This 30 minutes video features my interview about the upcoming course “Intuitive Machine Learning”, based on my new book with the same title. Hosted by Victor Chima, co-founder of LearnCrunch.com. Both the course and the book include a solid introduction to scientific computing in Python.
In this presentation, I answer the following questions:
- My background: computer vision, natural language processing (keyword scoring, web crawling), computational statistics and scoring technology. Worked at Visa, Wells Fargo, NBC, eBay, Microsoft.
- Important business problems that the course can help participants solve: taxonomy creation (NLP), fraud detection, shape recognition (computer vision), scoring using boosted / ensemble methods, leveraging rich synthetic data to get more robust predictions, automation of data validation / data cleaning / exploratory analysis, enhanced visualizations with data videos, and model-free inference techniques to name a few.
- Why is it important for AI models to be explainable (or in some cases, legally required).
- Exciting new trends in Machine Learning: synthetic and augmented data, generative models, automated data cleaning, causality detection, explainable AI.
- What is unique about the course that participants can’t get elsewhere? Quickly getting to the point, very little statistics used even when discussing advanced topics. Enterprise-grade projects. Understanding inner workings of Python libraries rather than using them as black-boxes. Creating your own Python library. Finding simple solutions when possible. Learning how to self-learn. Getting personal LinkedIn endorsement / job recommendation from a top ML influencer upon successful completion of the course. Last but not least, you will learn a new regression technique that encompasses and unifies all existing methods in one simple optimization algorithm, and a simplified yet powerful alternative to XGboost.
- Anything else you would like to say to your future students? You will learn how to build a portfolio on GitHub, build a strong presence on LinkedIn, blend your code with third party solutions, and learn how to efficiently find free, good quality resources to solve new problems not discussed in any book or classroom.
About the Author
Vincent Granville is a pioneering data scientist and machine learning expert, founder of MLTechniques.com and co-founder of Data Science Central (acquired by TechTarget in 2020), former VC-funded executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS).
Vincent published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is also the author of “Intuitive Machine Learning and Explainable AI”, available here. He lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory.