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Learn Autonomous Programming with Python  
Utilize Python’s capabilities in artificial intelligence, machine learning, deep learning and robotic process automation (English Edition)
Author(s): Varun P Divadkar
Published by BPB Publications
ISBN: 9789355517630
Pages: 324
https://doi.org/9789355517630

EBOOK (EPUB)

ISBN: 9789355517630   Price: INR 799.00
  
 

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Table of contents

Subject(s): Machine Learning, Deep Learning, Artificial Intelligence, Robotic Process Automation, Orchestration, Automation, Hyperautomation
The current industry (also called Industry 4.0) has witnessed an unprecedented expansion of technology in a short span of time, owing to an exponential increase in computational power coupled with internet technology. Consequently, domains like artificial intelligence, machine learning, deep learning and robotic process automation have gained prominence and become the backbone of organizations, making it inevitable for professionals to upgrade their skills in these domains. Orchestrate your work with AI and ML. Learn RPA's power, conduct web symphonies, utilize spreadsheets, and automate emails. You can also extract data from PDFs and images, choreograph applications, and play with deep learning. Design workflows, create hyperautomation finales, and combine Python with UiPath. You can further build a solid stage for your projects with PyScript, and continue with test automation. This book equips you to revolutionize your work, one Python script at a time. This book can be used as ready to reference as well as a user manual for quick solutions to common organizational needs and even for brushing up on key technical domain concepts.
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Description
Subject(s): Machine Learning, Deep Learning, Artificial Intelligence, Robotic Process Automation, Orchestration, Automation, Hyperautomation
The current industry (also called Industry 4.0) has witnessed an unprecedented expansion of technology in a short span of time, owing to an exponential increase in computational power coupled with internet technology. Consequently, domains like artificial intelligence, machine learning, deep learning and robotic process automation have gained prominence and become the backbone of organizations, making it inevitable for professionals to upgrade their skills in these domains. Orchestrate your work with AI and ML. Learn RPA's power, conduct web symphonies, utilize spreadsheets, and automate emails. You can also extract data from PDFs and images, choreograph applications, and play with deep learning. Design workflows, create hyperautomation finales, and combine Python with UiPath. You can further build a solid stage for your projects with PyScript, and continue with test automation. This book equips you to revolutionize your work, one Python script at a time. This book can be used as ready to reference as well as a user manual for quick solutions to common organizational needs and even for brushing up on key technical domain concepts.
Table of contents
Table of Contents
  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Dedication
  5. About the Author
  6. About the Reviewer
  7. Acknowledgement
  8. Preface
  9. Table of Contents
  10. 1. Why Python for Automation?
    1. Introduction
    2. Structure
    3. Objectives
    4. Python as an open-source language
    5. Python’s repository of extensive libraries
    6. Python as a high-level language
    7. Portability aspect of Python
    8. Salient advantages of Python
    9. Conclusion
  11. 2. RPA Foundations
    1. Introduction
    2. Structure
    3. Objectives
    4. History of Robotic Process Automation
    5. What is RPA
    6. Components of RPA
    7. Various RPA tools in the market
    8. Comparison between various RPA tools
    9. RPA Python package
    10. Practical use case of RPA with Python
    11. Conclusion
  12. 3. Getting Started with AI/ML in Python
    1. Introduction
    2. Structure
    3. Objectives
    4. Background and history of AI
    5. Machine learning concepts
    6. Supervised and unsupervised learning
    7. Popular Python Libraries for ML
    8. Reinforcement learning
    9. Deep learning
    10. Introduction to neural networks
      1. Types of neural networks
    11. Natural language processing
    12. Transformers and large language models
    13. Conclusion
  13. 4. Automating Web Scraping
    1. Introduction
    2. Structure
    3. Objectives
    4. What is web scraping
    5. Popular Python libraries for web scraping
      1. The requests module in Python
      2. The Beautiful Soup Library
      3. Inspecting the web page
    6. Extracting information from the Web Page
    7. Legal considerations of web scraping
    8. Practical use case in Python
    9. Conclusion
  14. 5. Automating Excel and Spreadsheets
    1. Introduction
    2. Structure
    3. Objectives
    4. Need for automating Excel using Python
    5. Introduction to openpyxl library
    6. Open and modify an existing workbook
    7. Access a cell using Range name
    8. Merging cells
    9. Looping through cells
    10. Working with Excel formulae using openpyxl
    11. Create charts using openpyxl
    12. Styling a chart
    13. Other Python libraries for Excel automation
    14. Comparison summary of Python libraries
    15. Practical use case in Python
    16. Conclusion
  15. 6. Automating Emails and Messaging
    1. Introduction
    2. Structure
    3. Objectives
    4. Prerequisites for Gmail automation
      1. Turning on 2-step verification for Gmail
      2. Getting app password
      3. Sending a Gmail message using Python
    5. Automating WhatsApp messaging
    6. Practical use case in Python
    7. Conclusion
  16. 7. Working with PDFs and Images
    1. Introduction
    2. Structure
    3. Objectives
    4. PyPDF library
    5. Read a PDF file using PyPDF2
    6. Rotate and merge PDF files
    7. Working with images using the PIL library
    8. Optical character recognition
    9. Working with OpenCV
    10. Practical use case in Python
    11. Conclusion
  17. 8. Mechanizing Applications, Folders and Actions
    1. Introduction
    2. Structure
    3. Objectives
    4. The os module in Python
    5. The shutil module in Python
      1. Copy and move a file using shutil
      2. Move files based on extension using shutil
    6. Using the PyAutoGUI Library
      1. Implementing basic mouse functions using PyAutoGUI
      2. Implementing basic keyboard functions using PyAutoGUI
      3. Exploring message box functions using PyAutoGUI
    7. Practical use case in Python
    8. Conclusion
  18. 9. Intelligent Automation Part 1: Using Machine Learning
    1. Introduction
    2. Structure
    3. Objectives
    4. Implementing supervised machine learning algorithms using Python
      1. Linear regression
    5. Key concepts in Machine Learning models
    6. Logistic regression
    7. K nearest neighbors
    8. Naïve Bayes
    9. Support vector machines
    10. Decision trees
    11. Implementing unsupervised learning algorithms using Python
      1. Dimensionality reduction
      2. Principal component analysis
      3. Linear discriminant analysis
      4. K means clustering
    12. Practical use case in Python
    13. Conclusion
  19. 10. Intelligent Automation Part 2: Using Deep Learning
    1. Introduction
    2. Structure
    3. Objectives
    4. Implementing a neural network in Python
    5. Backpropagation
    6. Popular Python libraries for deep learning
    7. Deep learning applications
    8. Natural language processing
    9. Practical use case in Python
    10. Conclusion
  20. 11. Automating Business Process Workflows
    1. Introduction
    2. Structure
    3. Objectives
    4. Understanding a business process workflow
    5. Introduction to orchestration
    6. Automation versus orchestration: Differences
    7. Orchestration platforms available in market
    8. Achieving orchestration with Python
      1. Prefect
      2. Luigi
    9. Practical use case in Python
    10. Conclusion
  21. 12. Hyperautomation
    1. Introduction
    2. Structure
    3. Objectives
    4. Defining hyperautomation: What it is and why it matters
      1. The hyperautomation cycle: Key steps and processes
      2. Exploring typical use cases for hyperautomation
      3. Enhancing document understanding with optical character recognition
    5. Implementing conversational agents: The role of chatbots
    6. Advancing efficiency with robotic process automation
    7. Navigating the challenges of hyperautomation
    8. Practical use case in Python
    9. Conclusion
  22. 13. Python and UiPath
    1. Introduction
    2. Structure
    3. Objectives
    4. Setting up the Python environment in UiPath
    5. Exploring Python activities in UiPath
    6. Creating the Python script
    7. Integrating Python with UiPath
    8. Conclusion
  23. 14. Architecting Automation Projects
    1. Introduction
    2. Structure
    3. Objectives
    4. Introduction to virtual environment
      1. Setting up a virtual environment
      2. Virtual environment directories at a glance
      3. Additional considerations involving a virtual environment
    5. Python PIP revisited
      1. Performing basic operations using pip
      2. Working with the requirements.txt file
      3. Using Docker for containerization
    6. Conclusion
  24. 15. The PyScript Framework
    1. Introduction
    2. Structure
    3. Objectives
    4. Introduction to PyScript
    5. Creating a basic webpage using PyScipt
    6. Adding working Python code to the webpage
    7. Using third party libraries with PyScript
    8. Referencing external Python files in PyScript
    9. Conclusion
  25. 16. Test Automation in Python
    1. Introduction
    2. Structure
    3. Objectives
    4. Introduction to Selenium
      1. Setting up the Selenium Python API
      2. Exploring web automation with Selenium Python API
    5. Pytest library
      1. Advantages and limitations of Pytest
    6. Python Robot Framework
      1. Running test cases in the Python Robot Framework
    7. Conclusion
  26. Index
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