Machine Learning explained!
At its heart, machine learning is the task of making computers more intelligent without explicitly teaching them how to behave. Well this sounds more like a technical explanation, correct? In simple terms, Machines imitating and adapting human like behavior is Machine Learning. We are trying to teach machines to “Learn from Experience”.
In machine learning you create a model which learns from the previous data. Before getting introduced to the various terms used in Machine Learning let us understand what is a Model. A machine learning model can be a mathematical representation of a real-world process. To generate a machine learning model you will need to provide data which can train a machine learning algorithm(steps involved to execute a task) to learn from. Machine learning is aptly named, because once you choose the model to use and tune it (a.k.a. improve it through adjustments), the machine will use the model to learn the patterns in your data. Then, you can input new conditions (observations) and it will predict the outcome!
Now to start with, Machine Learning comes with different flavors. Lets taste each one of them one by one!
Supervised Learning
As an example, we can think of a child learning shapes and colors. At first, we will tell this child that a circle is “a circle”, and that the red color is called “red”. Another example is that of your granny whom you can teach that a particular tablet is for controlling her blood pressure or maybe her blood sugar level. You will give the features of the tablet like its color or it’s name. And your granny will remember it and will be able to take the correct tablet. We label the information and give it to the child. Later on, after accumulating this knowledge, the child will be able to call a circle “a circle” and red “red”. Your granny will learn from the information given by you and then apply the knowledge learnt to take the correct tablet. The equivalence of this method in Machine Learning is Supervised Learning. All Supervised Learning techniques area form of either Classification or Regression. Now it’s okay if you’ll don’t know the exact meaning of the terms but the same will be explained to you now.
Classification: Classification is used for predicting distinct values. For eg. Whether India will WIN or LOSE a Cricket match? Or whether an received email is SPAM or Genuine?
Regression:Regression is used for predicting continuous responses. For eg: Trend in stock market prices, Weather forecast, Life Expectancy etc.
Unsupervised Learning
As you may have guessed correctly the opposite of Supervised Learning is Unsupervised Learning when it comes to labelled data. With unsupervised learning, you do not know whether your friend is there at home or gone out for some work, it is up to the computer to find patterns via a model to guess what happened or predict what will happen.
Most Unsupervised Learning techniques are a form of Cluster Analysis.
Cluster Analysis : In Cluster Analysis, you group data items that have some measure of similarity based on characteristic values. For eg. market segmentation (types of customers, loyalty) or to detect abnormal behavior.
Before winding up the blog let us look at some of the applications of Machine Learning
- Your personal Assistant Siri or Google uses ML.
- Weather predictions for the next week comes using ML.
- Win Predictor in a sports tournament uses ML.
- Medical Diagnosis dominantly uses ML.
- And something you would be familiar with, ever wondered how come media sites shows you recommendations and ads matching closely to your interests? They as well use Machine Learning.
- Online Fraud Detection
- Self driving cars
- Stock Market Predictions
and many many more……
Hope you enjoyed this article. Thanks for stopping by! Adieu.