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How Machine Learning Has Affected Online Casino Business

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…

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How Is Python Different Than Any Other Programming Languages?

Among many of the programming languages available today, Python is one such option that is high in demand by now. The increasing demand for the language is because it is entirely different than many other programming languages available in the market. One such difference or rather benefit is that Python is quite easy to understand and quite simple to learn. Do you want to learn Python from scratch? Then Intellipaat ‘Python Course’ is for you. Typically, selecting a proper programming language depends on many factors, such as training, availability, cost, emotional attachment, and prior investments. But these factors are also variable, and hence there are other factors on which the selection of the right programming language highly depends. Some of the programming languages available in the market now are Java, PHP, C++, Perl, Ruby, Javascript, TCL, Smalltalk, and many others. Python is very much different than these languages in many ways.

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Scale up Big Data mining with Hadoop machine learning tools

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…

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Modern betting depends on machine learning tools

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…

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[Machine Learning] Transforming the World and Everyday Life

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.

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Tools You Should Learn to Become an AI (Artificial Intelligence) and ML (Machine Learning) Master

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.

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Feature extraction from retina vascular images for classification

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.

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Running remote host Weka experiments

Previously, we tried to run a weka server to utilize all cores of the processor in classification tasks. But it appears that the weka server works only in explorer for classification routines. For more advanced machine learning, there is a more flexible tool – experimenter. Weka server doesn’t support this area. So what to do if you want more performance or utilize the multi-core processor of the local machine. There is a way out, but it is quite tricky. Weka has the ability to perform remote experiments that allow spreading the load across multiple host machines that have Weka set up. You can read the documentation of remote experiments here, but it may be somewhat confusing. It took time for me to figure out some parts by trial and error. The trickiest part is to set everything up and prepare the necessary command to be run before performing a remote experiment. So let’s get to it.

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Utilizing multi-core processor for classification in WEKA

Currently, WEKA is one of the most favorites machine learning tools. Without programming skills, you can do serious classification, clustering, and big data analysis. For some time I’ve been using its standard GUI features without thinking much about performance bottlenecks. But since researches are becoming more complex by using ensemble, voting and other meta-algorithms that generally are based on multiple classifiers running simultaneously, the performance issues start becoming annoying. You need to wait for hours until the task is completed. The problem is that when running classification algorithms from the WEKA GUI, the utilize a single core of your processor. Such algorithms as Multi-layer Percepron running 10 fold cross-validation is calculating one cross fold at the time on one core taking a long time to accomplish: So I started looking for options to make it use all cores of the processor as separate threads for each fold of operation. There are a couple of options available to do so. One is to use WekaServer package, and another is remote host processing. This time we will focus on WekaServer solution. The idea is to start a WEKA server as a distributed execution environment. When starting the server, you can indicate how…

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Implementing logistic regression learner with python

Logistic regression is the next step from linear regression. The most real-life data have a non-linear relationship, thus applying linear models might be ineffective. Logistic regression is capable of handling non-linear effects in prediction tasks. You can think of lots of different scenarios where logistic regression could be applied. There can be financial, demographic, health, weather and other data where the model could be implemented and used to predict next events on future data. For instance, you can classify emails into spam and non-spam, transactions being fraud or not, tumors being malignant or benign. In order to understand logistic regression, let’s cover some basics, do a simple classification on data set with two features and then test it on real-life data with multiple features.

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