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A Practical Approach to Learning Machine Learning with Radek Osmulski (Earth Species Project)

Podcast |: Machine Learning Engineered
Publisher |: Charlie You
Media Type |: audio
Podknife tags |: Artificial Intelligence,; Business,; Careers,; Data Science,; Interview,; Machine Learning,; Technology
Categories Via RSS |: Business,; Careers,; Science,; Technology
Publication Date |: Mar 30, 2021
Episode Duration |: 01:38:02

Description
iTunes Summary

Radek Osmulski is a fully self-taught machine learning engineer. After getting tired of his corporate job, he taught himself programming and started a new career as a Ruby on Rails developer. He then set out to learn machine learning. Since then, he's been a Fast AI International Fellow, become a Kaggle Master, and is now an AI Data Engineer on the Earth Species Project.

Learn more about Radek:

https://www.radekosmulski.com

https://twitter.com/radekosmulski

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/newsletter

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Subscribe to ML Engineered: https://mlengineered.com/listen

Comments? Questions? Submit them here: http://bit.ly/mle-survey

Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:15 How Radek got interested in programming and computer science

09:00 How Radek taught himself machine learning

26:40 The skills Radek learned from Fast AI

39:20 Radek's recommendations for people learning ML now

51:30 Why Radek is writing a book

01:01:20 Radek's work at the Earth Species Project

01:10:15 How the ESP collects animal language data

01:21:05 Rapid fire questions

Links:

Radek's Book "Meta-Learning"

Andrew Ng ML Coursera

Fast AI

Universal Language Model Fine-tuning for Text Classification

learning-efficiently.html">How to do Machine Learning Efficiently

NPR - Two Heartbeats a Minute

Earth Species Project

A Guide to the Good Life

The Origin of Wealth

Make Time

You Are Here

Radek details his journey switching careers into software engineering and then into machine learning. He talks about mistakes he made, how he would do it now, and gives a preview of his forthcoming book.