How can developers use generative AI for coding?

<text variant="body1">Developers can use generative AI to auto-generate code snippets, complete functions, and suggest bug fixes. AI models like GPT and Gemini support multiple programming languages, making coding faster and more efficient.

Do I need programming experience for this course?

<text variant="body1">No prior AI knowledge is required, but basic programming skills are helpful. The course is beginner-friendly and guides you step-by-step in applying generative AI techniques in real-world coding environments.

What advanced AI concepts will I explore in this course?

<text variant="body1">The course covers embeddings, retrieval-augmented generation (RAG), and fine-tuning to customize generative AI models. These concepts prepare you to build domain-specific applications and optimize performance.

What practical projects or labs are included?

<text variant="body1">Hands-on labs include generating code snippets, debugging with AI, applying RAG for context-aware outputs, and customizing AI-driven applications. These exercises simulate real-world developer tasks.

How will this course help my career in software engineering?

<text variant="body1">By completing this course, you’ll gain in-demand skills in AI-powered coding and debugging, making you competitive for roles in software development, DevOps, and AI-driven engineering.

Will I learn about the future of AI in software engineering?

<text variant="body1">Yes. The course concludes with discussions on the evolving role of AI in development, human-AI collaboration, and the future of automated software engineering.

How long does it take to complete the course?

<text variant="body1">The course takes about 3 hours to complete, with flexible scheduling so you can learn at your own pace. It balances theoretical concepts with hands-on practice to maximize learning efficiency.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Generative AI in Software Development

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Generative AI in Software Development

Dozent: Board Infinity

Bei enthalten

Mehr erfahren

2 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

3 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

2 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

3 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Apply generative AI to code generation, debugging, and testing for faster, error-free development workflows.
Build expertise in AI-powered developer tools like GitHub Copilot, ChatGPT, and CodeWhisperer for software engineering.
Implement embeddings, retrieval-augmented generation (RAG), and fine-tuning to optimize AI-driven applications.
Evaluate ethical issues, collaboration models, and future trends in AI-powered software engineering.

Kompetenzen, die Sie erwerben

Kategorie: Engineering Software
Kategorie: Software Development Tools
Kategorie: Retrieval-Augmented Generation
Kategorie: Embeddings
Kategorie: Responsible AI

Werkzeuge, die Sie lernen werden

Kategorie: Google Gemini
Kategorie: Gemini
Kategorie: AI Workflows

Wichtige Details

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7 Aufgaben

Unterrichtet in Englisch

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In diesem Kurs gibt es 2 Module

The course provides a comprehensive exploration of how generative AI is reshaping software development by accelerating coding, improving debugging, and enhancing automation. It is designed for aspiring software engineers, developers, and professionals who want to integrate AI into modern development workflows to build efficient, scalable, and error-free applications.

You will explore the role of large language models (LLMs) like GPT, Gemini, and LLaMA in coding tasks, software testing, and project automation. The course begins with foundational AI concepts—machine learning, deep learning, and generative models—before diving into practical applications of code generation, prompt engineering, and debugging. Hands-on labs and exercises guide you through AI-powered developer tools such as GitHub Copilot, ChatGPT, and CodeWhisperer. You will also examine advanced AI topics including embeddings, retrieval-augmented generation (RAG), and fine-tuning to customize AI models for specific development needs. Ethical considerations, human-AI collaboration, and the future of AI in engineering are also covered. By the end of this course, you will be able to: - Apply generative AI to accelerate coding, debugging, and software testing. - Use AI-powered tools like Copilot, ChatGPT, and CodeWhisperer to improve productivity. - Implement advanced AI methods such as embeddings, RAG, and fine-tuning. - Evaluate ethical, collaborative, and practical challenges of AI in software engineering. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

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.

Das ist alles enthalten

10 Videos2 Lektüren3 Aufgaben1 Diskussionsthema1 Plug-in

10 Videos Insgesamt 49 Minuten

Introduction to the Course 2 Minuten
Meet your Instructor 1 Minute
Difference between AI, ML, and Deep Learning 6 Minuten
Comparing Generative AI with Discriminative AI 7 Minuten
Evolution and Democratization of AI: From GPT to ChatGPT 4.0 6 Minuten
Key AI Models in Software Development (GPT, Gemini, Copilot, LLaMA) 4 Minuten
OpenAI Models Overview (GPT, Whisper, DALL-E) 3 Minuten
Exploring APIs: OpenAI, Gemini, Mistral and Practical Use 6 Minuten
Practical Demo - Using Mistral with Python/API-Postman 8 Minuten
Prompt Engineering: Effective Techniques for Developers 7 Minuten

2 Lektüren Insgesamt 20 Minuten

Syllabus 5 Minuten
Read more about AI: Foundations, Innovations, and Emerging Trends 15 Minuten

3 Aufgaben Insgesamt 15 Minuten

Practice Quiz: Introduction to Generative AI for Software Engineers 5 Minuten
Practice Quiz: Core Generative AI Models 5 Minuten
Practice Quiz: Exploring API’s and practical Demo’s 5 Minuten

1 Diskussionsthema Insgesamt 10 Minuten

Meet and Greet 10 Minuten

1 Plug-in Insgesamt 5 Minuten

Quick Course Check-In 5 Minuten

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.

Das ist alles enthalten

9 Videos1 Lektüre4 Aufgaben

9 Videos Insgesamt 56 Minuten

Introduction to AI for Code Generation (GitHub Copilot, ChatGPT, CodeWhisperer) 8 Minuten
Writing Efficient Code with AI Assistance 7 Minuten
AI-Driven Debugging: Detecting and Fixing Errors 7 Minuten
Advanced AI Concepts: Embeddings, RAG, Fine-Tuning 15 Minuten
Real-World Project Implementation Using AI Models 7 Minuten
AI and the Future of Software Engineering: Automation vs. Job Creation 4 Minuten
The Human-AI Collaboration: Redefining the Role of Developers 4 Minuten
Ethical Considerations in AI Development 4 Minuten
Course Closure - Gratitude ! 1 Minute

1 Lektüre Insgesamt 15 Minuten

Read more about AI in Software Development: Code Generation, Debugging, Ethics, and Future Trends 15 Minuten

4 Aufgaben Insgesamt 35 Minuten

Practice Quiz: AI-Powered Code Generation and Debugging 5 Minuten
Practice Quiz: Glimpse into advance Gen AI concepts 5 Minuten
Practice Quiz: The Future of Generative AI in Software Engineering 5 Minuten
Graded Quiz 20 Minuten

Dozent

Board Infinity

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Häufig gestellte Fragen

Generative AI in software development automates coding, debugging, and documentation tasks. Tools like GitHub Copilot and ChatGPT enhance developer productivity, reduce errors, and accelerate software delivery.

You will learn how to apply generative AI to software engineering tasks, including code generation, debugging, testing, and optimization. The course also covers advanced topics like embeddings, retrieval-augmented generation (RAG), and fine-tuning, giving you the ability to build AI-driven applications.

The course provides hands-on practice with popular developer tools such as GitHub Copilot, ChatGPT, and CodeWhisperer. You will use these tools to generate code, fix bugs, and accelerate project workflows.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.