Published Feb 19, 2021

SDS 446: Getting Started in Machine Learning — with Jon Krohn

Jon Krohn delves into effective learning paths for mastering machine learning, offering resources for all skill levels, and emphasizes the importance of continuous education in the rapidly evolving field. He tackles complex concepts like kernel tricks in SVMs and deep reinforcement learning, bridging the gap between theory and practice.

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Dexa/Super Data Science: ML & AI Podcast with Jon Krohn

SDS 446: Getting Started in Machine Learning — with Jon Krohn

Topics covered

Popular Clips

Mastering Deep Q Learning

Understanding Machine Learning

Episode Highlights

Learning Machine Learning

Continuous Learning

Complex Concepts

Related Episodes

SDS 556: @JonKrohnLearns's Machine Learning Courses

SDS 554: @JonKrohnLearns's Deep Learning Courses

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SDS 474: The Machine Learning House — with Jon Krohn

SDS 464: A.I. vs Machine Learning vs Deep Learning — with Jon Krohn

SDS 510: Deep Reinforcement Learning — with Jon Krohn

SDS 470: My Favorite Books — with Jon Krohn

SDS 506: Supervised vs Unsupervised Learning — with Jon Krohn

SDS 448: How to be a Data Science Leader — with Jon Krohn

SDS 444: Future-Proofing Your Career — with Jon Krohn

SDS 469: Learning Deep Learning Together — with Konrad Körding

SDS 472: The Learning Never Stops (so Relax) — with Jon Krohn

SDS 476: Peer-Driven Learning — with Jon Krohn

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SDS 498: How Only Beginners Know Everything — with Jon Krohn

SDS 446: Getting Started in Machine Learning — with Jon Krohn

Topics covered

Popular Clips

Episode Highlights

Learning Machine LearningJon Krohn provides insights into effective learning paths and foundational knowledge essential for mastering machine learning. He shares resources and courses that cater to various skill levels, ensuring a comprehensive understanding of the field.

Learning Machine Learning

Continuous Learning

Complex ConceptsJon Krohn explores the complexities of kernel tricks in support vector machines and the challenges of deep reinforcement learning. He emphasizes the gap between practical execution and theoretical understanding in these advanced machine learning concepts.

Complex Concepts

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