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).
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
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python,… Read More »28 Statistical Concepts Explained in Simple English – Part 18
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python,… Read More »39 Statistical Concepts Explained in Simple English – Part 17
This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists,… Read More »Free Book: Statistics — New Foundations, Toolbox, and Machine Learning Recipes
Originally posted by Michael Grogan. The below is an example of how sklearn in Python can be used to develop a k-means clustering algorithm. The purpose of… Read More »Python: Implementing a k-means algorithm with sklearn
