Top 5 Machine Learning Libraries in Python


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Machines are becoming more intelligent by day. With simple data observations, they can automatically pick out recurring patterns and make better decisions without any human intervention.

The explosive growth of machine learning is largely driven by multiple open-source tools which makes it easier for Python developers to familiarize with this language and adjust accordingly.

In this article, we are going to explore top 5 Machine Learning Libraries in Python. If you are a developer, they will help you design a robust and performance-centered machine learning apps in Python. Their functionality is unmatched and can be directly imported into your application.

So, why is Python so popular or why is it considered best programming language for machine learning in particular situations? Often regarded as utilitarian, Python is a general-purpose language specifically designed to simplify read and write. The language doesn’t overemphasize on conventional syntax, making it easier to work with. No wonder, Python developers are in-demand and are often required on different types of projects. Even if it’s an issue to find and hire one locally, companies use other models of hires – check over here.

Another reason why Python has been trending is the increasing demand for Data Science and AI skills. The two have been branded as the future of technology and the language is fast becoming the programming language of choice for machine learning professionals and data scientists.

Here is our list of top 5 best Python ML packages:

1. Tensorflow

If you have been researching how to become a machine learning engineer, chances are that you have come across the term Tensorflow. It is an open-source Python ML library that was developed by Brain Team at Google and widely used by most Google applications for machine learning purposes. A good example is the Google voice as the model is built using this library.

This computational framework expresses algorithms that involve multiple Tensor operations simply because neural networks can be presented in form of computational graphs. The expression is implemented in a series of Tensors which are n-dimensional matrices that represents your data.

2. Numpy

Numpy is another great mathematical and scientific computing library for Python. It’s internally used by other libraries such as Tensorflow to perform several other operations on Tensors. The library features the powerful array interface which is mostly used to translate sound waves, images, and other binary data streams in form of N dimensions.

Besides the obvious scientific uses, the library can also be deployed as a logical multidimensional generic data container.

3. Theano

Theano is another great computational framework that comes in handy when computing multidimensional arrays. Theano tightly integrates with Numpy and can execute data-intensive computations compared to a typical CPU.

Although the library has similarities with Tensorflow, it leaves a lot to be desired in terms of fitting into production environments.

4. Keras

Keras is one of the best libraries for beginners learning how to use Python for machine learning. It allows for easy neural network expression at the same time provide datasets processing utilities and compiling models.

Internally, Keras can use either Tensorflow or Theano although it’s also compatible with other neural network frameworks such as CNTK.

Since Keras’ backend infrastructure is used for performing operations and computing graphs, it can be relatively slow. With that said, it’s a cool framework for you if you are into Python programming.

5. Scikit-Learn

Scikit-Learn is a deep machine learning toolkit for Python. It is specifically designed to interoperate with multiple other scientific and numerical Python libraries such as Numpy and SciPy.

This library offers both supervised and unsupervised learning algorithms courtesy of the consistent Python interface.

Final Thoughts

Python has continued to dominate the web development world for some time. The question then becomes: what can you do with Python? Well, owing to the explosion of machine learning, this programming language will help you build ML algorithms. Python also supports a majority of the popular ML including TensorFlow, Theano, Keras, and many others. If you are looking to build a machine learning technology stack or just pick some machine learning skills, the above list of best Python libraries is a good place to start.

About Author:

Anastasia Stefanuk is a passionate writer and a marketing manager at Mobilunity. The company provides professional staffing services, so she is always aware of technology news and wants to share her experience to help tech startups and companies to be up-to-date.”
Let me know what you think.


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