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Machine learning is a subset of artificial intelligence that enables systems to learn and predict outcomes without explicit programming. It is often used interchangeably with the term AI because it is the AI technique that has made the greatest impact in the real world to date, and it’s what you’re most likely to use in your business. Chatbots, product recommendations, spam filters, self-driving cars and a huge range of other systems leverage machine learning, as do “intelligent agents” like Siri and Cortana.
In this Fortune article, What is the Difference Between Artificial Intelligence and Machine Learning, the author clearly describes the difference between AI and Machine Learning: “AI is the broader concept of machines being able to carry out tasks in a way that we would consider smart,” while machine learning is “a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.”
Instead of writing algorithms and rules that make decisions directly or trying to program a computer to “be intelligent” using sets of rules, exceptions and filters, machine learning teaches computer systems to make decisions by learning from large data sets. Rule-based systems quickly become fragile when they have to account for the complexity of the real world; machine learning can create models that represent and generalize patterns in the data you use to train it, and it can use those models to interpret and analyze new information.
Machine learning is suitable for classification, which includes the ability to recognize text and objects in images and video, as well as finding associations in data or segmenting data into clusters (e.g., finding groups of customers). Machine learning is also adept at prediction, such as calculating the likelihood of events or forecasting outcomes. Machine learning can also be used to generate missing data; for example, the latest version of CorelDRAW uses machine learning to interpolate the smooth stroke you’re trying to draw from multiple rough strokes you make with the pen tool.
When software is used to tell the algorithm specifically what we want to discover, it is called supervised ML. The machine learning algorithms use a ‘target’ variable or attribute to ‘train’ a model based on the data in the target variable.
Unsupervised ML is when the machine can learn to identify complex processes and patterns without a human to provide guidance along the way. It uses clustering as an application, where the dataset records are automatically segmented into groups. Those groups are similar to records in their own group and dissimilar to records in other groups.
Almost every business industry can benefit from machine learning, including applications such as forecasting, fraud detection and human resources.
If you are considering a career in machine learning, here are some of the skills that will help you excel in this growing field.
Below are skills that will help you become a professional in machine learning: