Venelin Valkov

Venelin Valkov's

"Get Shit Done with AI" Bootcamp

This AI BootCamp, focused on real-world applications, offers a comprehensive self-paced learning experience that will equip you with the skills and knowledge needed to become great AI engineer. Join now if:

  • You thrive on hands-on learning and gain insights through building

  • You're eager to dive into practical applications and discover how theoretical concepts make sense of the real world

About the Bootcamp


The Bootcamp is work in progress. The preview is now available for MLExpert Pro subscribers. This message will be removed once the Bootcamp hits version 1.0.

"What I cannot build. I do not understand." - Richard Feynman

I've crafted this AI bootcamp specifically for you, an ML/software engineer with a grasp of AI/ML basics, ready to escalate your abilities. This bootcamp is your practical guide through Python for AI, analyzing real-world data, crafting and deploying ML models, all the way to a deep dive into Large Language Models, including how to tailor one yourself. It's a unique opportunity to enhance your AI prowess through tangible projects you won't want to miss!

What's Inside:

  • Hard Work: This course demands a willingness to delve deep into AI's essence and workings. It's challenging, but incredibly rewarding if you're committed to the effort.

  • Practical Application: Immerse yourself in projects that transition from theory to real-world application, setting you up for immediate impact in the industry.

  • Comprehensive Learning: Over 16 hours of instructional content awaits, aiming to develop you into a well-rounded AI engineer proficient in both basic and advanced AI strategies.

  • Learn at Your Own Pace: The course is designed for flexible study, enabling you to integrate learning smoothly into your busy schedule.

  • Preparation for the Future: With a competitive job landscape, this bootcamp prepares you with a robust portfolio and a keen understanding of AI's latest trends.

Your Path to an AI Expert: Sign up now to begin your transformation into an AI professional!


Part 1: Foundational Skills

This part is designed to build a strong foundation in the practical aspects of AI engineering - tools, techniques, and best practices.

  • Python Essentials for AI: review the fundamentals of Python programming for AI applications, including data structures, algorithms, and file handling functions
  • Analyze Your Data For Insigts: how to use libraries such as Pandas, NumPy, and Matplotlib to analyze data and gain practical insights about your datasets
  • Real-World PyTorch: PyTorch is a vast library with many applications in AI and ML. In this section, we'll cover the most important aspects of PyTorch that will help you build your own real-world AI models.

Part 2: ML Pipelines

  • Develop Your Model: train your own model from scratch on a real-world dataset
  • AI Project Template: create a template that will allow you to quickly start new projects, experiment with different models, and deploy your models in the cloud
  • Evaluation Techniques: How do you know your model is good? Learn different evaluation techniques that will help you assess your model's performance.
  • Deploy Your ML App: your model is nothing when it's just in your notebook. Learn how to deploy your model in the cloud and make it accessible to the world.

Part 3: Large Language Models (LLMs)

  • LLMs 101: What are LLMs? How do they work? How do you use them? We'll answer all these questions and more.
  • Write Great Prompts: LLMs are great at generating text, but you need to know how to write great prompts to get the best results
  • Build a RAG System: LLMs don't know anything about your data. How do you give them context? How do you make it relevant? You'll learn how to build a RAG system that works with your own data.
  • Fine-Tune Your Own LLM: the ultimate way to bend LLMs to your will is to fine-tune them on your own data. You'll learn how to do that and how to evaluate your results.
  • Deploy Your LLM: your fine-tuned LLM is ready to go. How do you deploy it and make it accessible to the world?