Before we talk about anything,how about we begin with a friendly example? When you receive an email, the provider automatically places it into the inbox/spam folders. Almost all the time, they are correctly placed in their corresponding folders while sometimes, even the mails that we wanted to see in our inbox are marked as spam 😕. But more than all this, who does this job for us? 🤔

Machine Learning is the magician in the background here!

Machine learning makes a certain task easier by learning on a set of data. The times, the mail has been correctly placed in the inbox is how accurate the model is and the times it fails to do the job, depend on how accurate the Machine Learning model is.

In simple terms, Machine Learning uses some set of algorithms to learn different examples of similar data to perform a specific task for a particular domain.

When I began to learn Machine Learning, I found this course by Google that helped me understand the concepts better. You can practice Machine Learning anywhere as far as you have a machine with good computational capacity. Now once you have a model working fine, what next? How do you put it in action? You need to deploy it somewhere, right? There are a lot of options you can choose from like either get a space on the cloud, create your own environment and deploy there or choose from existing service providers like Amazon Web Services, Microsoft Azure or Google Cloud.

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