Lvl.2 -> Generative AI Engineer
Pre-requisite: Lvl.1 Machine Learning Apprentice
Approx. Time Required: 2 months
✅ NEW! Join our Mentorship Program for this Career Path here. 👈
Step into the cutting-edge world of Generative AI with this career path designed for those ready to evolve from AI fundamentals to building powerful, real-world applications with Large Language Models (LLMs). Over the course of two months, you’ll gain both theoretical mastery and hands-on experience in the tools and techniques that define the future of AI engineering.
You’ll begin with Artificial Intelligence A-Z, where you’ll build a strong foundation in AI by mastering core concepts, including deep learning, reinforcement learning, and Q-learning. You’ll also get your first exposure to LLMs and transformers through curated podcast content and technical insights.
Next, you’ll dive into Large Language Models A-Z, where you’ll explore the architecture, training, and capabilities of models like ChatGPT. You’ll go deep into concepts like tokenization, self-attention, and fine-tuning, while also learning practical workflows using Hugging Face tools.
Finally, you’ll put everything into practice in AI Engineering Essentials – Part 1. This hands-on course helps you build real-world GenAI applications through guided mini-projects, including an intelligent web-search assistant and a social media post generator. You’ll get comfortable with development environments, frontier models like multimodal LLMs, and emerging GenAI workflows.
Whether you’re a developer or an aspiring AI professional, this path will equip you to innovate, engineer, and lead in the rapidly evolving landscape of Generative AI.
1. Artificial Intelligence A-Z
A comprehensive course that takes you from complete AI beginner to expert.
You’ll master all the essential skills, develop your intuition, and practice your skills on a variety of hands-on applications.
- Develop Q-learning intuition
- Build deep learning and AI for a self-driving car
- Learn and apply Deep Convolutional Q-learning techniques
- Intro to Large Language Models and their fine-tuning
2. Kirill’s podcast episode 747: Technical Intro to Transformers and LLMs
Get a preview of transformers from this episode of the SuperDataScience Podcast. You will learn each element of the architecture, understand what it’s for and how it works:
- 1: Input embedding
- 2: Positional encoding
- 3: Attention mechanism
- 4: Feedforward neural network
- 5: Linear transformation and softmax
Make sure to have the “Attention Is All You Need” paper handy or at least have a look at the architecture image before listening to the podcast.
Embark on a journey through the fascinating world of Large Language Models (LLMs) with this detailed course. From the history of transformers to the intricate mechanisms of ChatGPT, gain a comprehensive understanding of how LLMs generate text and the science behind their capabilities.
- Grasp the foundational concepts of tokenization, input embedding, and self-attention mechanisms.
- Dive deep into the architecture of transformers, including multi-head self-attention and the decoder’s cross-attention.
- Learn the intricacies of training LLMs, including masking techniques and model output generation.
- Explore advanced concepts like LLM parallelization, fine-tuning, and applications in business through practical tutorials with Hugging Face.
By the end of this course, you’ll be well-versed in the workings of LLMs, equipped with hands-on experience in fine-tuning them for specific tasks. Whether you’re looking to integrate LLMs into your projects or seeking to understand the cutting-edge of AI, this course offers the knowledge and skills you need.
4. AI Engineering Essentials – Part 1
A hands-on, practical course designed to kickstart your journey into AI engineering with a focus on Generative AI and frontier models. Perfect for developers and aspiring AI professionals, this course blends theory with mini projects to help you build confidence with Gen AI tools and workflows. You’ll learn how to interact with cutting-edge models, build useful apps, and understand the emerging capabilities shaping the future of AI. What you’ll do:
Understand the core concepts of Generative AI and how it’s reshaping software development.
Set up your development environment for AI engineering in minutes.
Explore frontier models like LLMs and multimodal models, and when to use each.
Build mini-projects including:
Searching the Web Using LLMs – create an intelligent assistant that fetches and summarizes answers from the internet.
Social Media Post Generator – build an app that generates catchy posts for different platforms, tones, and audiences.
Gain the confidence to integrate Gen AI into your own projects and workflows.
By the end of this course, you’ll be ready to build real-world Gen AI apps and take your first confident step into the world of AI engineering.
Plus, Check out these Live Lab Recordings:
Live Lab Recording #3: RAG & LangChain – Enhancing ChatGPT with Retrieval Augmented Generation
👉 Remember to join the Mentorship Program [here] to grow even faster!