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Python has enjoyed a steady rise to fame over recent years and is now jostling for the position of one of the most popular programming languages in the world. Favoured for applications ranging from web development to scripting and process automation, Python is quickly becoming the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.
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The new surge in Artificial Intelligence development has a strong impact on the InfoSec sector. However, there is much more nuance to it than you may think. Machine learning algorithms are not ready to take over on both sides of the fence. They are just a powerful tool that both security specialists and their adversaries use in their arms race.
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How to Estimate a Machine Learning Project

Artificial Intelligence (AI) may add unparalleled value to your product, but it will make it more difficult and unpredictable to build for sure. To meet this challenge you need to partner with an experienced engineering and project management team to define and estimate the process of custom software development with exceptional precision. Here's an evaluation framework used by Netguru.
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Python is a programming language that has been around for quite a while now and has become very popular. However, it seemed like it wasn’t as much in the spotlight as some of the other languages, despite being used by Google. In reality, Python developers weren’t a hot commodity because the language itself wasn’t used widely as some of the others, such as JavaScript, Java, C, or C++.
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Although scientists, engineers, and business mavens agree we might have finally entered the golden age of artificial intelligence when planning a machine learning project you have to be ready to face much more obstacles than you think.
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It’s 2018, and we’ve gained a level of advancement in machine learning technology that no one expected this soon. Images made using algorithms don’t look artificial, retailers’ smart recommendations raise sales, and the stock market has forever been changed by predictions based on machine learning. It’s a revolution happening right before our eyes, and those late to the party might find themselves at a huge disadvantage as their business competitors leverage ML and leave everyone else in the dust.
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If you’ve been following the latest trends in technology, you’ve probably noticed that machine learning (ML) and deep learning (DL) are not just buzzwords anymore (see what is the difference between ML and DL). In fact, they’re responsible for some of the most impressive achievements in artificial intelligence so far, like the spotting of a distant eight-planet solar system or beating real humans at master-level chess. But are you also familiar with the concept of convolutional neural networks (CNNs or ConvNets), and how they contribute to AI development (see how does AI differ from ML)?
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Machine Learning vs. Deep Learning Explained

Machine Learning (ML) and Deep Learning (DL) are two data science terms that have become hot buzzwords in tech, business, and marketing. In fact, ML and DL are two different terms often confused for the same thing. Here’s the difference: Deep Learning is a part of Machine Learning, an advanced technique of teaching algorithms to take independent decisions, which is a part of a broader field of Artificial Intelligence.
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The power and potential of machine learning (ML) to bring huge benefits and new capabilities to mobile and web applications is remarkable. Today, more and more businesses and app developers are opting to build machine learning into their products to the extent that this once avant-garde technology is now becoming decidedly mainstream.
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Machine learning is one of the strongest trends in today's tech scene. A recent survey revealed that 61% of organizations picked machine learning as their most critical data initiative for 2018.
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Both artificial intelligence and machine learning are hot buzzwords right now. It's not surprising at all, since that’s exactly what modern technologies, such as: virtual agents, decision management, and even content creation, are starting to rely on.
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The infrastructure in cities of all sizes is evolving at a neck-breaking pace. New constructions are being built, and old ones are constantly being renovated, which requires heavy equipment such as diggers or cranes. I can imagine it might be a bit frustrating that every single photo from your city break is “embellished” with several cranes hanging above the horizon. The Sagrada Familia in Barcelona is a perfect example – do we really have to wait another ten years or so to get an Instagram-winning shot without any distractions in the background?
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Introducing Machine Learning Practice at Netguru

We’re happy to announce that we’ve kickstarted Machine Learning practice at Netguru. Matthew Opala, who became our ML Tech Lead, has dynamically grown a team of top experts in the industry.
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Over five hundred images, three days, and one team to release the first version of the application that will make our cities look prettier on photographs. Netguru and MicroscopeIT joined forces to run in an AI/ML-focused hackfest. The result? An app that can automatically remove all those pesky cranes from your cityscape pictures with a single click of a button, all thanks to Deep Learning algorithms. If you've ever been to London or Warsaw, you'll know that cranes are everywhere! Even though the app has very limited use (let's be honest, you don't actually get so many cranes in pictures, do you?), it lays a solid base for co-operation on future Machine Learning projects between the two companies.
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For a casual observer, the fintech scene looks like a uniform rock: it is all about digital banking alternatives (such as N26, Revolut, Tandem, Yoyo Wallet or Monzo). As in any industry, the same logic applies here - companies with a B2C business model attract customers the fastest, raise the largest investment rounds and, in the end, grab the most headlines.
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Machine learning applications can bring you more clients, increase sales and reduce business costs. However, if not used properly, they may lead to customer outflow, money loss and reputation damage. When should you use ML in your business and when is it better to stick with traditional computing methods?
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Machine learning models help businesses understand their data better, make more accurate predictions, and accelerate insights across the entire organisation. Amazon, Google, IBM, and Microsoft are the four largest players on the cloud scene, and they have all recently added some machine learning tools to help businesses take advantage of machine learning models for handling their data. But which solution is the right one for your business?
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