Technology News - IoT and Machine Learning

In this article, we are going to review the most common and valuable machine learning algorithms which are frequently applied in the industry, academy and research. Note that some algorithms may have many subcategories or derivations since machine learning and artificial intelligence have been actively and extensively studied and utilised nowadays. This article will present a categorical overview of these fundamental algorithms and briefly explain each of them. In the succeeding articles, each algorithm will be deeply explained and their implementations will be exhibited.  Machine learning (ML) algorithms are generally considered in two main categories as follows; Supervised Learning Methods Unsupervised  Learning Methods Supervised Learning Methods  Supervised learning techniques create logical connections or maps between input and output data. Therefore, these types of methods usually require a significant amount of labelled data for training. After that, th...

Machine learning (ML) techniques have been applied in many applications from academia to industry and have started to influence our daily lives such as in social media applications or online shopping. Hence, many machine learning algorithms have been developed to improve the performance of these ML techniques. While learning machine learning basic or developing new algorithms it is essential to have reliable and large datasets which include logical connections and labels between data member. Especially in academia, having a well-known and extensively examined datasets is necessary in order to investigate the performance of newly developed machine learning algorithms and compare them to existing ones. There are a large amount of publicly available datasets that could be used with various machine learning techniques such as deep learning, classification, reinforcement learning, clustering, etc. I would like to present the datasets that I really like to use: 1. UC Irvine Mac...