Introduction to Machine Learning

Artificial Intelligence, and most notably its sub-field Machine Learning has been rapidly changing the ecosystem of Silicon Valley these days. While the aim of machine learning is to reduce simple jobs by automating them, the subject itself has created a lot more high level jobs. There is an increase in demand for machine learning experts, data scientists and researchers. As a response to this demand, there has been increased interest in the subject of machine learning. Especially among computer science students and new graduates, the prospects of finding internships and jobs in the field of machine learning is substantial. You too are probably reading this because you don't want to miss out on the latest updates in Silicon Valley, or perhaps you wanted to know what all your friends are talking about. In this article you'll learn the basics of machine learning, that is, what you need to know before you get deep into the mechanics of building your own intelligent machines. 
Machine Learning

What is Machine Learning?

Machine Learning is a branch of Artificial Intelligence that deals with teaching machines how to solve problems on their own by learning from data. With machine learning, you would be able to train a machine to perform repetitive tasks for you, without you having to teach it (read: program it) how to do. The machine will observe some data - its environment, some previous information you shared with it, or input from outside, and will compute the best course of action it is expected to perform. Since you haven't told it how to go about performing the action, it will make mistakes, and just like a human being, it will learn from them.

How to Get Started?

You are now probably thinking, so how can I achieve such a feat with my computer? The first recommendation to you would be to learn python. Seriously. There are tons of python libraries for almost everything, just getting started with the most popular ones such as scikit-learn, numpy, scipy, tensorflow will give you a jump start in machine learning. If you want a well-structured course on machine learning, give a look to one of the most popular online courses on Coursera taught by Andrew Ng:

Types of Machine Learning

In machine learning, the machine that you create will automatically observe data and perform suitable actions. There are 3 major classifications of machine learning, depending on the data they receive, and how they arrive at the correct set of actions to perform.
  • Supervised Learning: In supervised learning, first you give the machine labelled data, where the label corresponds to the correct answer/action. During the first round, the machine matches its own output with the correct labels to see how it performed, and then learns from its mistakes and correct labeling. It then creates a general model on how to solve such type of problems, and uses this model to solve the problem on new data.
  • Unsupervised Learning: In unsupervised learning, there is no labelled data, as we ourselves might not know the correct answers. Unsupervised learning has its major application in classification problems. The machine is presented with raw data, and the machine reads the data, finds the similarities between different data points and then classifies or groups similar data points together.
  • Reinforcement Learning: Reinforcement learning is based on providing the machine feedback on its performance. A correct action will bring rewards while an incorrect action will attract punishment. This helps the machine to learn the best sequence of actions to perform a given task.

Types of Machine Learning
Types of Machine Learning

In this article we discussed the definition of machine learning, how to get started and types of machine learning. This concludes our discussion for today. Next we'll discuss about the difference between deep learning and machine learning, as well as other popular terminologies, and study different types of machine learning individually with practical examples. Follow us on twitter and subscribe to our RSS for updates on future articles and tutorials. Thank you for reading! 
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