Course: Intuitive Machine Learning
This course is based on my book “Intuitive Machine Learning and Explainable AI”, available here. To register, follow this link. To receive updates about my future courses, sign-up to my newsletter, here. The information below provides a brief overview of the course. Course Description Solid machine learning foundations presented by a world leading expert. Full life cycle […]
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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
Entitled “Advanced Machine Learning with Basic Excel”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. I discuss ensemble methods combining many mini decision trees, blended with regression, explained in simple English with both Excel and Python implementations. Case study: natural language processing (NLP) problem. Ideal reading for professionals who […]
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The Sound that Data Makes
It is common these days to read stories about the sound of black holes, deep space or the abyss. But what if you could turn your data into music? There are a few reasons one might want to do this. First, it adds extra dimensions, in top of those displayed in a scatter plot or […]
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Machine Learning Cloud Regression: The Swiss Army Knife of Optimization
Entitled “Machine Learning Cloud Regression: The Swiss Army Knife of Optimization”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. Many machine learning and statistical techniques exist as seemingly unrelated, disparate algorithms designed and used by practitioners from various fields, under various names. Why learn 50 types of regressions when […]
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Weird Random Walks: Synthetizing, Testing and Leveraging Quasi-randomness
Entitled “Weird Random Walks: Synthetizing, Testing and Leveraging Quasi-randomness”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. I discuss different types of synthetized random walks that are almost perfectly random, in one and two dimensions. Besides the theoretical interest, it provides new modeling tools, especially for physicists, engineers, natural […]
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New Perspective on the Riemann Hypothesis
Entitled “New Perspective on the Riemann Hypothesis”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. In about 10 pages (plus Python code, exercises and figures), this article constitutes a scratch course on the subject. It covers a large range of topics, both recent as well as unpublished, in a […]
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Synthetic Data in Machine Learning: What, Why, How?
In this episode, Nicolai Baldin (CEO) and Simon Swan (Machine Learning Lead) of Synthesized are welcoming the founder of Data Science Central and MLTechniques.com Vincent Granville to discuss synthetic data generation, share secrets about Machine Learning on synthetic data, key challenges with synthetic data, and using generative models to solve issues related to fairness and […]
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