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.
In 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.
Machine 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.
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.
The K-nearest neighbors (KNNs) classifier or simply Nearest Neighbor Classifier is a kind of supervised machine learning algorithms which operates based on spatial distance measurements. In this post, we investigate the theory behind it.
The purpose of this post is to introduce a shortcut to developers and researcher for finding useful resources about Deep Learning for Natural Language Processing. Please refer to the associated GitHub project and documentation for further details.