Ever tried to speak to your friend in a secret code? We all have tried, right?

And it’s not only us. When Julius Caesar sent messages to his generals, he didn’t trust his messengers. So he replaced every A in his messages with a D, every B with an E, and so on through the alphabet. Only someone who knew the “shift by 3” rule could understand his messages. Pretty clever, right?

This method of converting original data to “secret” data is known as encryption. And, the converted “secret data” itself is known as encrypted data.

While it’s a clever…

This post is part of our Introduction to Machine Learning course at Code Heroku.

Wondered how Google comes up with movies that are similar to the ones you like? After reading this post you will be able to build one such recommendation system for yourself.

It turns out that there are (mostly) three ways to build a recommendation engine:

- Popularity based recommendation engine
- Content based recommendation engine
- Collaborative filtering based recommendation engine

Now you might be thinking “That’s interesting. But, what are the differences between these recommendation engines?”. Let me help you out with that.

**Popularity based recommendation engine:**

Perhaps…

This post is part of our Introduction to Machine Learning course at Code Heroku.

Hey folks, today we are going to discuss about the application of gradient descent algorithm for solving machine learning problems. Let’s take a brief overview about the the things that we are going to discuss in this article:

- What is gradient descent?
- How gradient descent algorithm can help us solving machine learning problems
- The math behind gradient descent algorithm
- Implementation of gradient descent algorithm in Python

So, without wasting any time, let’s begin :)

Here’s what *Wikipedia* says: “Gradient descent is a first-order iterative optimization algorithm…