

Hadelin de Ponteves
AI educator whose courses have been taken by over 3 million students. Co-founder of CloudWolf and BravoTech.ai.
Top 5 podcasts with Hadelin de Ponteves
Ranked by the Snipd community

141 snips
Jan 14, 2025 • 1h 29min
853: Generative AI for Business, with Kirill Eremenko and Hadelin de Ponteves
Join Kirill Eremenko, CEO of Super Data Science, and Hadelin de Ponteves, a leading AI educator, as they delve into the transformative power of foundation models for businesses. They discuss the eight-step lifecycle for implementing these models, offer criteria for selecting the best fit, and explore clever customization techniques. The duo also introduces AWS generative AI tools, making it easier for companies to leverage AI without breaking the bank. Their insights are a treasure trove for anyone looking to navigate the generative AI landscape!

Apr 18, 2023 • 1h 3min
671: Cloud Machine Learning
Kirill Eremenko and Hadelin de Ponteves discuss the importance of learning cloud computing for data scientists, essential AWS services, database options, running analytics, and the benefits of AWS certification on the podcast.

Jan 31, 2023 • 1h 22min
649: Introduction to Machine Learning
Data science instructors Kirill Eremenko and Hadelin de Ponteve discuss essential ML concepts like logistic regression, feature scaling, and the Elbow Method. They introduce their new course and cover supervised vs unsupervised learning, false positives/negatives, and linear regression assumptions.

Sep 14, 2021 • 46min
505: From Data Science to Cinema
Data science educator turned actor Hadelin de Ponteves discusses his transition to a cinema career, the importance of sleep for productivity, decision-making processes, and the contrasts between Bollywood and Hollywood industries.

Apr 26, 2017 • 1h 3min
047: An Expert Overview of the Deep Learning Models, Supervised and Unsupervised
Deep Learning expert Hadelin de Ponteves discusses supervised and unsupervised deep learning models, comparing deep learning to machine learning, applications of CNNs and RNNs, self-organizing maps for fraud detection, and the roles of 'artist' and 'engineer' in data science