Overview

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Course Description: This course provides a comprehensive exploration of Generative AI in software development, equipping learners with the knowledge and practical skills to integrate AI models into coding, debugging, and automation workflows. Designed for software engineers and AI enthusiasts, the course covers foundational AI concepts, key models like GPT, Gemini, and Copilot, and hands-on applications through APIs and real-world demos. Learners will gain expertise in AI-powered code generation, debugging, and emerging AI techniques like embeddings, retrieval-augmented generation (RAG), and fine-tuning. The course also delves into the future of AI-driven development, human-AI collaboration, and ethical considerations. Target Audience: -Software Engineers seeking to integrate AI into their development workflows. -AI Enthusiasts interested in leveraging Generative AI for coding and automation. -Data Scientists and Developers exploring AI-driven debugging and optimization. -Product Managers looking to understand AI’s impact on software engineering. -Anyone interested in practical applications of AI tools like GitHub Copilot, ChatGPT, and CodeWhisperer. Learning Objectives By the end of this course, learners will be able to: -Understand Generative AI models and their applications in software development. -Utilize AI tools for code generation, debugging, and optimization. -Apply advanced AI techniques, including embeddings, RAG, and fine-tuning. -Implement AI-powered automation while addressing ethical considerations. -Analyze the evolving role of AI in software engineering and human-AI collaboration. Module 1: Foundations of Generative AI This module introduces the fundamentals of AI, machine learning, and deep learning, providing a solid foundation for software engineers. Learners will explore core Generative AI models, including GPT, Gemini, and LLaMA, and understand their applications in development. Practical demos with AI APIs like OpenAI and Mistral will showcase real-world integrations. Module 2: AI in Software Engineering Building on the foundations, this module focuses on AI-powered coding, debugging, and advanced Generative AI concepts. Learners will explore AI-driven development tools like GitHub Copilot and ChatGPT, understand key concepts such as embeddings and fine-tuning, and analyze the ethical implications of AI in software engineering. The module concludes with insights into the future of AI automation and human-AI collaboration.

Syllabus

Foundations of Generative AI in Software Development

This module introduces learners to the fundamental concepts of Generative AI and its applications in software development. It covers key AI technologies, including Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning, explaining their differences and real-world use cases. Learners will explore the evolution of Generative AI, comparing it with Discriminative AI, and understand how these models contribute to tasks such as content creation, classification, and predictive analysis. Additionally, the module examines the latest AI models, such as GPT, Gemini, and Copilot, showcasing their role in software engineering. By the end of this module, learners will have a strong foundation in Generative AI, preparing them for advanced applications in coding, automation, and AI-driven software development.

AI in Software Engineering

This module explores the transformative role of AI in modern software engineering. It covers AI-powered code generation, debugging, and optimization, demonstrating how tools like GitHub Copilot, ChatGPT, and CodeWhisperer assist developers in writing efficient, maintainable, and error-free code. Learners will also delve into advanced AI concepts such as embeddings, retrieval-augmented generation (RAG), and fine-tuning, gaining insights into their applications in real-world software development. The module concludes with discussions on the future of AI in software engineering, human-AI collaboration, and the ethical considerations developers must address when integrating AI into their workflows.