Venelin Valkov

Venelin Valkov's

Get Shit Done with AI Bootcamp

Are you interested in harnessing the transformative power of Artificial Intelligence and Machine Learning? 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.

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.

I designed this AI bootcamp for you, ML/software engineer with understanding of the basics of AI/ML, who wants to take your skills to the next level. It's a practical, hands-on journey through Python for AI, real-world data analysis, ML model development and deployment, culminating in an in-depth exploration of Large Language Models, including customizing your own. This bootcamp is a rare chance to elevate your AI skills with real-world applications and is too good an opportunity to pass up!

What's Inside:

  • Hard Work - If you don't see the value in deeply understanding what is AI and how it works, this course is not for you. Nothing will be easy, but it will be worth it, if you put in the work.

  • Real-World Focus: Gain hands-on experience with projects that go from research to production, preparing you for immediate industry impact.

  • Empowering Knowledge: 16+ hours of learning content designed to transform you into a highly-skilled AI engineer, equipped with both foundational and advanced AI concepts.

  • Flexible Learning: Embrace the freedom of self-paced study, allowing you to balance learning with your personal and professional life.

  • Career Readiness: The job market is competitive, but you'll be ready with a strong portfolio of projects and a solid understanding of the latest developments in AI.

Your AI Career Awaits: Enroll now and start your journey to becoming an AI engineer!


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.