Название: Learn Autonomous Programming with Python: Utilize Python's capabilities in Artificial Intelligence, Machine Learning, Deep Learning and robotic process automation Автор: Varun P Divadkar Издательство: BPB Publications Год: 2024 Страниц: 324 Язык: английский Формат: epub (true) Размер: 26.6 MB
Unleash the hidden potential of Python to emerge as a change maker of contemporary industry.
Key Features:
- Explore Python commands for RPA, workflows and hyperautomation. - Concise chapters with lucid examples and elaborate codes that make learning interesting. - Practical industry use case at the end of every chapter to highlight its real world application.
Description:
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.
Each chapter would end with an industry relevant use case in Python. These use cases are golden exercises in brushing up ones potential to add value and have been designed keeping in mind the demands of today’s industry and the salient skills and abilities required to fulfil them.
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.
What you will learn:
- You will have a clear understanding of Python and create concise, flexible and maintainable applications for current industry needs. - You will explore web scraping techniques using powerful libraries to extract valuable data from the web. - You will have a high level overview of fundamentals in ML, deep learning, RPA, and hyperautomation. - You will learn to write compact and maintainable code in Python catering to typical applications in contemporary industries. - You will also learn how to apply your learnings to real world industry scenarios using the practical Python use cases presented at the end of each chapter.
The Chapter 1 introduces the reader to the significance of Python as an open-source language and further delves deeper to explain why it is the most powerful and formidable tool for autonomous programming. The chapter emphasizes Python’s inherent flexibility, adaptability, and user-friendly nature, attributed to its high-level language and straightforward syntax. It also throws insights into Python’s flexibility and adaptability considerations and ease of use as a high-level language with a simple syntax. Further into the chapter, Python’s rich assortment of libraries like ‘Pandas’, ‘NumPy’, ‘Matplotlib’ would be discussed which are pivotal for machine learning. In subsequent chapters, we will delve deeper into the practical implementations of these libraries.
Who this book is for: This book is specifically meant for students and professionals who have prior working knowledge of Python from a basic to intermediate level and would want to expand their horizon of Python programming.
1. Why Python for Automation? 2. RPA Foundations 3. Getting Started with AI/ML in Python 4. Automating Web Scraping 5. Automating Excel and Spreadsheets 6. Automating Emails and Messaging 7. Working with PDFs and Images 8. Mechanizing Applications, Folders and Actions 9. Intelligent Automation Part 1: Using Machine Learning 10. Intelligent Automation Part 2: Using Deep Learning 11. Automating Business Process Workflows 12. Hyperautomation 13. Python and UiPath 14. Architecting Automation Projects 15. The PyScript Framework 16. Test Automation in Python
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