Python Machine Learning Libraries With Examples

What is Machine Learning?

As the web is immensely growing with each day, analyzing data based on a pattern by human intervention is becoming challenging. To leverage this, computer programs are being developed that can analyze data.

This allows computers to learn automatically to observe data, look for patterns and make better decisions without much human intervention.

This process of making computers to get trained with a given data set to predict the properties of the data is called Machine Learning.

For example, we can train the computer by feeding numerous images of cars to teach the computer to recognize a car. Numerous other images that are not cars can be fed to the computer. From the above training, the computer can recognize the image of a car.

Machine Learning has earned huge popularity in recent decades and transforming our lives.

Why Python for Machine Learning?

Unlike other computer languages like C, Java, etc., Python is renowned for its readability and less complexity.

Anyone can easily understand it and make others to easily understand it, too.

Data Scientists can make use of Machine Learning to analyze huge volumes of data and draw useful insights with very less effort.

Python supports a lot of popular in-built libraries that can be readily used to provide Machine Learning functionality.

These libraries have 0 learning curve. Having a basic understanding of Python allows programmers to implement these ready to use libraries.

The best part is, these Python packages are free under GNU license.

Recommended Read: Python Data Science Libraries

Python Machine Learning Libraries

Let’s go through some of the commonly used libraries used in the field of Machine Learning.

1. NumPy

NumPy is a core Python package for performing mathematical and logical operations.

It supports linear algebra operations and random number generation. NumPy stands for “Numerical Python”.

NumPy has built-in functions to perform linear algebra operations. NumPy supports multi-dimensional arrays to perform complex mathematical operations. It is essential for fundamental computations in the field of Machine Learning.

2. SciPy

SciPy is a Python library that is built upon NumPy.

It makes use of NumPy arrays. SciPy is significantly used for performing advanced operations like regression, integration, and probability.

Hence, SciPy is popularly used in the field of Machine Learning as it contains efficient modules for statistics, linear algebra, numerical routines, and optimization.

3. Scikit-Learn

Scikit-Learn is a popular open-source Machine Learning library that is built on top of two famous Python libraries namely NumPy and SciPy.

It features classical ML algorithms for statistical data modeling that includes classification, clustering, regression and preprocessing.

It also provides effective and easy to use Machine Learning tools.

Scikit-learn supports popularly used supervised learning algorithms, as well as unsupervised learning algorithms. The algorithms include support vector machines, grid search, gradient boosting, k-means clustering, DBSCAN and many more.

Along with these algorithms, the kit provides sample datasets for data modeling. The well documented APIs are easily accessible.

Scikit-learn library is known for its optimal performance across various platforms. This is the reason for its popularity.

Hence, it is used for academic and commercial purposes. Scikit-learn is used to build models and it is not recommended to use it for reading, manipulating and summarizing data as there are better frameworks available for the purpose. It is open-source and released under the BSD license.

4. SymPy

SymPy, as the name suggests is a symbolic computation Python library that mainly focuses on algebraic computations.

Many data scientists use the SymPy library for intermediate mathematical analysis of data. This analysis can be later consumed by other Machine Learning libraries.

5. Shogun

Shogun is a free, open-source toolbox used for ML that is implemented in C++.

It supports an interface to multiple languages (Python, Java, C#, Ruby, etc) and platforms (Linux, Windows, macOS).

Anyone, be it data scientists, journalists, hackers, students, etc can use Shogun with minimal effort and free of cost.

It provides effective implementation of the standard ML algorithms like SVM, kernel hypothesis, multiple Kernel learning, etc.

Shogun comes along with binary installation packages for scaling multiple systems and hence, provides an extensive testing infrastructure.

Users can download its docker image and locally run the Shogun cloud. Shogun can scale dozens of OS setups and process about 10 million data samples accurately. Shogun’s cloud is non-commercial and available for educational purposes at universities.

6. TensorFlow

TensorFlow was initially developed for Google’s internal use by Google Engineers.

But, the system is general enough to be applied for a variety of domains. At the year 2015, the library became open source and was released under Apache 2.0 open source license.

TensorFlow is a popular library for dataflow programming. It is a symbolic math library that uses different optimization techniques to make efficient calculations. This Python package is used for the application of Machine Learning and neural network.

TensorFlow provides robust and scalable solutions for computations across numerous machines and for computations involving huge data sets. Hence, it is the preferred framework for Machine Learning.

The library is extensible and supports numerous platforms. It provides GPU support for faster computations, improved performance, and better visualization. TensorFlow provides algorithms for classification, estimation models, differentiation, etc.

TensorFlow provides rich API support for training neural networks and speech recognition using NLP (Natural Language Processing).

7. Theano

Theano is a numerical computation library primarily used for implementing neural network models.

Theano allows to efficiently define, optimize and evaluate mathematical expressions effectively. Theano focusses on solving complex mathematical equations. It uses a multi-dimensional matrix using NumPy to perform these complex operations.

Theano can find out unstable expressions and replace them with stable ones to evaluate the expressions.

Theano can make effective usage of GPUs. It provides speed optimization by executing parts of expressions in CPU or GPU.

Theano is smart enough to automatically create symbolic graphs for computing gradients and thereby provides symbolic differentiation. Theano is platform-independent.

Along with the mentioned features, Theano provides a unit testing platform for error detection.

8. PyTorch

PyTorch is a Python-based scientific computing package targeted for Machine Learning.

It is a replacement for NumPy and provides maximum speed and flexibility by making use of multiple GPUs.

PyTorch also provides custom data loaders and simple preprocessors. PyTorch provides an interactive debugging environment that allows users to debug and visualize effortlessly. It provides an easy to use API.

PyTorch support imperative programming. It performs computations on the fly. The biggest benefit of this feature the code and the programming logic is debugged after each line of code.

PyTorch supports dynamic graphs. Instead of using predefined graphs having specific functionalities, PyTorch provides a simple framework to build computational graphs dynamically and also make changes to them during runtime. This is useful in situations where memory requirements for creating a neural network is unknown.

9. Keras

Keras is a high-level neural networks API. It is written in Python and can run on top of Theano, TensorFlow or CNTK (Cognitive Toolkit).

Keras is a user-friendly, extensible and modular library which makes prototyping easy and fast. It supports convolutional networks, recurrent networks and even the combination of both.

Initial development of Keras was a part of the research of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). It acts as a plugin for other machine learning libraries.

There are countless deep-learning frameworks available today, but there are some of the areas in which Keras proved better than other alternatives. Keras focuses on minimal user action requirement when common use cases are concerned.

For example, if a user makes an error, clear and actionable feedback is provided. This makes Keras easy to learn and use. Hence, Keras is easy-to-use and is the ideal choice for quick prototyping.

You can easily deploy models to use into other applications very easily, using Keras. Keras also supports multiple backends and allows portability across backends i.
e. you can train using one backend and load it with another.

Keras provides built-in multiple GPU support and supports distributed training.


In this article, we have discussed the commonly used Python libraries for Machine Learning. Hope this tutorial would help Data Scientists to deep dive into this vast field and make the most out of these Python libraries.

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