In my previous post on recommendation engines, I fleetingly mentioned about machine learning. Talking about machine learning, what comes to my mind is a recent conversation I had with my uncle. He was asking me on what I was working on and I started mentioning about machine learning and data science . He listened very attentively and later on told my mother that he had absolutely no clue what I was talking about. So I thought it would be a good idea to try and unravel the science behind machine learning.

Let me start with an analogy. When we were all toddlers whenever we saw something new ,say a dog, our parents would point and tell us *“Look , a dog”*. This is how we start to learn about things around us,from inputs such as these that we receive from our parents and elders . The science behind machine learning works pretty similar. In this context, the toddler is the machine and the elder which teaches the machine is a bunch of data .

In very simple terms the setup for a machine learning context works like this. The machine is fed with a set of data. This data consists of two parts, one part is called features and the other labels. Let me elaborate a little bit more. Suppose we are training the machine to identify the image of a dog. As a first step we feed multiple images of dogs to the machine. Each image which is fed, say a jpeg or png image, consists of millions of pixels. Each pixel in turn is composed of some value of the three primary colors Red, Blue and Green. The values of these primary colors ranges between 0 to 256. This is called the pixel intensity. For example the pixel intensity for the color orange would be (255,102,0), where 255 is the intensity of its red component, 102 its green component and 0 its blue component. Like wise, every pixel in an image will have various combinations of these primary colors.

These pixel intensities are the features of the image which are provided as inputs to the machine. Against each of these features, we also provide a class or category describing the features we provided. This is the label. This data set is our basic input. To visualize the data set, think of it as a huge table of pixel values and its labels. If we have,say 10 pixels per image and there are 10 images. Our table will have 10 rows, corresponding to each image and for each row there would be 11 columns. The first 10 columns would correspond to pixel values and the 11th column would be the label.

Now that we have provided the machine its data, let us look at how it learns. For this let me take you back to your school days. In your basic geometry, you would have learnt the equation of a line as *Y = C + (theta * X).* In this equation, the variable * C *is called the intercept and

*the slope of the line. These two variables govern the properties of the line*

**theta***. The relevance of these variables is that, if we are given any other value of*

**Y***, then by our knowledge of*

**X***and t*

**C***we will be able to predict or create a line. So by learning two parameters we are in effect predicting an outcome. This is the essence of machine learning. In a machine learning, setup the machine is made to learn the parameters from the features which is provided.Equipped with the knowledge of these parameters the machine will be able to predict the most probable values of Y(Outcomes) when new values of X(features) are provided.*

**heta**In our dog identification example, the X values are the pixel intensities of the images we provided, Y denotes labels of the dogs. The parameters are learned from the provided data. If we are to give the machine new values of X’s which contain say features of both dogs and cats, the machine will correctly identify which is a dog and which is a cat, with its knowledge of the parameters. The first set of data which we provide to the machine for it to learn parameters is called the training set and the new data which we provide for prediction is called the test set. The above mentioned genre of machine learning is called Supervised Learning. Needless to say, the earlier equation of the line is one among multiple types of algorithm used in machine learning. This type of algorithm for the line is called linear regression. There are multiple algorithms like these which enables machines to learn parameters and carry out predictions.

What I have described herewith is a very simple version of machine learning. Advances are being made in this field and scientists are trying to mimic the learning mechanism of human brain on to machines. An important and growing field aligned to this idea is called Deep Learning. I will delve on deep learning in a future post.

The power of machine learning is quite prevalent in the world around us and quite often the learning is inconspicuous. As a matter of fact, we are all party to the training process inconspicuously. A very popular example is the photo tagging process in Facebook. When we tag pictures which we post on Facebook, we are in fact providing labels enabling a machine to learn. Facebook’s powerful machines will extract features from the photos we tag. Next time we tag a new photo, Facebook will automatically predict the correct tag through the parameters which it has learned. So next time you tag a picture on Facebook, realize that you are also playing your part in teaching a machine to learn.