Guest Written By Rebecca Njeri Last Thursday, I attended the machinery.ai After about 1.5 years of learning and practising data science, this conference reminded me of the things that intrigued me when I first started learning data science, and I thought that I should write a post explaining the three different groups of machine learning algorithms. Machine Learning can be defined as the science of getting computers to act without being explicitly programmed. It can be further divided into three broad categories: supervised learning, unsupervised learning, and reinforcement learning. A machine learning model should be chosen depending on the nature of the data available as will be illustrated below.
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Data science and machine learning are having profound impacts on business, and are rapidly becoming critical for differentiation and sometimes survival. Being able to quickly categorize the potential impacts into one of five categories, and communicate their potential, will help data and analytics leaders drive better results.