Introduction to Machine Learning

Arthur Samuel, who is remembered as pioneer in Artificial Intelligence and computer gaming, defined Machine Learning as a “Field of study that gives computers the capability to learn (through data), without the need to be explicitly programmed". In other words, Machine Learning is an application of Artificial Intelligence, which will enable the algorithm to learn with the data, and build from experience and constantly improving itself without being explicitly programmed. For example, how would you recognize an apple and a mango. You can identify it with the properties such as color, shape, size etc., One approach of identifying it is by hardcoding and making Rules, and applying those Rules across board. But the problem is that you cannot have a unified Rule applicable to all cases. Another approach is to use use Machine Leaning, where we do not have to apply any Rules, and it is robust and practical. With machine learning, we are giving the machine the ability to learn, with minimum human intervention, and without it being explictly programmed. Therefore, we can say that Data is the fuel and Machine Learning Algorithm is the refinery, where one cannot co-exist without the other.

Why Python in Machine Learning ?
1. Pre-built supporting libraries
2. Simplicity
3. Platform Independent
4. Good Visualizatiom Options
5. Good Community Support
6. Provide trend and insight
7. Decision Making

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