| |October 202119Types of Machine LearningSupervised Learning- Supervised learning is the most popular paradigm for machine learning. It is the easiest to understand and the simplest to implement. It is very similar to teaching a child with the use of flash cards.Unsupervised Learning- Unsupervised learning is very much the opposite of supervised learning. It fea-tures no labels. Instead, our algorithm would be fed a lot of data and given the tools to understand the properties of the data. From there, it can learn to group, cluster, and/or organize the data in a way such that a human (or other intelligent algorithm) can come in and make sense of the newly organized data.Reinforcement Learning- Reinforcement learning is fairly different when compared to supervised and unsu-pervised learning. Where we can easily see the relation-ship between supervised and unsupervised (the presence or absence of labels), the relationship to reinforcement learning is a bit murkier. Some people try to tie rein-forcement learning closer to the two by describing it as a type of learning that relies on a time-dependent sequence of labels, however, my opinion is that that simply makes things more confusing.Today, artificial intelligence is one of the fastest-growing emerging technologies and describes ma-chines that can perform tasks that previously required human intelligence. Machine learning takes it a step further. It's one of the latest artificial intelligence technologies where ma-chines can learn by taking in data, analysing it, taking action, and then learning from the results of that action. · In machine learning, a target is called a label.· In statistics, a target is called a dependent variable.· A variable in statistics is called a feature in machine learning.· A transformation in statistics is called feature creation in machine learning.Why machine learning?To better understand the uses of Machine Learning, con-sider some instances where Machine Learning is applied: the self-driving Google car; cyber fraud detection; and, online recommendation engines from Facebook, Netf-lix, and Amazon. Machines can enable all of these things by filtering useful pieces of information and piecing them together based on patterns to get accurate results.The process flow depicted here represents how Machine Learning works:What's required to create good machine learning systems?· Data preparation capabilities.· Algorithms ­ basic and advanced.· Automation and iterative processes.· Scalability.· Ensemble modelling. Machine learning is one of the latest artificial intelligence technologies where machines can learn by taking in data, analysing it, taking action, and then learning from the results of that action
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