Technology News - IoT and Machine Learning

15 Best IoT Energy Startups Energy is one of the common problems all around the world. Without it, all our technology will be useless. The interesting thing about energy is despite we have numerous sustainable and renewable energy sources; we still rely on scarce sources. Renewable energy is important because it provides a clean, sustainable and unlimited source of energy that can reduce our dependence on finite and polluting fossil fuels, helping to mitigate the impacts of climate change. Additionally, renewable energy can lead to local economic development, improve energy security and reliability, and create new jobs and industries. Wind power is one of the most important renewable energy sources.  On the other hand, the Internet of Things will change the trends in the energy industry as well. In fact, the number of IoT energy startups is worth mentioning. If you are interested in this field and love the idea of taking advantage of new technologies, then you need to know the foll...

Programming languages are constantly evolving, but as of 2022, some of the most widely used programming languages include Python, Java, C++, C#, and JavaScript. These languages are commonly used in various fields such as artificial intelligence, machine learning, web development, data science, and mobile app development. Python Python is a general-purpose programming language that is widely used in fields such as machine learning, artificial intelligence, web development, data science, and mobile app development. One of the main advantages of Python is its easy-to-understand syntax, which makes it a great language for beginners to learn. Additionally, Python has many popular libraries and tools, making it an important language for professional programmers. Java Java is an object-oriented programming language that is commonly used in fields such as mobile app development, web development, and desktop application development. Java has the advantage of being platform-independent, meaning ...

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...

I think the first question which must be answered clearly while starting teaching artificial intelligence and machine learning should be about the difference between them. AI - Artificial Intelligence is a comprehensive concept that stating that the computers can learn, think and decide what they should do by themselves in every situation. However, fully AI concept is not possible at the moment as various operations such as image recognition, playing a game, creative thinking etc. require different algorithms which are striving to solve specific problems and tasks. ML - Machine Learning is the specific application of AI, which is mostly relying on learning based on historical data to analyze future data and decide using these analyzes. It can be categorized into supervised and unsupervised learning. Former one utilizes the labelled data to train the machine learning core (brain) and the latter one uses an agent in order to solve the problems. Machine l...