Course: Intuitive Machine Learning and Explainable AI
This live course is based on my book “Intuitive Machine Learning and Explainable AI”, available here. Participants will receive a free copy of this book. The information below provides a brief overview of the course. See also my video presentation here, and a 6-min video extract from this live course, here. To be notified about […]
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Machine Learning Dictionary
Top entries are in bold, and sub-entries are in italics. This dictionary is from my new book “Intuitive Machine Learning and Explainable AI”, available here and used as reference material for the course with the same name (see here). These entries are cross-referenced in the book to facilitate navigation, with backlinks to the pages where […]
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Explainable AI, Blackboxes and Synthetic Data
In this 45 minutes podcast hosted by Ben Cole, Executive Editor at TechTarget, Vincent Granville, founder of MLTechniques.com and Executive ML Scientist, discusses the following topics: This podcast comes with two attachments (videos) that further illustrate the problems discussed: In this Q&A, Vincent offers solutions to problems such as creating rich and meaningful synthetic data, […]
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New Book: Intuitive Machine Learning and Explainable AI
Intuitive Machine Learning with focus on explainable AI, human-friendly intelligence, powerful visualizations and applications. By Vincent Granville Ph.D, published in September 2022. PDF format, 156 pages. Version 1.0 with Python code. The book is available here. For my upcoming course based on this book, see here. This book covers the foundations of machine learning, with modern […]
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Advanced Machine Learning with Basic Excel: Simple Alternative to XGBoost
Entitled “Advanced Machine Learning with Basic Excel”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. Also discussed in details with Python code in chapter 2 in my book “Intuitive Machine Learning and Explainable AI”, available here. I discuss ensemble methods combining many mini decision trees, blended with regression, explained in […]
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