My ML Journey
A mostly chronologicial listing of my ML exposure
2012
- enrolled in University of Louisville graduate 'certificate' program in Data Science
- Data Mining With Linear Models: CECS-694
- using Enterprise SAS for linear model applications
- Big Data: Document-oriented Databases: CECS-694-50
- mongodb, hadoop, semester project
- decided to discontinue, the program felt 'long in the tooth' compared to industry
- Data Mining With Linear Models: CECS-694
- supported 'similarity search' application for million product catalog using automatic and user-supplied tags for images associated with products
2013
- completed Andrew Ng's original 'Machine Learning' course at Coursera
2014
- online course "Hilary Mason: An Introduction to Machine Learning with Web Data"
- online course "More Data Mining With Weka"
- necessary local install and use of weka toolset
2017
- achieved Deep Learning Specialization from DeepLearning.ai (founded by Andrew Ng)
2020
- worked through 'Practical Deep Learning for Coders', Jeremy Howard's PyTorch-based course and library
2021
- Deep Learning With Graphs
- study group based on CS224w class at Stanford
2022
- revisiting the 2022 updates to 'Practical Deep Learning for Coders'
- ran through all of Jeremy Howard's Live Coding sessions
2023
- weekly remote meetup of TWIML Generative AI Study Group
- all the DeepLearning.ai short courses on LLMs
-- made repos of some of them in order to run independent of the course kernel (LangChain for LLMs, Agents)
2024
- continue weekly GenAI Study Group
- contributed to a Google Hackathon project - LLM-based evaluator for RAG
- founded a GenAI consultancy / agency NewThink.ai
Kaggle competitions
Numer.ai Competition
- basic linear model entry with minor tuning
- participated with study group participants for their advanced submissions