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
• 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)
  • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
  • Neural Networks and Deep Learning
  • Convolutional Neural Networks

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)

Kaggle competitions

• Cassava leaf disease detection
• Mechanisms of Action (MOA) - Drug Discovery

Numer.ai Competition

• basic linear model entry with minor tuning
• participated with study group participants for their advanced submissions

Religiously Listened to ML Podcasts

• Twiml - This Week In Machine Learning