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Spatial data is so ever-present that we tend to forget about its significance – from the simplest of maps to weather forecasts to GPS – all of this is based on spatial data. Spatial data science simply takes this to the next level – utilizing data science techniques and methods to use spatial data in […]

Named entity recognition (NER) is vital to natural language processing (NLP)

Despite fears of AI taking over the world, at its core, AI is still reactionary and doesn’t truly learn on its own. It can only make predictions based on the data it’s given. That is why data quality for machine learning and AI should be at the core of every project. Without high-quality training data, […]

You will hear many data scientists use the phrase ‘rubbish in, rubbish out’. In the world of machine learning, it means that an algorithm will not serve its intended purpose if the training data is no good. And professional data acquisition for machine learning is the first step to having good training data. That is […]

Data collection might seem like a simple process on the surface. But because it’s the basic building block of all ML projects, the data needs to be accurate, relevant, and cover all iterations of a problem. Consequently, it is crucial which data collection methods for machine learning are employed to gather it. Without a good […]

The power of machine learning and artificial intelligence goes beyond their flashy features and a futuristic vibe. Even though these technologies aren’t flawless, they have already transformed many industries and are poised to continue doing so in the future. From healthcare to finance to transportation, machine learning, and AI are streamlining processes, improving accuracy, and […]