All Case Studies Design Development Interviews Machine Learning Project Management
More effective operation management may turn out to be a key driver of  competitive advantage for retailers. A simple CCTV camera combined with an intelligent video analytics solution can contribute to achieving that goal. How?Intelligent video analytics is a new opening for streamlining retail operations. This AI-driven solution processes video footage to detect and classify various objects (like people, retail products, or vehicles) and simplify extracting business value from customer and object tracking data. 
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Data science in marketing can be used for channel optimization, customer segmentation, lead targeting and advanced lead scoring, real-time interactions, among others, by providing a valuable insight into customer's preferences and behaviours. 
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To understand the relative benefits of data science vs business intelligence, we can break them down into their key features and analyze where they fit into the data analytics timeline.
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Machine Learning Tools Comparison

Machine Learning has blossomed over the last few years and is not going to slow down any time soon. According to Market and Markets, it is expected to grow from $1 Billion in 2016 to $9 billion by 2022. The adoption of AI in business is accelerating every month, and so should the work of Machine Learning Engineers. Moreover, the ever-changing nature of Machine Learning forces Machine Learning Engineers to optimize their work by outsourcing tedious and time-consuming tasks and focusing on the juicy stuff. 
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In the past decade, data science and machine learning have become indispensable tools to many types of businesses, from tiny startups to established multinational enterprises. Data processing, task automation, product recommendations, and financial analysis are just some of the areas that they’ve revolutionized.
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Data science and machine learning have different objectives and functionalities. While data science is an approach to extract insights from data, machine learning is a set of techniques that allows computer technology to learn from data. However, data science and machine learning have something in common: They use maths, statistics, and algorithms to extract the value from data. Machine learning and data science are two tech buzzwords that have everyone's attention today. But when does each of these approaches bring best results? Which projects stand to benefit most from machine learning (ML) and which ones from data science techniques? Read on to find out what the key differences and similarities between ML and data science are and why it's a good idea to pick ML for your project.
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Are you looking to bring data science experts on board for your project? Expect to face a bit of a challenge. Data scientists who specialize in machine learning are in high demand today so finding and hiring talents will be a tough nut to crack.
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In recent years, the insurance industry has finally started to embrace digital technology, as insurtech startups have forced the traditional players to adapt if they want to survive. Wefox, a digital insurance marketplace, is at the forefront of the changes. Having successfully moved what has historically been a face-to-face business online, the company is looking into the future, a world where the Internet of Things, data science and artificial intelligence will play major roles in our lives, including how we insure what is valuable to us.
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As Barclays becomes the latest bank to launch a robo-advisor offering, we look at how these AI-powered advice platforms are changing the field of wealth management and ask, “could robo-advisors replace human representatives?” 
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AI is evolving at an incredible rate. Consumers are starting to encounter AI-powered products on a regular basis as we go about our increasingly digital lives, and yet the industry is still in its infancy, with lots of room to innovate. Hour One is one of the companies proving that AI can go further than we ever dreamed of. The New York-based start-up uses AI to generate digital characters based on real people which businesses can hire for educational, professional, and commercial use.
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Machine learning is one of the hottest topics in modern software development. Still, it is a fairly young discipline, which is undergoing rapid change and development. We wrote this article to aid you in selecting the right tool of ML for your project, showcasing the top frameworks along with their upsides and shortcomings.
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At first glance, real estate may seem like one of the industries least affected by the internet and new technology. After all, constructing and maintaining buildings had been done perfectly well long before the advent of the Internet. However, underneath the bricks and concrete, buildings feature highly complex systems that need to be constantly monitored and maintained. Traditionally, this has been a manual, costly, and inefficient process, in which problems often undiscovered and repairs not made until it’s too late, lead to incidents such as floods and explosions.
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So you’re ready to dive into the exciting world of AI. However, in such a rapidly-evolving field it may be difficult to decide how to go about it. What’s the best way to create a robust and effective AI product? Should you develop a custom solution or buy one off the shelf? We gathered practical pros and cons for both options to help you decide.
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Machine Learning is a trending field of Computer Science turning computer’s computations into usable data and giving a number of unique opportunities. It’s rapidly gaining in popularity – it’s common in modern web applications as well as in services such as Netflix, Spotify, Amazon and Facebook. Machine Learning is a good solution for apps based on recommendations or some kind of predictions. If you want to build this kind of app you will need an efficient backend technology to support it – is Ruby on Rails the right choice?
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The use of machine learning in fintech is growing. You’ve likely noticed an increasing number of companies using it to provide personalized services, transform customer experience, or improve fraud detection.
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Dynamic price optimization is becoming increasingly popular among retailers. Machine learning has exponentially increased their ability to process data and apply this knowledge to real-time price adjustments. But the necessary software is still relatively expensive. So how can it really help businesses? And, most importantly, what’s the return on investment at the end of the day?
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Adobe developed a tool that automates the process of selecting headlines and images. Its AI gathers data and learns how to personalize content for different audiences. Will it improve how content is targeted?
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This year, an international diploma program used by thousands of high schools canceled exams that students rely on for college admissions, replacing them with a grade determined by an algorithm. The result was lower-than-expected grades for many students, who are now left to deal with the consequences. What went wrong and what can be done to prevent it from happening again?
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A bot is a computer program that automates a task. Chatbots are, therefore, programs that automate conversation, both voice and text. In essence, a chatbot can simulate an intelligent conversation with a human. They generally make use of machine learning to gather conversational rhythm, beat and flow, and thus mimic human conversation.
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The buzz surrounding GPT-3, the most powerful language model ever built, has been pretty spectacular. Some experts warn us of the dangers AI technology can introduce, such as more lifelike generated content slipping into the already chaotic online news and social media spaces. But what is GPT-3? Is it really dangerous and does it deserve the hype that’s generated around it? Let’s find out.
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