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Welcome to Machine Learning in 100 Pages, the podcast that takes you through the essential concepts of machine learning in a concise, digestible format. Inspired by Andriy Burkov's best-selling The Hundred-Page Machine Learning Book, this podcast brings you a broad overview of machine learning theory and practice, from foundational topics like linear regression to cutting-edge methods such as deep learning and random forests.
Each episode distills key principles from the book, offering practical insights without overwhelming you with complex mathematics. Whether you're a newcomer eager to get started or a seasoned professional looking for a quick refresher, this podcast delivers clear explanations and actionable advice. With Python code examples and real-world applications, we’ll explore how to apply machine learning techniques efficiently in your own projects, while maintaining a focus on what truly matters for practitioners. Tune in to get a solid foundation in machine learning without spending years in the weeds.
By Future Center Ventures, Mark M. Whelan5
22 ratings
Welcome to Machine Learning in 100 Pages, the podcast that takes you through the essential concepts of machine learning in a concise, digestible format. Inspired by Andriy Burkov's best-selling The Hundred-Page Machine Learning Book, this podcast brings you a broad overview of machine learning theory and practice, from foundational topics like linear regression to cutting-edge methods such as deep learning and random forests.
Each episode distills key principles from the book, offering practical insights without overwhelming you with complex mathematics. Whether you're a newcomer eager to get started or a seasoned professional looking for a quick refresher, this podcast delivers clear explanations and actionable advice. With Python code examples and real-world applications, we’ll explore how to apply machine learning techniques efficiently in your own projects, while maintaining a focus on what truly matters for practitioners. Tune in to get a solid foundation in machine learning without spending years in the weeds.

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