Machine Learning: The use and development of computer systems that can learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Artificial Intelligence: Artificial intelligence (AI) is the computer’s ability or a robot controlled by a computer to do tasks that humans usually do because they require human intelligence and discernment. Machine Learning is a precursor which helps create use cases for Artificial Intelligence such as Intrusion Detection, Image recognition, or categorizing data. The combination of both ML and AI has a great potential to shape the future for all technological innovation.
Would you believe it if I tell you that you are a part of one of the sources generating big data? A layperson may instantly disagree, but it is, in fact, valid for most people using smartphones. Whether you are using social media, sending money to someone online, or using a variety of mobile applications, all of your web activity leads to data generation. This data is precious to companies who want to understand their customers better and provide personalized services. For example, eCommerce giant Amazon uses your shopping history and past product search data to recommend you related products you are most likely to buy. Research shows that we generate around 2.5 quintillion bytes of data each day, which is quite huge! Well, you may wonder how can such a massive amount of data be handled. This is where Hadoop comes into the picture. Apache Hadoop is one of the earliest open-source tools to offer storage and large-scale processing of big data. As mentioned in Apache.org, Hadoop is a framework that allows for distributed processing of large data sets across clusters of computers using simple programming models. Various companies and organizations use Hadoop for research as well as production.
Do you have a data science interview coming up and want to know how to prepare for it? If so, you have already made some progress. Getting an interview means you have gone through the initial stages of the hiring process. This includes applying for a job and submitting your resume. You may also have taken a technical and aptitude test. Given that you have been selected for the interview, you would want to be well prepared to impress your potential employer. Given below are a few tips that will help you crush your data science interview and land the job you aspire.
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…