Venelin Valkov's
"Get Things Done with AI" Bootcamp
Transform your AI skills with hands-on projects and real-world applications
Join the most comprehensive, industry-driven AI bootcamp used by engineers from Google, Microsoft, Amazon, and Meta:
- Learn by Doing: Dive into hands-on projects and gain practical experience
- Apply Cutting-Edge Concepts: From Deep Learning to Large Language Models
- Be Always Job-ready: Master the latest AI tools and frameworks used in the industry
Ready to elevate your AI skills and make an impact?
About the Bootcamp
"What I cannot build. I do not understand." - Richard Feynman
Ready to revolutionize your AI skills? This bootcamp, tailored for ML/software engineers with a foundational understanding of AI/ML, is your fast track to becoming an in-demand AI engineer. Dive into real-world data, craft and deploy ML models, and explore advanced topics like Large Language Models and Generative AI.
What's Inside:
- Challenge Yourself: This bootcamp pushes you to explore AI's depths. It's demanding but immensely rewarding if you're dedicated to learning.
- Real-World Projects: Gain practical experience by working on projects that bridge theory and application, preparing you for immediate industry impact.
- Comprehensive Learning: With over 16 hours of instructional content, you'll master both basic and advanced AI techniques, becoming a well-rounded AI engineer.
- At your Own Pace: Designed for busy professionals, this course allows you to learn at your own pace, fitting seamlessly into your schedule.
- Career Preparation: Equip yourself with a robust portfolio and deep understanding of the latest AI trends, ready to conquer a competitive job market.
Your Path to AI Excellence: Don't miss this chance to transform into an AI professional. Sign up now and start your journey to success!
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?
Part 4: Agents
- Build Agentic Apps: agents are the future of AI. Learn how to build your own team of agents that can interact with the world and make decisions.
- Agents with Llama 3 and Custom Tools: use open LLM and create custom tools to do data analysis
- Tweet Writing Agents in Action: build a team of agents that can research and write tweets using free tools
- SQL Agents: build agents that can interact with SQL databases and perform complex queries