Week 1 Update
During the first week, I focused on researching AI agents and finding the right learning resources. My goal was to build a strong foundation by understanding core concepts such as:
- Large Language Models (LLMs)
- Agentic AI
- Context
- Tokens
- Other fundamental AI concepts
At the same time, I wanted to follow a practical learning approach by building projects that progress from beginner to advanced levels while covering a wide range of real-world applications.
Initially, I considered pursuing certifications, such as Microsoft's Agentic AI certifications. However, I know that long, theory-heavy courses are difficult for me to stay committed to, and I didn't want to risk purchasing something I might not complete.
Instead, I decided to start with a highly recommended Udemy course by Ed Donner on LLM Engineering. So far, it has been an excellent choice. The explanations are clear, the concepts are easy to grasp, and the course emphasizes hands-on learning. I have now progressed into Week 2 of the course.
What I've Learned So Far
- Understanding frontier models such as Grok, GPT, Claude Sonnet, and DeepSeek.
- Differences between open-source and closed-source language models.
- The benefits and limitations of using LLMs.
- Built a website summarizer using a Gemma model running locally with Ollama and Selenium.
- Currently learning about Transformers and how modern LLMs work under the hood.
Next Steps
- Continue progressing through the LLM Engineering course.
- Gain a deeper understanding of Transformers.
- Build more hands-on AI agent projects with increasing complexity.
- Start experimenting with more advanced agentic workflows.
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