In this article, we focus on the notion of supervised machine learning and its associated categories. We briefly discuss the popular categories and algorithms without digging too much into detail. The goal is to have an idea of what is supervised learning.
Read moreIn this article, we outline different steps to define, frame, organize, deploy, and evaluate a successful Machine Learning (ML) project. The expected audience for this article involves business stakeholders, supervisors, Machine Learning experts, and software development engineers.
Read moreMachine Learning, as a tool for Artificial Intelligence, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this article is to provide an insightful overview of Machine Learning by presenting a high-level definition of that and further break it into its associated categories.
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Do you want to start learning Machine Learning and keep hearing buzzwords related to Linear Algebra? Vector, matrix, tensor? Those are basic Linear Algebra Definitions. Here, we introduce some of the most commonly used Linear Algebra definitions.
In this post, we address the importance of Linear Algebra in Machine Learning and discuss why we have to take it seriously!
In this post, we present a working example of the k-nearest neighbor classifier. Previously we covered the theory behind this algorithm. Please refer Nearest Neighbor Classifier – From Theory to Practice post for further detail. A Recap to Nearest Neighbor Classifier When Read more…
Have you ever ask yourself why data matters in healthcare? The data volume is growing at the speed of light in healthcare, and the majority of the time, we cannot touch it due to privacy. Now, let’s see why big data is a unique topic in the healthcare sector.
In this tutorial, we described logistic regression and represented how to implement it in code. Instead of making a decision based on the output probability based on a targeted class, we extended the problem two a two class problem in which for each class we predict the probability.
In this tutorial, we walked through the linear model creation using TensorFlow. The line which was found after training, is not guaranteed to be the best one. Different parameters affect the convergence accuracy. The linear model is found using stochastic optimization and its simplicity makes our world easier.
In this tutorial, we describe how to define and initialize variables in TensorFlow. It is a crucial step as without having parameters, training, updating, saving, restoring and any other operations cannot be performed.