Developing AI Applications with Python and Flask

Go to Course: https://www.coursera.org/learn/python-project-for-ai-application-development

Introduction

### Course Review: Developing AI Applications with Python and Flask on Coursera #### Overview The “Developing AI Applications with Python and Flask” course on Coursera is an exceptional opportunity for budding developers who wish to dive into the realm of Artificial Intelligence (AI) application development. This hands-on mini-course effectively bridges the gap between fundamental Python skills and practical AI application creation, making it suitable for those who have a basic understanding of Python and are eager to expand their expertise. #### Course Highlights The course equips learners with essential skills to develop AI-enabled applications while working with Python and Flask. These skills include: - **Developing Functions and Application Logic**: You will learn to write and structure code, ensuring that your applications are efficient and maintainable. - **Exchanging Data with Watson AI Libraries**: By incorporating powerful IBM Watson tools, you’ll enhance your applications with sophisticated AI capabilities, such as Natural Language Processing. - **Writing Unit Tests**: You'll gain insights into writing effective tests to ensure the reliability of your code, a critical practice for professional-grade software. - **Packaging Applications for Distribution**: The course teaches you how to package your applications effectively, readying them for deployment and distribution. #### Syllabus Breakdown The course is segmented into three main modules, each focusing on vital components of AI application development. 1. **Python Coding Practices and Packaging Concepts** - This module sets the foundation by explaining the key differences between web applications and APIs. You’ll engage with the application development lifecycle, learning about best coding practices as outlined by the Python Enhancement Proposal (PEP8). Notably, you’ll delve into static code analysis to ensure your code meets professional standards, alongside learning to create and verify Python packages and execute unit tests. 2. **Web App Deployment Using Flask** - Here, you will be introduced to Flask, a robust micro-framework ideal for web app development. This module tackles the nuances of developing Flask applications, including routing, handling requests/responses, error management, and decorators. By the end, you’ll have practical experience in building and deploying web applications. 3. **Creating AI Applications and Deployment Using Flask** - In the final module, you’ll implement AI capabilities using IBM Watson libraries. You will work on two hands-on projects—a Sentiment Analysis tool and an Emotion Detection application—that challenge you to apply your newly acquired skills. The structure of these projects, including peer assessment, provides a unique opportunity to demonstrate proficiency and receive constructive feedback on your work. #### Recommendations I highly recommend the “Developing AI Applications with Python and Flask” course for several reasons: - **Hands-On Learning**: The course is not just theoretical; it emphasizes hands-on practice, allowing students to apply their skills in real-world applications. - **Practical Skills**: The ability to integrate AI into your web applications is increasingly in demand in tech markets. This course prepares you effectively for such roles. - **Flexible Learning**: As a Coursera course, you enjoy the flexibility of learning at your own pace, making it easier to fit into your busy schedule. - **Access to Resources**: You'll benefit from high-quality resources, including lectures, readings, and community forums, enriching your learning experience. - **Certification**: Completing this course provides you with a certificate to showcase your skills, which can enhance your CV or LinkedIn profile. #### Conclusion In summary, if you are a developer looking to enhance your skill set within the realm of AI and web development, the “Developing AI Applications with Python and Flask” course is well worth your investment. The combination of foundational programming practices, the power of Flask, and the capabilities of IBM Watson will not only bolster your technical abilities but also set you up for a promising career in a rapidly evolving industry. Enroll today and start building the future of AI applications!

Syllabus

Python Coding Practices and Packaging Concepts

In this module, you will start with the basic difference between web applications and APIs. Next, you will learn about the application development lifecycle, from gathering requirements to maintaining the project. You will also get familiar with the best practices for coding as documented in the Python Enhancement Proposal (PEP8) style guide. You will learn about static code analysis, which is used to ensure that the code you write adheres to the coding rules. Next, you will learn how to create and run unit tests. Finally, you will learn how to create, verify, and run Python packages.

In this module, you will be introduced to the definitions of and key differences between Python libraries and frameworks for application development. You will also learn about Flask, a Python-based micro framework used for web deployment of applications. The module will also introduce development and deployment concepts, including routes, request and response objects, error handling, and decorators. After building an API with Flask, you will also learn to deploy web apps using Flask.

In this module, you will be introduced to Embeddable Watson AI libraries. You will also have the opportunity to build two AI-based apps. The practice project will provide you with a challenge to apply your programming skills and incorporate the IBM Watson libraries to build a text-based Sentiment Analysis tool. You will be provided guidance through each step of the project. The final project, Emotion Detection based on the text input, will help you get your skills and proficiency assessed by your peers. For both projects, you will perform unit testing, static code analysis, and incorporate error handling.