Starting in Machine Learning

This article is about getting the basics and essential terminologies to start with Machine learning.

Currently, ML has become one of the hottest topics in this technology arena. And, it is in huge demand and the most crucial in every industry to increase the profit and better predicting the future of the industry.

What is Machine learning and why do need it?

Machine learning is about finding the patterns in the existing data, then create and use a model that recognizes those patterns in new data.

Machine learning process

Here, the most confusing to understand could be “patterns” so let’s try to understand via example, suppose we have the telecom company “xyz” and we have the task to find out how many customers in the coming months can switch to the different provider. So, for that we have provided with:

• Historical data of the customers which includes registered complaints on our customer support channel.
• Here the registered complaints are the “patterns” which we find out using the ML algorithm to create the model.
• Now, we have the model created and deployed and we can supply the newly registered complaints (data) to see if it matches the existing complaints (patterns) to predict whether the customer will switch or not.

This is how we can use machine learning to predict the future of the industry and can help us to take the adequate steps before something goes wrong.

It is not only limited to the specific industry we can also use ML in identifying credit cards frauds, sorting out spam emails and self-driving cars, etc.

Why is Machine learning is so hot now?

Doing Machine learning well requires:

• Lots of data.
• Lots of compute power.
• Effective machine learning algorithms.

All of those things are available than ever.

Important terminologies in Machine learning:

Commonly used Machine learning algorithms:

These algorithms play a significant role in creating the model from the prepared data.

What do we need to start in Machine learning?

If we want to begin ML today, we need the following:

• Data: It acts as the raw material for ML.
• Machine learning algorithms: It is the tough and the most crucial part to master but not impossible, we will need to learn calculus, probability, and statistics, etc. These algorithms will process the data to create the model to make the prediction with the newly supplied data.

I hope we have got some necessary clarity what exactly ML is and how we can start adopting it today. This is the most demanding skill in the IT industries these days.

Happy learning :)