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Though often confused with AI, machine learning (ML) is where we currently stand in our quest to achieve actual (or sentient) artificial intelligence. After all, while at the moment, we are not yet able to hold full-blown conversations with our devices, today’s machine learning (ML) algorithms have ushered in a brand new era in automation.
Machine Learning (ML) is an important technology that allows computers to learn from data and make predictions or decisions without being explicitly programmed. ML is becoming increasingly popular as a tool for businesses and organizations of all types to gain insights, automate processes, and improve performance. Machine learning software development is also an emerging field with many opportunities.
The most popular types of ML at the moment include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is used to predict outcomes based on labeled data, unsupervised learning is used to identify patterns or structure in unlabeled data, and reinforcement learning is used to train models to make decisions in complex, dynamic environments.
As the name implies, with the help of some very clever algorithms, machine learning allows us to feed data into our machines (known as “training data”) to automatically perform predictions without any explicit programming — as if they’d learnt to do it all by themselves. In this way, we can develop software solutions to scan images for objects and faces, respond to specific voice commands, and recognise other useful patterns.
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