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

Machine Learning in Biotechnology and Life Sciences
Contributors
About the author
About the reviewers
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
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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
    Tutorial – 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
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