Introduction to Machine Learning. Blog-1 Introduction to Machine Learning
Tags: Artificial Intelligence, Computer Science, Supervised Learning, Machine Learning, Unsupervised Learning

Blog-1 of Machine Learning Series.

Introduction to Machine Learning

Introduction

In this series of blogs, we will be learning about Machine learning and its applications. Don’t worry if you are not good at mathematics or programming, We will learn things as we go.

What is Machine Learning in Layman’s term?

Imagine you are trying to toss a paper into the dustbin. Assume the wind force to be zero at all times. In the first attempt you put too little force and the paper does not reach the bin. In the second attempt, you put too much force, you still don’t make it to the bin. With each attempt you make you get closer to the target. You experiment by changing the throw angle, adjusting the force. In this case, you are learning from your experience.

We can do something similar with machines too. We can program a machine to learn from every attempt/experience/data-point and then improve the outcome. This is what we call machine learning.

Here’s the definition of machine learning according to Arthur Samuel. He defined machine learning as the field of study that gives computers the ability to learn without being explicitly learned.

You come across Machine Learning applications in your day to day life without even knowing about it.

Remember when you received an email from a Nigerian prince who wanted to send you money and Gmail sent it to the spam folder.

That’s an example of Machine Learning.

Classifying spam and not spam, Image Recognition, Recommender Systems, Self Driving Cars are all examples of machine learning.

Types of Machine Learning:-

Machine Learning can be classified into three types.

Supervised Learning Unsupervised Learning Reinforcement Learning

Supervised Learning

Imagine you want to teach a child what an apple and a cupcake is. You show him images of apple and tell him that it is an apple. Then you show him images of a cupcake and tell him that it is a cupcake.

Now there are certain things you tell him, like the difference between color, texture, and shape so that he understands the differences between the two.

Now you again show him the pictures of apple and cupcake and ask him to identify them. If he is able to differentiate between the apples and cupcakes, congratulations, you have successfully taught him. (And you get to see a real-life example of Supervised Learning)

As we can understand from the above example, in supervised learning, we are given a data set and already know what our correct output should look like.

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.

Unsupervised Learning

In Unsupervised Learning, we’re given data that doesn’t have any labels. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.

Unlike supervised learning, there is no teacher to teach.

Suppose the machine is given images of cats and dogs and there are no labels. The machine has no idea about the features of dogs and cats but it can categorize them according to their similarities, patterns, and differences.

In unsupervised learning, the machine learns on its own without labels.

Reinforcement Learning

Imagine you are trying to teach your dog to fetch a ball when you throw it. Now the dog does not understand your language so when you throw the ball the dog can react in a number of ways. But when it does what you wanted him to do you reward him with a snack. Now the next time you throw a ball the dog will fetch it knowing that this kind of behaviour earns him good points.

In this scenario, the Dog is a learning Agent. The snack is a reward.

Assume an agent, which might be a self-driving car or a program playing chess, interacts with its environment, receives a reward state depending on how it performs, such as driving to the destination safely or winning a game. Conversely, the agent receives a penalty for performing incorrectly, such as going off the road or being checkmated.

The system of giving rewards for correct behaviour and penalty for wrong behaviour is the basis of reinforcement learning.

To look further into interesting implementations of reinforcement learning Watch the following video.

For further information about this topic, you can check the sources listed below.

http://www.adevole.com/machine-learning-laymans-terms%E2%80%8A-%E2%80%8Aa-guide-non-tech-co-founders/

https://www.edureka.co/blog/supervised-learning

https://medium.com/@canburaktumer/machine-learning-basics-with-examples-part-2-supervised-learning-e2b740ff014c

https://www.geeksforgeeks.org/supervised-unsupervised-learning/

https://www.techopedia.com/definition/32055/reinforcement-learning-rl

Stuart J. Russell, Peter Norvig (2010) Artificial Intelligence: A Modern Approach, Third Edition, Prentice-Hall ISBN 9780136042594

In this blog, we have presented you with the basic concepts of Machine learning. We hope this blog was helpful and would have motivated you enough to get interested in the topic. We will meet you next time as we together continue to explore the vast world of machine learning and it’s related topics. Till then keep exploring!.