Why do we need Python and scikit-learn for machine learning? Well, in this short introduction we will try to find out the real reason.
Firstly, let’s try to learn why we need Python.
The real reason behind using Python for machine learning is that Python has a ton of libraries designated for machine learning.
Secondly, for the same reason, data scientists prefer Python over other languages. Because many data science applications are inconceivable without Python.
If you are a complete beginner your journey to learn TensorFlow might start from here.
For the TensorFlow beginners we have a dedicated category – TensorFlow for Beginners.
But besides that, you may need to learn several other machine learning and data science libraries.
As a result, you may check these categories as well – NumPy, Pandas, Matplotlib.
However, without learning Python, you cannot learn the usages of these libraries. Why? Because they all use Python as the Programming language.
Therefore please learn Python at the very beginning and start learning TensorFlow.
And, finally please check our Mathematics, Discrete Mathematics and Data Structures categories specially. We have tried to discuss from basic to intermediate level so that you can pick up the core ideas of TensorFlow.
There are reasons for that. Python, on the one hand, has the power of general purpose programming languages. And on the other hand it has the power of domain specific scripting language.
Finally, as a result, we have many Python libraries like scikit-learning which help us for data loading, visualization, and more.
Why do we need scikit-learn?
Let’s take a brief look at the reasons.
Firstly, scikit-learn is one of the most efficient tools for predictive data analysis.
Secondly, we can access scikit-learn at any time. It’s open source and reusable in various contexts.
And finally, we have seen a few examples of NumPy, SciPy, and Matplotlib already. The scikit-learn is built on top of these libraries.
There are more reasons why we need to use Python and scikit-learn at the same time.
Let’s take a look at them also.
We can interact with Python code through our terminal. For local use we can use the Jupyter Notebook. However, to show examples, we’ve used Google Collab.
Without the use of gigantic Python libraries and tool boxes we cannot even process and visualize data.
Why are we talking about interactivity?
Firstly, machine learning is an interactive process.
As a result, we need tools which are also equally interactive.
Secondly, with the help of Python language we can also interact with existing systems, creating graphical user interfaces, etc.
Lastly, we will try to learn more about scikit-learn.
It’s an open source project, as a result, we can take a look at the source code.
Another great thing about scikit-learn is that it is constantly evolving.
A large scientific community has an association with this project.
Moreover, scikit-learn has a lot of machine learning algorithms that we can use to learn machine learning as a whole.
As we progress we will learn together and make things easier for us to use machine learning.
So stay tuned and cheer up.
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