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