I’m not saying you need to be an expert in advanced calculus to do machine learning…
BUT, there is a big difference between someone that does vs someone that does NOT have a good foundation in stats when it comes to getting & explaining business results.
My thought process back in the day was to obtain a great foundation in stats and machine learning at the same time.
So here’s what helped me. I read a ton of books.
Here are the 3 books that helped me learn data science the most...
1. R for Data Science (Wickham & Grolemund) r4ds.had.co.nz
Agentic AI: A comprehensive survey of architectures, appications, and future directions (A 37 page PDF)
Here are the best parts:
1. The Dual-Paradigm Framework
The survey argues that Agentic AI research must be categorized to avoid conceptual retrofitting (applying old symbolic models to new systems).
2. Key Findings and Implications
Paradigm-Market Fit: The choice of architecture is strategic and dictated by domain constraints (e.g., symbolic for high-stakes, neural for complex data analysis).
🚨BREAKING: New Python library for agentic data processing and ETL with AI
Introducing DocETL.
Here's what you need to know:
1. What is DocETL?
It's a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks.
It offers:
- An interactive UI playground
- A Python package for running production pipelines
2. DocWrangler
DocWrangler helps you iteratively develop your pipeline:
- Experiment with different prompts and see results in real-time
- Build your pipeline step by step
- Export your finalized pipeline configuration for production use