What we can do with Python Programming?

“What exactly can I use Python for?”

Well that’s a tricky question to answer, because there are so many applications for Python.

But over time, I have observed that there are 3 main popular applications for Python:

• Web Development
• Data Science — including machine learning, data analysis, and data visualization
• Scripting

Let’s talk about each of them in turn.

Web Development

Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.

These web frameworks help you create server-side code (backend code) in Python. That’s the code that runs on your server, as opposed to on users’ devices and browsers (front-end code). If you’re not familiar with the difference between backend code and front-end code, please see my footnote below.

But wait, why do I need a web framework?

That’s because a web framework makes it easier to build common backend logic. This includes mapping different URLs to chunks of Python code, dealing with databases, and generating HTML files users see on their browsers.

Data Science — including machine learning, data analysis, and data visualization

First of all, let’s review what machine learning is.

I think the best way to explain what machine learning is would be to give you a simple example.

Let’s say you want to develop a program that automatically detects what’s in a picture.

So, given this picture below (Picture 1), you want your program to recognize that it’s a dog.

Given this other one below (Picture 2), you want your program to recognize that it’s a table.

You might say, well, I can just write some code to do that. For example, maybe if there are a lot of light brown pixels in the picture, then we can say that it’s a dog.

Or maybe, you can figure out how to detect edges in a picture. Then, you might say, if there are many straight edges, then it’s a table.

However, this kind of approach gets tricky pretty quickly. What if there’s a white dog in the picture with no brown hair? What if the picture shows only the round parts of the table?

This is where machine learning comes in.

Machine learning typically implements an algorithm that automatically detects a pattern in the given input.

You can give, say, 1,000 pictures of a dog and 1,000 pictures of a table to a machine learning algorithm. Then, it will learn the difference between a dog and a table. When you give it a new picture of either a dog or a table, it will be able to recognize which one it is.

I think this is somewhat similar to how a baby learns new things. How does a baby learn that one thing looks like a dog and another a table? Probably from a bunch of examples.

You probably don’t explicitly tell a baby, “If something is furry and has light brown hair, then it’s probably a dog.”

You would probably just say, “That’s a dog. This is also a dog. And this one is a table. That one is also a table.”

Machine learning algorithms work much the same way.

You can apply the same idea to:

• recommendation systems (think YouTube, Amazon, and Netflix)
• face recognition
• voice recognition

among other applications.

Popular machine learning algorithms you might have heard about include:

• Neural networks
• Deep learning
• Support vector machines
• Random forest

You can use any of the above algorithms to solve the picture-labeling problem I explained earlier.

Python for machine learning

There are popular machine learning libraries and frameworks for Python.

Two of the most popular ones are scikit-learn and TensorFlow.

• scikit-learn comes with some of the more popular machine learning algorithms built-in. I mentioned some of them above.
• TensorFlow is more of a low-level library that allows you to build custom machine learning algorithms.

If you’re just getting started with a machine learning project, I would recommend that you first start with scikit-learn. If you start running into efficiency issues, then I would start looking into TensorFlow.