A machine learning podcast that explores more than just algorithms and data: Life lessons from the experts. Welcome to "Learning from Machine Learning," a podcast about the insights gained from a career in the field of Machine Learning and Data Science. In each episode, industry experts, entrepreneurs and practitioners will share their experiences and advice on what it takes to succeed in this rapidly-evolving field.
But this podcast is not just about the technical aspects of ML. It will also delve into the ways machine learning is changing the world around us. From the implications of artificial intelligence to the ways machine learning is being applied in various sectors, a wide range of topics will be covered that are relevant to anyone interested in the intersection of technology and society.
All interviews available on YouTube: Learning from Machine Learning (https://www.youtube.com/@learningfrommachinelearning)
Substack: Mindful Machines (https://mindfulmachines.substack.com/)
Dan Bricklin: Lessons from Building the First Killer App | Learning from Machine Learning #14
Host [Host Name] interviews Dan Bricklin, co-creator of VisiCalc, as they discuss how breakthrough innovations must be 100 times better and what five decades of platform shifts teach us about today's AI moment.
1 Jul 2025TRANSCRIPT
Lukas Biewald | You think you're late, but you're early | Learning from Machine Learning #13
Lukas Biewald, Weights & Biases CEO, discusses his journey through the AI landscape, from early programming to leading a vital AI tool company, emphasizing conviction over consensus in this interview.
28 May 2025TRANSCRIPT
Maxime Labonne: Designing beyond Transformers | Learning from Machine Learning #12
Maxime Labonne, Head of Post-Training at Liquid AI, discusses his journey into AI and how focusing on model efficiency and data quality shapes the future of machine learning in this interview.
29 Apr 2025TRANSCRIPT
Aman Khan: Arize, Evaluating AI, Designing for Non-Determinism | Learning from Machine Learning #11
Host [Host Name] interviews Aman Khan, Head of Product at Arize AI, about designing for non-determinism and evaluating AI systems as a core machine learning challenge.
25 Oct 2024TRANSCRIPT
Leland McInnes: UMAP, HDBSCAN & the Geometry of Data | Learning from Machine Learning #10
Host Maarten Grootendorst interviews Leland McInnes, creator of UMAP and HDBSCAN, about the geometry of data and transparent algorithm development.
1 Mar 2024TRANSCRIPT
Chris Van Pelt: Machine Learning Tooling, Weights and Biases, Entrepreneurship | Learning from Machine Learning #9
Chris Van Pelt, co-founder of Weights & Biases, shares insights on MLOps tooling and entrepreneurship in this interview.
11 Jan 2024
Michelle Gill: AI-Assisted Drug Discovery, NVIDIA, Biofoundation Models, Creating Applied Research Teams | Learning from Machine Learning #8
Host Seth P. Levine interviews Dr. Michelle Gill, Tech Lead and Applied Research Manager at NVIDIA, about AI-assisted drug discovery and building applied research teams.
Host Seth P. Levine interviews Ines Montani, CEO of Explosion, about generative AI, NLP, and entrepreneurship, offering insights into building a successful software company.
3 Oct 2023
Lewis Tunstall: Hugging Face, SetFit and Reinforcement Learning | Learning from Machine Learning #6
Host Seth Levine interviews Lewis Tunstall, machine learning engineer at Hugging Face, about his journey from quantum physics to NLP and reinforcement learning.
19 May 2023
Paige Bailey: Google Deepmind, LLMs, Power of ML to improve code | Learning from Machine Learning #5
Paige Bailey, Lead Product Manager at Google DeepMind, discusses the transformative power of LLMs and ML in improving code and software design in this interview.
26 Mar 2023
Sebastian Raschka: Learning ML, Responsible AI, AGI | Learning from Machine Learning #4
This episode we welcome Sebastian Raschka, Lead AI Educator at Lightning and author of Machine Learning with Pytorch and Scikit-Learn to discuss the best ways to learn machine learning, his open source work, how to use chatGPT, AGI, responsible AI and so much more. Sebastian is a fountain of knowledge and it was a pleasure to get his insights on this fast moving industry. Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. Resources to learn more about Sebastian Raschka and his work:
https://sebastianraschka.com/ (https://sebastianraschka.com/)
https://lightning.ai/ (https://lightning.ai/)
Machine Learning with Pytorch and Scikit-Learn (https://amzn.to/3z9H88Y)
Machine Learning Q and AI (https://leanpub.com/machine-learning-q-and-ai/)
Resources to learn more about Learning from Machine Learning and the host: https://www.linkedin.com/company/learning-from-machine-learning (https://www.linkedin.com/company/learning-from-machine-learning)
https://www.linkedin.com/in/sethplevine/ (https://www.linkedin.com/in/sethplevine/)
https://medium.com/@levine.seth.p (https://medium.com/@levine.seth.p)
twitter (https://twitter.com/NLP_nerd)
References from Episode
https://scikit-learn.org/stable/ (https://scikit-learn.org/stable/)
http://rasbt.github.io/mlxtend/ (http://rasbt.github.io/mlxtend/)
https://github.com/BioPandas/biopandas (https://github.com/BioPandas/biopandas)
Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch (https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)
Andrew Ng - https://www.andrewng.org/ (https://www.andrewng.org/)
Andrej Karpathy - https://karpathy.ai/ (https://karpathy.ai/)
Paige Bailey - https://github.com/dynamicwebpaige (https://github.com/dynamicwebpaige)
Contents
01:15 - Career Background
05:18 - Industry vs. Academia
08:18 - First Project in ML
15:04 - Open Source Projects Involvement
20:00 - Machine Learning: Q&AI
24:18 - ChatGPT as Brainstorm Assistant
25:38 - Hype vs. Reality
27:55 - AGI
31:00 - Use Cases for Generative Models
34:01 - Should the goal to be to replicate human intelligence?
39:18 - Delegating Tasks using LLM
42:26 - ML Models are overconfident on Out of Distribution
44:54 - Responsible AI and ML
45:59 - Complexity of ML Systems
47:26 - Trend for ML Practitioners to move to AI Ethics
49:27 - What advice would you give to someone just starting out?
52:20 - Advice that you’ve received that has helped you
54:08 - Andrew Ng Advice
55:20 - Exercise of Implementing Algorithms from Scratch
59:00 - Who else has influenced you?
01:01:18 - Production and Real-World Applications - Don’t reinvent the wheel
01:03:00 - What has a career in ML taught you about life?
24 Feb 2023
Nils Reimers: Sentence Transformers, Search, Future of NLP | Learning from Machine Learning #3
This episode welcomes Nils Reimers, Director of Machine Learning at Cohere and former research at Hugging Face, to discuss Natural Language Processing, Sentence Transformers and the future of Machine Learning. Nils is best known as the creator of Sentence Transformers, a powerful framework for generating high-quality sentence embeddings that has become increasingly popular in the ML community with over 9K stars on Github. With Sentence Transformers, Nils has enabled researchers and developers (including me) to train state-of-the-art models for a wide range of NLP tasks, including text classification, semantic similarity, and question-answering. His contributions have been recognized by numerous awards and publications in top-tier conferences and journals.
Resources to learn more about Nils Reimers and his work:
https://www.nils-reimers.de/ (https://www.nils-reimers.de/)
https://www.sbert.net/ (https://www.sbert.net/)
https://scholar.google.com/citations (https://scholar.google.com/citations)?... (https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa0RCZVN1bjJVenVESmxHelJCMjQ0RUFZMXZSQXxBQ3Jtc0trWDlnbTIzcUhiMjVZZTB4dzlCazNPUnYyQ3QxMFZjUERLYjlCUTd4Vmo0TTdoZ2dKaU5jQ3hnbEpZekV5MUcwa0xOQ3VNaFBhZ3lVWVE0Si1sZ1FDOHZxUklGUDBtOGNtVS05UWN5bVhjcFNIeHVaMA&q=https%3A%2F%2Fscholar.google.com%2Fcitations%3Fuser%3D57GA3A8AAAAJ%26hl%3Dde&v=jOj4d3JNBDU)
https://cohere.ai/ (https://cohere.ai/)
Resources to learn more about Learning from Machine Learning:
https://www.linkedin.com/company/learning-from-machine-learning (https://www.linkedin.com/company/learning-from-machine-learning)
https://www.linkedin.com/in/sethplevine/ (https://www.linkedin.com/in/sethplevine/)
https://medium.com/@levine.seth.p (https://medium.com/@levine.seth.p)
Youtube Clips
02:29 (https://www.youtube.com/watch?v=jOj4d3JNBDU&t=149s) What attracted you to Machine Learning?
06:32 What is sentence transformers?
28:02 (https://www.youtube.com/watch?v=jOj4d3JNBDU&t=1682s) Benchmarks and P-Hacking
33:53 (https://www.youtube.com/watch?v=jOj4d3JNBDU&t=2033s) What’s an important question that remains unanswered in Machine Learning?
38:41 (https://www.youtube.com/watch?v=jOj4d3JNBDU&t=2321s) How do you view the gap between the hype and the reality in Machine Learning?
50:45 (https://www.youtube.com/watch?v=jOj4d3JNBDU&t=3045s) What advice would you give to someone just starting out?
52:30 (https://www.youtube.com/watch?v=jOj4d3JNBDU&t=3150s) What advice would you give yourself when you were just starting out in your career?
57:22 (https://www.youtube.com/watch?v=jOj4d3JNBDU&t=3442s) What has a career in ML taught you about life?
31 Jan 2023
Vincent Warmerdam: Calmcode, Explosion, Data Science | Learning From Machine Learning #2
Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. This episode we welcome Vincent Warmerdam, creator of calmcode, and machine learning engineer at SpaCy to discuss Data Science, models and much more. @learningfrommachinelearning
Resources to learn more about Vincent Warmerdam:
https://calmcode.io/ (https://calmcode.io/)
https://youtu.be/kYMfE9u-lMo (https://youtu.be/kYMfE9u-lMo)
https://youtu.be/S7vhi6RjBZA (https://youtu.be/S7vhi6RjBZA)
https://github.com/koaning (https://github.com/koaning)
References from the Episode:
You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place https://amzn.to/3Jt1qjX (https://amzn.to/3Jt1qjX)
The Future of Operational Research is Past https://ackoffcenter.blogs.com/files/the-future-of-operational-research-is-past.pdf (https://ackoffcenter.blogs.com/files/the-future-of-operational-research-is-past.pdf)
Supervised Learning is great - it's data collection that's broken https://explosion.ai/blog/supervised-learning-data-collection (https://explosion.ai/blog/supervised-learning-data-collection)
Deon - An ethics checklist for data scientists https://deon.drivendata.org/ (https://deon.drivendata.org/)
Hadley Wickham - https://hadley.nz/ (https://hadley.nz/)
Katharine Jarmul - https://www.linkedin.com/in/katharinejarmul/?originalSubdomain=de (https://www.linkedin.com/in/katharinejarmul/?originalSubdomain=de)
Vicki Boykis - https://vickiboykis.com/ (https://vickiboykis.com/)
Brett Victor - https://youtu.be/8pTEmbeENF4 (https://youtu.be/8pTEmbeENF4)
Resources to learn more about Learning from Machine Learning:
https://www.linkedin.com/company/learning-from-machine-learning/ (https://www.linkedin.com/company/learning-from-machine-learning/)
https://www.linkedin.com/in/sethplevine/ (https://www.linkedin.com/in/sethplevine/)
https://medium.com/@levine.seth.p (https://medium.com/@levine.seth.p)
9 Jan 2023
Maarten Grootendorst: BERTopic, Data Science, Psychology | Learning from Machine Learning #1
The inaugural episode of Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts.
This episode we welcome Maarten Grootendorst to discuss BERTopic, Data Science, Psychology and the future of Machine Learning and Natural Language Processing.
Towards Data Science Article featuring this interview (https://medium.com/towards-data-science/learning-from-machine-learning-maarten-grootendorst-bertopic-data-science-psychology-9ed9b9b2921)
Resources to learn more about Maarten Grootendorst:
https://www.maartengrootendorst.com/ (https://www.maartengrootendorst.com/)
https://maartengr.github.io/BERTopic/ (https://maartengr.github.io/BERTopic/)
https://www.linkedin.com/in/mgrootendorst/ (https://www.linkedin.com/in/mgrootendorst/)
https://twitter.com/MaartenGr (https://twitter.com/MaartenGr)
https://medium.com/@maartengrootendorst (https://medium.com/@maartengrootendorst)
Resources to learn more about Learning from Machine Learning:
https://www.linkedin.com/company/learning-from-machine-learning/ (https://www.linkedin.com/company/learning-from-machine-learning/)
https://www.linkedin.com/in/sethplevine/ (https://www.linkedin.com/in/sethplevine/)
https://medium.com/@levine.seth.p (https://medium.com/@levine.seth.p)
Inside a recent episode
Dan Bricklin: Lessons from Building the First Killer App | Learning from Machine Learning #14
Published 17 Oct 2025 · Transcript excerpt
[…] Our computer, you could scroll and stop and it would stop. But some who stopped, it still had a few extra keys and kept going. But it had to be that fast. That was a real challenge to make it fit in memory, do all the things that we wanted it to do and how do we decide what not to do? We wanted a great help system. We wanted more functions. We wanted, you know, I mean, you're just, you know, it's deciding what not to do is a very tough thing. And we had to make it easy to understand, you know, to learn. And people learn by using the reference card that, and a manual, they either read the manual or they looked at the reference card or both that tell them if you push this key, it does that. […]
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