How Is Python Different Than Any Other Programming Languages?

Among many of the programming languages available today, Python is one such option that is in high 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 relatively 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 correct 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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[Machine Learning] Transforming the World and Everyday Life

The technical advancements are all about experimenting with machines and making them more human, where the collaborative efforts of both man and machines are deriving results beyond expectations. Next to the invention of the Microchip, Artificial Intelligence(AI) and Machine Learning(ML) are considered to be the biggest technological innovation. From a fanciful science fiction concept, 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 on harnessing 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’ that 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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Forward to the future with augmented reality app and mobile app development by Silver-Solutions.net

Modern technologies can do real magic that people could not think of 20-30 years ago. It was amazing to think about the existence of a mobile phone that you can carry with you, and now we are using it more for entertainment and surfing the web. But progress goes further, and today the reality itself is complemented by virtual objects that we can see and even affect them. We are talking about augmented reality. Augmented Reality (AR), which translates to “augmented reality”, was first coined in 1990 by researcher Tom Codel, who worked for Boeing. The name itself speaks about the essence of the concept of “augmented reality” — technologies that complement reality with virtual elements.

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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, and new applications of AI are being explored. We see how AI is slowly being used in business and everyday life. 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 to 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 valuable 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 making 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 essential 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 severe classification, clustering, and extensive data analysis. For some time, I’ve been using its standard GUI features without thinking much about performance bottlenecks. But since research 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, they utilize a single core of your processor. Such algorithms as Multi-layer Perceptron 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 operation fold. 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 many cores you…

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4 Giant Industries – Where Data Science is Flourishing Well

In a fast-paced world where data is the primary language between processes, people who know how to read these are very much in-demand. These people are called data scientists, and their field is one of the fastest-rising professions in the world today. This is because their specific skillset can be utilized by many fields ranging from retail and business to government organizations like the different commissions or departments. The reason data scientists are in such high demand lies in the very nature of what they do. As mentioned above, data scientists are basically translators between people and computers. With the current state of technology, it is only logical for this field to rise to the top. Data is the product of studies and research, and nowadays, studies are not just conducted by academics but also by business owners and people in other fields. With data-gathering technology continuously growing, more data is now up for the taking. Aside from the sheer number of data, data and their implications also vary, which is why in the following industries, data scientists are really thriving.

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The Rise of the Machines: How Will AI Integrate With Our Work Lives?

Artificial intelligence is one of the biggest buzzwords in science right now. It’s hitting the headlines frequently, due to the massive leaps in progress being made. The story which grabbed the most attention was the DeepMind’s AlphaGo AI defeating a three-time European Go champion in emphatic fashion – and then doing the same for the number 1 ranked player in the world. To put this into context: Go is one of the most difficult games in the world, and is significantly harder to master than chess. It has a vast range of decision trees and possible outcomes, making it extremely difficult to predict, meaning players have to think on their feet and strategise as much as possible. This has people excited because if AI utilizing neural networks can master one of the most complicated games on the planet, its application within the world of work could be massive.

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