Python Libraries for Machine Learning – Absolute Beginner’s Guide [Infographics]

Introduction

If you are an absolute beginner and just heard that python should be the language of choice for machine learning & data science then you are at the correct place. Python libraries for machine learning are very easy to learn and is useful for beginners who want to have first hand experience of creating their own machine learning model.

The ecosystem of python libraries for machine learning is only growing, all thanks to such an active open source python community. But if you are just beginning out, you should not feel overwhelmed with so many python libraries by trying to learn everything at once. Instead you should initially focus on the most essential libraries of python which are minimum required for doing practical machine learning and data science.

The below infographics list downs the most basic but essentials python libraries which you should learn to create your first machine learning model as a beginner.

Python Libraries for Machine Learning

    • In machine learning, the complex mathematical operations can be done efficiently if the data is represented in arrays or multi dimensional arrays.
    • Numpy has powerful capabilities for computing n-dimensional arrays for linear algebra operations. 
    • Numpy should be the first library you should understand as a beginner in machine learning / data science.
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    • Before creating machine learning model we have to analyse data, manipulate it, pre-process it and make it clean for building model.
    • Pandas library allows you to convert numpy array or raw data into Data Frames (similar to database tables) and provide capabilities for easy data analysis and pre-processing.
    • Pandas should be the second python library in your list to learn machine learning / data science.
    • Visualization is very important to explore data during pre-processing phase and interpret the results generated by machine learning model.
    • Matplotlib is powerful library that can generate plots like histogram, pie chart, heat map, scatter plot and many other types of graphs and visualization.
    • Matplotlib should be the next library you should learn for visual story telling of data.
    • This is where the machine learning magic takes place.
    • Scikit Learn is a very powerful python library which has ready to use modules for creating machine learning models using various algorithms.
    • You should make yourself aware about various machine learnig algorithms and hyper parameters tuning options available in Scikit learn.
    • Deep Learning is a specialized field within Machine Learning that deals with neural networks
    • Keras library encapsulates complex deep learning frameworks like Tensorflow, Theanos, CNTK and provides user friendly high level APIs to create deep neural networks in modular fashion.
    • If you want to quickly learn deep neural network you should can use Keras as a beginner.

In The End…

If you are not beginner you might be thinking where is Tensorflow or Pytorch for deep learning or Seaborn for visualization in the list. Well as I said earlier, although these libraries are very amazing but these are not essentially required for your first machine learning project.

As a matter of fact, you can create almost any powerful model with just these 5 libraries listed above. So build your basics rights with these libraries and then cross the ship to more advanced libraries to take yourself to the next level.

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