Machine Learning

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ML can be described as software that changes when it learns from new information. As the software is self-adaptive, it is not necessary to add new rules manually. One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data.

ML is an application of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs (slides5-15) that can access data and use it and learn for themselves. The primary aim is to allow the computers learn automatically without human intervention and adjust actions accordingly. Machine learning can be performed using multiple approaches. The three basic models of machine learning are supervised, unsupervised and reinforcement learning. 

In ML, statistical methods are used to empower machines to learn without being programmed explicitly. The field focuses on letting algorithms learn from the provided data, collect insights, and make predictions on unanalyzed data based on the gathered information.

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.