1. Learning Goals

• Understand core AI / ML concepts and terminology
• Build practical projects using Python and modern AI tools
• Apply AI to automation, data analysis, and real business problems
• Stay updated with the latest AI frameworks and models

2. Learning Process

• Step 1 Learn fundamentals: Python, statistics, linear algebra
• Step 2 Study machine learning basics (regression, classification, clustering)
• Step 3 Explore deep learning and large language models (LLMs)
• Step 4 Practice with real projects and datasets
• Step 5 Optimize, deploy, and document AI solutions

3. Tools & Technologies

• Programming: Python, JavaScript
• Libraries: NumPy, Pandas, Scikit-learn, PyTorch / TensorFlow
• AI & LLMs: OpenAI API, LangChain, Embeddings
• Web & Backend: HTML, CSS, Flask / FastAPI
• Automation & Scraping: Playwright, APIs

4. Work List / Projects

• Resume & Job Description matching using embeddings
• Automatic cover letter generation with LLMs
• Job scraping and notification system
• AI-powered email automation
• Market and stock analysis with AI-assisted insights

5. Next Steps

• Improve model accuracy and prompt design
• Deploy AI tools to cloud or server environment
• Build a personal AI portfolio website
• Continue learning and experimenting with new models