![]() ![]() These algorithms work by exposing multilayered (hence “deep”) neural networks to vast amounts of data. In deep learning, a subset of machine learning, programs discover intricate concepts by building them out of simpler ones. As we feed data to these algorithms, they build their own logic and, as a result, create solutions relevant to aspects of our world as diverse as fraud detection, web searches, tumor classification, and price prediction. Machine learning is the science of designing algorithms that learn on their own from data and adapt without human correction. Now, before we look at how machine learning aids data analysis, let’s explore the fundamentals of each. And the force behind them all is machine-learning algorithms that use data to predict outcomes. What do these events all have in common? It’s artificial intelligence that has guided all these decisions. Over the course of an hour, an unsolicited email skips your inbox and goes straight to spam, a car next to you auto-stops when a pedestrian runs in front of it, and an ad for the product you were thinking about yesterday pops up on your social media feed. ![]()
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