Top Python Libraries for Machine Learning

python machine learning

Python is getting developers attention after the outstanding works accomplished with this technology in the scientific and data science field. Currently, it is one of the fastest programming languages with more than 63,000 packages available for simplifying complex development tasks. Its open source nature allows new participator to develop and merge new package in this language.

In this article, I’m going to discuss top python libraries which are the building blocks of the intelligent models’ development. Its simplified syntax and built-in libraries make it easy to develop, test and execute your code. One more advantage of the Python Programming is that it is trustworthy and backed up by Google as it is Googles’ official language. Let’s explore these libraries:

1. TensorFlow:

TensorFlow- an open-source deep learning library is developed for performing high-performance numerical computations. It was developed by the Brain team of Google. The flexible architecture of TensorFlow allows developers to deploy their project on various platforms including GPU, TPU or CPU. Google voice-enabled search and photos are the one of the revolutionary product developed on the TensorFlow. Its computational framework allows you to express algorithms through tensors such as neural networks graphs can be implemented on the Tensors (n-dimensional matrices for data representation). TensorFlow is very helpful for data pipelining, distributed computing through the GPUs, serialization, module checkup etc.

2. Seaborn:

This python data visualization library is built on the concept of matplotlib. Its user-friendly interface allows you to create attractive statistical graphs easily. Whether it is a heatmap or a violinplot or a jointplot, draw independently. It’s data set oriented application program interface is very helpful for recognising relationships among multiple variables. Seaborn also allows you to visualize bivariate or univariate distributions easily so that you can compare both the data subsets. One of the best Seaborn features is linear regression plotting which allows developers to create prediction based models in an effective way. In addition, colour palettes, built-in themes and convenient views are available to simplify your work.

3. SciKit Learn:

Build on the SciPy, NumPy and matplotlib like libraries, the SciKit Learn is one of the simple and efficient tools for the machine learning. This open-source library is accessible to anyone who is willing to develop advanced applications comprised of image recognition, spam detection, customer segmentation, and parameter tuning etc. Whether you are going for classification, clustering, regression, model selection or preprocessing- SciKit Learn will help.

4. Keras:

Keras provides high-level neural networks API for executing it on the top of CNTK, Theano or TensorFlow. Currently, Keras has broadly adopted in the various industries with 250,000 individual users. You can see the active Keras application in Netflix, Instacart, Uber, Yelp and many more giants. It allows fast prototyping, run on both CPU and GPU, reliable for convolutional and recurrent networks.

5. Theano:

Theano is another useful Python library which allows a user to represent and evaluate mathematical expressions through multidimensional arrays. It is heavily coupled with NumPy to utilize your work. The transparent involvement of GPU makes it easy to accomplish data-intensive computation tasks which takes more time on the CPU. Here, one can generate C code dynamically for faster evaluation. For large scale computations like scientific research – Theano is best option. It was developed in the Universite de Montreal’s ML group.

Thus, we can see how Python programming has emerged as a powerful language for developing advanced applications. These were the five topmost libraries used for creating ML applications. You can also go for the other libraries like PyTorch, OpenCV, Flask, Astropy etc., depending upon your project’s requirement.


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