Top Python Libraries for ML

By NIIT Editorial

Published on 16/04/2021

6 minutes


In software, libraries are a set of pre-defined routines and functions created in a programming language. Writing long lines of code is a thing of the past these days. Libraries act as a short-cut to implement code that would otherwise consume time to write. 


Machine learning is a field driven and rested on mathematical foundations. Python helps effectuate high-end computations. Its libraries play an important role for machine learning experts who can just plug and play the code without having to worry about its accuracy. 


Here are 5 python libraries that are useful in ML. 


Scikit Learn 


Scikit learn is a free-to-use machine learning library that is used for supervised and unsupervised learning algorithms such as K-means clustering, decision trees, and logic/linear regressions. 


It integrates well with other Python libraries such as SciPy and NumPy. Developed by David Cournapeau as a summer Project at Google in 2007, Scikit Learn is often recommended for Python practitioners still at the beginning phase of their career, before they can move onto implementing complex algorithms. 


You can perform cross-validation to calculate the performance of supervised models. To begin using Scikit Learn, users need to install Scientific Python (SciPy).  




TensorFlow can be used for a range of activities but it is particularly lauded for its custom-focus on the training and inference of deep neural networks. Based on differentiable programming, it is a math library that allows scalable computations for data sets. Operating on TensorFlow, the same node can run on both CPU and GPU. The library also works with mathematical expressions with multi-dimensional arrays. 


Google uses TensorFlow as a speech recognition tool in its voice-recognition app. 




This is yet another Python library with which you programmers can conduct a whole host of operations from defining, and optimizing to evaluating multi-dimensional arrays. With features like unit testing and self-verification, you can identify bugs in the initial phases of model development. Dynamic c-code generation helps evaluate programmatic expressions faster. 


Theano runs on a GPU, offering speed optimizations of upto 140 times against a conventional CPU. This library had its last version released back in 2017, when improvements in interface changes had been made. 




This Python library offers the use of high-level data structures. It has built-in time series functionality and can merge high-performance data easily. Given its niche customization into data structures, users can delete or insert columns rather comfortably. The library allows you to reshape and pivot data sets. 


When working with Python and tables, there is hardly a library better than Panda. 




All the libraries mentioned above offer value-addition towards algorithmic operations. But an essential last step of the way includes communicating the insights to other business stakeholders. Matplotlib is a low-level Python library that has become a mainstay for creating 2-dimensional graphs and plots. It requires a greater number of commands than with some advanced libraries. 


The advantage of these many commands is that you can design the presentation any way you want. This ranges from histograms to non-cartesian coordinate graphs. 


All in all, when it comes to data visualization in Python, you choose Matplotlib. 

Whether self-taught or a product of online tutorials, machine learning professionals have a lot to learn and need specialized training to rise in their career. 


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