installing packages with pip When things don't work… Discovering the Python language Selecting an IDE Data types Tutorial – getting started in Python Creating variables Importing installed libraries General calculations Lists and dictionaries Arrays Creating functions Iteration and loops List comprehension DataFrames API requests and JSON Parsing PDFs Pickling files Object-oriented programming Working with Rdkit and BioPython Working with Small Molecules and Rdkit Summary Chapter 3: Getting Started with SQL and Relational Databases Technical requirements Exploring relational databases Database normalization Types of relational databases Tutorial: Getting started with MySQL Installing MySQL Workbench Creating a MySQL instance on AWS Working with MySQL Creating databases Querying data Conditional querying Grouping data Ordering data Joining tables Summary Chapter 4: Visualizing Data with Python Technical requirements Exploring the six steps of data visualization Commonly used visualization libraries Tutorial – Visualizing data in Python Getting data Summarizing data with bar plots Working with distributions and histograms Visualizing features with scatter plots Identifying correlations with heat maps Displaying sequential and time-series plots Emphasizing flows with Sankey diagrams Visualizing small molecules Visualizing large molecules Summary Section 2: Developing and Training Models Chapter 5: Understanding Machine Learning Technical requirements Understanding ML Overfitting and underfitting Developing an ML model Data acquisition Exploratory data analysis and preprocessing: Developing and validating models Saving a model for deployment Summary Chapter 6: Unsupervised Machine Learning Introduction to UL Understanding clustering algorithms Exploring the different clustering algorithms Tutorial – breast cancer prediction via clustering Understanding DR Avoiding the COD Tutorial – exploring DR models Summary Chapter 7: Supervised Machine Learning Understanding supervised learning Measuring success in supervised machine learning Measuring success with classifiers Measuring success with regressors Understanding classification in supervised machine learning Exploring different classification models Tutorial: Classification of proteins using GCP Understanding regression in supervised machine learning Exploring different regression models Tutorial: Regression for property prediction Summary Chapter 8: Understanding Deep Learning Understanding the field of deep learning Neural networks The perceptron Exploring the different types of deep learning models Selecting an activation function Measuring progress with loss Deep learning with Keras Understanding the differences between Keras and TensorFlow Getting started with Keras and ANNs Tutorial – protein sequence classification via LSTMs using Keras and MLflow Importing the necessary libraries and datasets Checking the dataset Splitting the dataset Preprocessing the data Developing models with Keras and MLflow Reviewing the model's performance Tutorial – anomaly detection in manufacturing using AWS Lookout for Vision Summary Chapter 9: Natural Language Processing Introduction to NLP Getting started with NLP using NLTK and SciPy Working with structured data Searching for scientific articles Exploring our datasets Tutorial – clustering and topic modeling Working with unstructured data OCR using AWS Textract Entity recognition using AWS Comprehend Tutorial – developing a scientific data search engine using transformers Summary Chapter 10: Exploring Time Series Analysis Understanding time series data Treating time series data as a structured dataset Exploring the components of a time series dataset Tutorial – forecasting demand using Prophet and LSTM Using Prophet for time series modeling Using LSTM for time series modeling Summary Section 3: Deploying Models to Users Chapter 11: Deploying Models with Flask Applications Understanding API frameworks Working with Flask and Visual Studio Code Using Flask as an API and web application Tutorial – Deploying a pretrained model using Flask Summary Chapter 12: Deploying Applications to the Cloud Exploring current cloud computing platforms Understanding containers and images Understanding the benefits of containers Tutorial – deploying a container to AWS (Lightsail) Tutorial – deploying an application to GCP (App Engine) Tutorial – deploying an application's code to GitHub Summary Index About PACKT Other Books You May Enjoy