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

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

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کتاب یادگیری ماشینی در بیوتکنولوژی و علوم زیستی: مدل‌های یادگیری ماشین را با استفاده از پایتون بسازید و آن‌ها را بر روی ابر مستقر کنید. نسخه زبان اصلی

دانلود کتاب یادگیری ماشینی در بیوتکنولوژی و علوم زیستی: مدل‌های یادگیری ماشین را با استفاده از پایتون بسازید و آن‌ها را بر روی ابر مستقر کنید. بعد از پرداخت مقدور خواهد بود
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توضیحاتی در مورد کتاب Machine Learning in Biotechnology and Life Sciences: Build machine learning models using Python and deploy them on the cloud

نام کتاب : Machine Learning in Biotechnology and Life Sciences: Build machine learning models using Python and deploy them on the cloud
عنوان ترجمه شده به فارسی : یادگیری ماشینی در بیوتکنولوژی و علوم زیستی: مدل‌های یادگیری ماشین را با استفاده از پایتون بسازید و آن‌ها را بر روی ابر مستقر کنید.
سری :
نویسندگان :
ناشر : Packt Publishing
سال نشر : 2022
تعداد صفحات : 408
ISBN (شابک) : 1801811911 , 9781801811910
زبان کتاب : English
فرمت کتاب : pdf
حجم کتاب : 16 مگابایت



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فهرست مطالب :


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
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