Generative AI is reshaping the landscape of artificial intelligence, allowing machines to create text, images, audio, and even answer questions in natural language. But understanding the entire end-to-end process can be complex without structured guidance. This is where an immersive course can be important for software developers looking to master this transformative technology.
We just published a course on the freeCodeCamp.org YouTube channel that will teach you all about generative AI, covering every core aspect from foundational concepts to real-world deployment. Created by Boktiar Ahmed Bappy, this 21-hour course takes you through a comprehensive learning journey with hands-on projects and in-depth explanations of cutting-edge AI tools and techniques.
You'll learn about important topics such as large language models (LLMs), data preprocessing, and advanced methods like fine-tuning and retrieval-augmented generation (RAG). The course includes practical projects with popular tools like Hugging Face, OpenAI, and LangChain, allowing you to build applications ranging from text summarizers and chatbots to custom Q&A systems.
In this course, you'll start by understanding generative AI fundamentals, followed by building a complete generative AI pipeline. You'll dive deep into data preprocessing and vectorization techniques, preparing data for efficient model training. As you progress, you'll explore LLMs, gaining an understanding of transformer architecture, including a detailed look at the revolutionary "Attention is All You Need" paper. From here, you'll work directly with Hugging Face to learn hands-on implementations, including tokenization, feature extraction, and fine-tuning models for specific tasks.
The course also includes real-world projects, such as text summarization, text-to-image, and text-to-speech generation, all using Hugging Face's robust libraries. Then, you'll shift focus to OpenAI's tools, where you'll develop skills in ChatCompletion API and function calling, create a Telegram bot, and finetune a GPT-3 model for tasks like text classification and audio transcription. Advanced projects with DALL-E will further enhance your understanding of creative text-to-image generation.
Beyond individual AI models, this course will teach you about vector databases, essential for storing and retrieving AI-generated embeddings efficiently. With tutorials on databases like ChromaDB, Pinecone, and Weaviate, you'll master the art of vector storage and retrieval, essential for handling large-scale data in generative AI applications. The course then covers LangChain, a powerful framework for managing complex LLM workflows, where you'll explore prompt templates, chain structures, memory management, and more. You'll even build practical applications such as an interview question generator and a custom chatbot for websites.
For those interested in open-source options, the course covers tools like Llama and Falcon, enabling you to use these powerful models within LangChain for versatile application development. An entire section is dedicated to Retrieval-Augmented Generation (RAG), a hybrid method combining the best of retrieval and generative models, with a final project using Google Cloud's Gemini Pro and AWS Bedrock for deployment.
By the end of this course, you'll have a well-rounded skill set, capable of deploying AI applications on both Google Cloud Vertex AI and AWS Bedrock. You'll also gain insight into LLMOps, the operational side of maintaining and scaling AI applications in production. This comprehensive course is packed with invaluable tools and techniques, making it an ideal resource for anyone looking to master the rapidly evolving world of generative AI.
