What is machine learning?

Machine learning is automated extraction of knowledge from Data. It is the way to automate your existing workflows with old mathematics theorem. At the end Machine Leaning is nothing but a programmatic way to solve any kind of problem.

The problem can be of predicting house prices, recognizing people from their photos, checking which interfaces of router will go down, predicting that the router link will be chocked, finding sales number basis on the investment etc.

As per Andrew NG from Coursera “The complexity in traditional computer programming is in the code (programs that people write). In machine learning, learning algorithms are in principle simple and the complexity (structure) is in the data. Is there a way that we can automatically learn that structure? That is what is at the heart of machine learning.

Types of Machine Learning
Supervised learning is also known as predictive modeling, is process of making predictions using data. It can apply what has been learned in the past to new data using labeled examples to predict the future events. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly. You make predictions of new data for which you don’t know the true outcomes. E.g. If dataset is email messages and by using predictive learning we will find out whether the particular email is spam or not.

Unsupervised learning is process of extracting structure from data or learning how to best represent the data. Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

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