Guide On Machine Learning
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Machine learning is important because it gives enterprises a view of trends in customer behaviour and business operational patterns, as well as supports the development of new products. Many of today’s leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies
Machine learning has seen use cases ranging from predicting customer behaviour to forming the operating system for self-driving cars.
Today, machine learning is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook’s news feed.
Facebook uses machine learning to personalize how each member’s feed is delivered. If a member frequently stops to read a particular group’s posts, the recommendation engine will start to show more of that group’s activity earlier in the feed.
Behind the scenes, the engine is attempting to reinforce known patterns in the member’s online behaviour. Should the member change patterns and fail to read posts from that group in the coming weeks, the news feed will adjust accordingly
When it comes to advantages, machine learning can help enterprises understand their customers at a deeper level. By collecting customer data and correlating it with behaviours over time, machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand.
Some companies use machine learning as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses machine learning to surface the ride advertisements in searches.
But machine learning comes with disadvantages. First and foremost, it can be expensive. Machine learning projects are typically driven by data scientists, who command high salaries. These projects also require software infrastructure that can be expensive.
There is also the problem of machine learning bias. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models it can run into regulatory and reputational harm.
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