Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
Regression and classification are both supervised machine learning techniques that use known data to make predictions. Where they differ is in what type of question you want answer, and… Read More »Difference Between Classification and Regression in One Picture
Basic Terminology in Context – Machine learning should be treated as a culture in an organisation where business teams, managers and executives should have some basic… Read More »Machine Learning(ML) – Basic Terminologies in Context
In supervised machine learning algorithms, Random Forest stands apart as it is arguably the most powerful classification model. When Microsoft developed their X-box game which… Read More »Random Forest Classification explained in detail and developed in R
A Generative model aims to learn and understand a dataset’s true distribution and create new data from it using unsupervised learning. These models (such as… Read More »Which Face is Real? Applying StyleGAN to Create Fake People
This article was written by Daniel McAuley. I recently had the pleasure of speaking on a few panels about analytics to my fellow MBA students… Read More »MBA guide: 8 resources to go from the spreadsheet to the command line
What are the best and worst cases where machine learning algorithms can be applied? Can anyone help me in this?
Let’s start with some simple real-lie examples that we’re sure you all must have experienced. You watch Netflix and it offers you viewing suggestions. Twitter… Read More »Top Machine Learning Tools for Easy Development
Recorded in Bloomberg’s London offices in November 2019: slides here
Summary: High stakes models like those that allocate scarce resources to competing hospitals are headline news. New thinking contrasting model-based versus model-free learning are emerging… Read More »Model Transparency and the Complication of Model-Based vs Model-Free Learning
Bayesian Machine Learning (part-8) Mean Field Approximation Have you ever asked a question, why do we need to calculate the exact Posterior distribution ? To… Read More »Bayesian Machine Learning (Part 8)
