Machine Learning using Python
Machine Learning using Python

Machine Learning is the new buzz word in the world of Artificial Intelligence. Machine Learning and Python are two inseparable best friends. When you search for Machine Learning, Python is sure to pop up. Machine learning has some side friends like MATLAB, but what makes Python so special? What is the relation of Machine Learning and Python? How to get started with Machine Learning using Python?

In this article, I have penned down all that makes these two compatible with each other. Obviously this article won’t help you learn everything there si to learn about ML and Python. But we see no harm in getting started with the basics.

What is Machine Learning

Simply put, Machine Learning is the working of human brains integrated in machines. When a machine is designed to think like a human, it is in the process of Machine Learning. Simple enough, right?

What is Python

No, not a snake. Well, that too. But Python here refers to the programming language. We don’t need to know when and by whom it was founded because what’s the point? We want to know how to get started with Machine Learning using Python.

Familiarization is the key

If you don’t know where do you want to go, you will never even start your journey. Getting started with Machine Learning requires that you familiarize yourself basic concepts like Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning and Object Detection. You shouldn’t limit yourself to these terms only. There is a lot you need to explored in the domain of Machine Learning. The point is to get an idea of what you are getting yourself in to.

Polish your Mathematics Skills

If you think you can go through Machine Learning without polishing up your Maths skills then boy, oh boy, you are in trouble. You can get a hang of the working of the models but when it comes to practical work, you are bound to find yourself in trouble. Brushing your mathematics skills is very necessary. However, you don’t need to get a degree in Mathematics. Online courses are there to help you. Giving yourself 1 hour everyday can help you the basic Maths skills needed to become a ML master. Here is what you should be learning:

  • Linear Algebra
  • Matrices
  • Tensors
  • Derivatives
  • Gradients

These are enough to start with. Once you get the hang of it, you’ll start exploring things on your own and become a pro.

Learn Python

As if that wasn’t obvious! It is possible that you love the concepts of Machine Learning, but when it comes to coding, you’re like “Blekh”. Understandable. But there is no doing away without it. If you are familiar with the basic syntax of Python, good. But if you are not, do it right now! There are a lot of great online resources which can help you with that. Start off with the basics like lists, tuples, dictionaries. Once you learn that, get a move on. Learn some complex structures. Like functions. Loop through everything. Polish your code. Write shorter lines.Write more comprehensive ones. That’s how you roll.

Here are some of the best free resources to learn Python:

Take it up a notch

Once you get the hang of all the basics, up your game. By this time you would’ve known that Machine Learning requires some libraries. One of the major reasons to use Python for ML is because of the inbuilt libraries this language offers and how easy these make things for us. Libraries are a ready-made package of libraries that you can import in your code by using one simple command.

Libraries can be tricky and sometimes annoying to install. If you are a beginner, using Jupyter Notebook is your go to. You can easily import libraries without having the need to download them. If you are using Linux, then things will be easier to you. If you are using Windows, then you’ll have to go through “Something!”. How to download libraries in Windows is another story that we might explore in another post.

While there is still a lot to know about Machine Learning and Python, this article is a good starting point. Remember, the devil is in the details. You need to start somewhere and then learn the details.

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