Artificial Intelligence
A free, structured learning path covering generative AI concepts, models, prompt engineering, hands-on tools, building with AI, ethics, and a capstone project.
Learn from: the original creator. Original content published on YouTube. DigitalSkillX organizes these public resources into a structured learning path. DigitalSkillX does not claim ownership or partnership.
Lesson video is unavailable. Open the original source on YouTube when a link is provided below.
This learning path is based on the Barnie Free Courses playlist 'Learning 5: Mastering Generative AI'. It guides you through the fundamentals of generative AI, major model types like LLMs and GANs, prompt engineering, text and visual/audio generation, building applications with LangChain and RAG, and the ethical considerations. The path ends with a capstone project where you apply what you've learned. It's designed for tech professionals, creatives, business leaders, and students who want a practical understanding of generative AI.
Beginners who want a structured free introduction before deeper practice.
0 of 0 lessons completed.
Complete this learning path to become eligible for your DigitalSkillX certificate.
Artificial Intelligence
Mastering Generative AI: From Foundations to Real-World Applications
A free learning path covering the core concepts, models, and practical skills in generative AI, from prompt engineering to building real applications.
Artificial Intelligence
Mastering Generative AI: From Foundations to Real-World Applications
A free, self-paced learning path covering the core concepts, models, and tools of generative AI, with hands-on modules and a capstone project.
Programming & AI
Generative AI with Python: From Basics to Real-World Apps
A free, beginner-friendly learning path that teaches core Generative AI concepts and how to build real AI applications with Python and the OpenAI API.
Artificial Intelligence
Generative AI Fundamentals: From Autoencoders to GANs
A free learning path covering core generative AI concepts, model families, and hands-on implementations using TensorFlow and Python.