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Becaome a better programmer using Github Copilot.

Programming Effectively with Generative AI (Code Based)

  • DURATION

    2 days

  • WEEKLY

    6 hours

  • FEE

    Contact us

Introduction

The world of programming is rapidly evolving. Generative AI tools, like Github Copilot, are transforming how developers approach their work. This course, “Programming Effectively with Generative AI,” will equip you with the skills to leverage this cutting-edge technology to boost your productivity, efficiency, and code quality.

Why AI Programming is Important

A study by Stack Overflow Developer Survey 2023: found that 70% of developers are already using, or are interested in using, AI-powered coding assistants (https://survey.stackoverflow.co/2023/#ai-sentiment-and-usage). This trend is driven by the significant benefits AI offers:

  • Increased Productivity: AI can automate repetitive tasks like boilerplate code generation and function definitions, freeing up developers to focus on complex logic and core functionalities with 25% Faster initial development. (https://aws.amazon.com/q/developer/)
  • Improved Code Quality: AI can suggest best practices, identify potential errors, and generate code that adheres to industry standards, leading to more robust and maintainable applications.
  • Faster Learning Curve: AI can provide context-sensitive code examples and explanations, helping developers learn new technologies and programming languages more efficiently with up to 40% Developer productivity increase.

Generative AI is revolutionizing the programming landscape. By learning to harness its power, you can become a more efficient, productive, and innovative developer. This course provides the practical skills and knowledge you need to integrate AI seamlessly into your development workflow.

Ready to take your programming skills to the next level? Enroll in “Programming Effectively with Generative AI” today!

Prerequisites

  • Curiosity
  • Basic arithmetic skills - Brackets, division, multiplication, addition, subtraction
  • Ability to operate a computer, keyboard and mouse
  • Ability to use a web browser to access and use the internet
  • Ability to install software on your computer
  • Knowledge of at least one programming/scripting language

Hardware and Software Requirements

  • Physical operational computer (not in virtualization) – Fedora 34 or greater OR PopOS/Ubuntu 20.04 or greater, OR Windows 10 or greater, OR MacOS 10 or greater
  • 16 GB RAM
  • Broadband internet connection > 5 MBPS
  • 50 GB free hard disk space. SSD Drive recommended

Skills, Learning Objectives, and Outcomes

This course will equip you with the following skills:

  • Understanding of generative AI concepts and their application in programming.
  • Proficiency in using Github Copilot and other AI-powered coding tools.
  • Ability to leverage AI for various development tasks, including code generation, debugging, and optimization.
  • Improved coding efficiency and productivity.

Course Outline

Modules 1-3: Introduction, Learning Objectives, and Setting Up the Tools. This section lays the groundwork by introducing generative AI in programming, defining your learning goals, and setting up the necessary tools like Github Copilot.

Modules 4-8: Deep Dive into AI-Assisted Coding. These modules explore how to leverage AI for specific tasks:

  • Module 4: Understanding Github Copilot’s functionalities.
  • Module 5: Explaining programming concepts with AI assistance.
  • Module 6: Generating code templates and boilerplate code.
  • Module 7: Creating custom code snippets with AI.
  • Module 8: Utilizing AI for debugging and troubleshooting code issues.

Modules 9-13: Optimizing Your Development Workflow with AI. Learn how AI can enhance various aspects of your development process:

  • Module 9: AI-powered code optimization for improved performance.
  • Module 10: Leveraging AI for effective code review and best practice suggestions.
  • Module 11: Utilizing AI for automated testing and test case generation.
  • Module 12: Explaining code functionalities with AI assistance for better documentation.
  • Module 13: Working with AI within Jupyter Notebooks for data science workflows.

Modules 14-16: Expanding Your Toolkit and Course Wrap-Up. This section explores alternative AI tools and concludes with a summary of key takeaways.

Fineprint

  • The topics presented are tentative and we reserve the right to add or remove a topic to update or improve the bootcamp, or for a technical or time reasons.
  • † taxes extra.
teacher
Manuj Chandra

Manuj Chandra

Data Science

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