Author and Publisher at MLtechniques.com. Machine learning scientist, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent also founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target).
The material discussed here is also of interest to machine learning, AI, big data, and data science practitioners, as much of the work is based… Read More »Two New Deep Conjectures in Probabilistic Number Theory
In my previous posts, I compared model evaluation techniques using Statistical Tools & Tests and commonly used Classification and Clustering evaluation techniques In this post,… Read More »Comparing Model Evaluation Techniques
Summary: The annual Burtch Works salary survey tells us a lot about which industries are using the most data scientists and the difference between higher and… Read More »Where’s the Love – Trends in Data Science Career Opportunities
You don’t need a sophisticated model nor advanced machine learning techniques to quickly get a high level picture and trends for bottom-line business metrics. Not… Read More »Growth Modeling for Business Managers and Executives
By Ajit Jaokar and Ayse Mutlu. Exclusively for Data Science Central members, with free access. You can download this book (PDF) here. This tutorial is the second book… Read More »Free Book: Azure Machine Learning in a Weekend
Summary: A new business model strategy based around intermediary platforms powered by AI/ML is promising the most direct path to fastest growth, profitability, and competitive… Read More »A Radical AI Strategy – Platformication
This article is no longer available. To learn AI using Python, you can check the following links: PyBrain Advanced AI: Deep Reinforcement Learning in Python… Read More »How do I learn AI using Python?
Originally posted by Vincent Ajayi. The most common challenge faced by data scientists (DS) and data analysts (DA) is missing data. Every day, both DA… Read More »How to deal with missing data
Many times, complex models are not enough (or too heavy), or not necessary, to get great, robust, sustainable insights out of data. Deep analytical thinking… Read More »Time series, Growth Modeling and Data Science Wizardy
Logistic regression (LR) models estimate the probability of a binary response, based on one or more predictor variables. Unlike linear regression models, the dependent variables are… Read More »Alternatives to Logistic Regression
