Data science has become a part of our lives directly and indirectly. Its wide range of applications in our daily lives includes intelligent devices like Amazon’s Alexa, Siri, meteorological forecasting, customized advertisements, chatbots, recommendations on different websites, and so much more. It is really fascinating how we all have shifted to this era of data. It has revolutionized every industry and organization in some way. And, it continues to do so by adding new functionalities and providing better outputs and customer satisfaction. The multi-dimensional approach to data science has made life easier on so many levels. Nonetheless, it is not fully explored and offers a great scope of research and development. No doubt there is a high rise in job opportunities in his domain. Terms like Machine Learning, Artificial Intelligence, Neural Networks, and Deep Learning are often used interchangeably and lead to confusion among common masses. Here, we address two of these technologies, deep learning, and neural networks. Let us consider them individually.
Almost everyone would agree that the time has come to properly differentiate between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). This is owing to the fact that a lot of people do not know the difference between these terms. Are you also caught up in the confusion? An artificial intelligence survey conducted by a business finance and accounting company known as Sage revealed that 43% of participants in the US and 47% of participants in the UK disclosed that they did not know what Artificial Intelligence was all about. As a result, leaders in the tech industry have realized the pressing need for them to put AI and its subcategories (machine learning, deep learning) into proper business vocabulary that they would be easily understood by everyone. Fortunately, in this post, we will be looking to differentiate between AI, ML and Deep Learning. These three terms are often used interchangeably; however, they mean different things. Let’s go deeper into the nitty-gritty of these terms.
During the past few years, the terms artificial insight and machine learning have started showing up now and again in innovation news and sites. Often the two are utilized as equivalent words, yet numerous specialists contend that they have inconspicuous yet real differences. Furthermore, of course, the specialists in some cases differ among themselves about what those differences are. When all is said in done, however, two things appear to be clear: first, the term artificial insight (AI) is more established than the term Machine learning with python and second, the vast majority consider machine learning to be a subset of artificial knowledge.
The online casino business is a lucrative venture injecting significant revenue to various countries. With the improvement of technology and the birth of artificial intelligence, the gambling industry will experience tremendous growth. Many online casinos are already leveraging machine learning in their database management. While consumers in other sectors are predictable, online casino users are unpredictable. Therefore, gaming companies need to collect a massive amount of data and analyze it to understand the consumers. Machine learning is affecting the online casino business in the following ways. Data Collection and Analysis The gambling industry is famous for analyzing consumer behaviors and using the information to create personalized services before the introduction of artificial intelligence, online casinos utilized loyalty programs, and club cards. Nowadays, gaming companies can collect tons of data from their customers. The data comes from how gamblers interact with online platforms and their preferences. They aim to understand the reason behind consumer behavior, for instance, why they choose specific games, when they switch games or stop playing. Analyzing the data requires complex systems. Machine learning simplifies the process by collecting and analyzing the data and presenting it in a way that decision-makers can interpret. Top-rated casinos are leveraging these…
Each human or organization doing various activities are constantly generating massive amounts of data. In real life, when you visit supermarkets, doctors, institutions, log into a bank account, visit webpages, spend time in social networks, buy online – you leave a footprint of data. The data of your past carries lots of interesting information about your human habits, interests, behavior patterns. If we scale up to organizations, where every process and decision play a significant role in business success, data becomes a valuable asset. Collected and properly mined historical data may help make critical decisions for the future, optimize the structure, and even see the business trends. Hadoop machine learning tools Big Data is everywhere and, so storing analyzing it becomes a challenge. No human can handle and effectively analyze vast amounts of data. This is where machine learning and distributed storage comes in handy. Hadoop machine learning is an excellent concept for dealing with large amounts of data. The Apache-based Hadoop platform is based on open source tools and utilities that use a network of lots of computers to store and process large amounts of data more efficiently. Hadoop machine learning has joined the concept of different tools. Hadoop…
Machine learning (ML) algorithms are becoming a standard way of improving decisions, whether this is a finance, health or gaming sector. Sports betting is one of those areas where ML technologies are becoming a standard decision support tools. The betting market is a massive industry, and simple means like statistics do not work anymore. Betting operators are focusing on machine learning-based methods to increase the expected return on investment. It is necessary to make accurate in-house predictions of sports events because all business mode success depends on this. It is essential for a betting operator to increase its accuracy in sports prediction to be an attractive company and be profitable. Professional betters, also are using machine learning algorithms to formulate strategies for accurate prediction. Using an ML is a two-way game, which depends on how well big data processing algorithms work on each side. To make a good prediction, this is necessary to consider all possible data. This include historical data of sports team performance, data of each player and team, geographical, seasonal, and other related data can be relevant in making the final prediction. Machine learning algorithms don’t use simple logic and rules. Instead – they are learning from…
Experimenting with machines and making them more human is what the technical advancements all about, where the collaborative efforts of both man and machines are deriving results beyond expectations. Next, to the invention of Microchip, Artificial Intelligence(AI) and Machine Learning(ML) are considered to be the biggest innovation of technology. From a fanciful concept of science fiction, AI is now the reality of this digital world. With AI came ML and imitating the real neurons, Deep Learning brings the study of neural networks, giving machine learning a great breakthrough. It’s the era of digital revolutions where the focus is merely to harness mental and cognitive abilities. And the day is not far when automated devices and programs will not only replace ‘manual labor’ but also the ‘mental labor’ which only a human performs today. People looking at such advancements raise their brows in surprise and imagine how these technologies work and run. I would say that technology lovers seeking to gain deeper insights about the latest advancements must undergo a Machine Learning Course which will drive you away with the digital benefits they provide to the world.
Some sixty years ago, artificial intelligence was just a concept that research scientists had in mind. But ever since the idea of super-computers-capable-of-thinking-like-humans has been floated, it has occupied a particular part in the public consciousness. Over recent years, we have seen tremendous growth and rapid evolution of artificial intelligence. Today, there is a vast amount of high-quality open source libraries and software tools available to AI and ML experts. Every day, new ideas and concepts on AI are being discovered, as well as new applications of AI are being explored. We see how AI is slowly being used in business and our everyday lives. According to Ottawa IT services experts from Firewall Technical, AI technology will continue to be a significant force in many IT solutions in the next few years. Many tech experts agree that AI has a very bright future ahead and some even predict the drastic changes AI can bring into the future generations. Considering all these great news now is the best time to become an AI master. But for you to become an AI expert, you’ll need to learn some useful tools in building AI algorithms.
Classifying medical images is a tedious and complex task. Using machine learning algorithms to assist the process could be a huge help. There are many challenges to make machine learning algorithms work reliably on image data. First of all, you need a rather large image database with ground truth information (expert’s labeled data with diagnosis information). The second problem is preprocessing images, including merging modalities, unifying color maps, normalizing and filtering. This part is important and may impact the last part – feature extraction. This step is crucial because on how well you can extract informative features, depends on how well machine learning algorithms will work. Dataset To demonstrate the classification procedure of medical images the ophthalmology STARE (STructured Analysis of the Retina) image database was pulled from https://cecas.clemson.edu/~ahoover/stare/. The database consists of 400 images with 13 diagnostic cases along with preprocessed images. For classification problem we have chosen only vessels images and only two classes: Normal and Choroidal Neovascularization (CNV). So the number of images was reduced to 99 where 25 were used as test data and 74 as training.