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

Start now

About the Bootcamp

"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!

Curriculum

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?