Python continues to be a constant companion for data science enthusiasts with its industry-wide acceptance, ease of use and the sheer number of Python libraries for data science.

Given below is a list of Top 10 Python Libraries you may have missed!

1. Hug:

Hug is the fastest and the most modern way to develop drastically simplified API’s over multiple interfaces. An ideal option if you wish to make private projects and get a working API in a really short period of time!

More information: Hug

2. Bokeh:

With high-performance interactivity as its biggest advantage, Bokeh is definitely worth the try if you are looking for a library completely independent of Matplotlib.

Aiming at providing concise construction of versatile graphics, over streaming or large datasets, Bokeh is of use to anyone who wants quick and easy creation of interactive plots, data applications, and dashboards.

More information: Bokeh

3. CatBoost:

A well known high-performance open-source library known for its inference time capabilities: CatBoost, is based on scalable gradient boosting over decision trees that can as well run on multiple GPU’s and allow you to run both classification and regression problems. The next time you plan on winning any Data Science competition, you might want to give this a try!

More information: Catboost

4. Eli5:

Eli5 is a package made use of to debug ML Classifiers and help understand their predictions.

With its versatile nature, and successful implementation of several algorithms to inspect Black-Box models, It can visualize critical features on both text data as well as images.

For more information: Eli5

5. StatModels:

StatsModels acts like a supplement to SciPy for statistical computations inclusive of descriptive statistics, statistical models, estimation and has multiple different models built in it. With the stars and the number of people working over the library on GitHub, you might want to check it out and you might find something useful for you!

More Information: Statsmodels

6. Pattern:

Pattern is an exceptionally interesting, full package library that covers many areas of Machine Learning. The library can assist you in Unsupervised Learning, Data Mining, NLP, as well as Network analysis.

Also, Since you can use one library for both Data Mining and Machine Learning, it is not only well documented but also fun to play around with!

For More Information: Pattern

7. Gensim:

Gensim is a library that was created by keeping Natural Language Processing in mind. With its distinguishable feature of having being built in Python, you can utilize it for document indexing, topics modelling, and similarity retrieval with large amounts of data.

Since it doesn't a require huge amount of RAM as all its algorithms are memory in-dependent, it is a masterpiece that you will hear a lot about!

For more information: Gensim

8. Gluon:

A collaboration of Amazon and Microsoft, Gluon is an open-source deep learning interface which allows easy and quick build of machine learning models, without any compromise on performance and aims at simplifying the use of AWS and Azure Platforms.

This library does not confine itself only to the specialists in AI but also extends out to developers of all abilities and is hence a stand out in the list.

For More Information: Gluon

9. SpaCy:

SpaCy, built for advanced Natural Language Processing in Python and Cython, is a reliable library that comes with the fastest syntactic parser in the world and has been designed for multiple languages.

It is a production-ready package with an emphasis on efficiency that can as well be easily used across many Deep Learning frameworks.

For more Information: Spacy

10. Coach:

Coach is a library that stands out in terms of usage and enables you to train state-of-the-art Reinforcement Learning algorithms making the use of multiple different games. Developed by Intel, it has a huge number of supported algorithms and is a library that is a sure recommend.

For more Information: Coach

To get a deeper insight into the above, refer to the link mentioned below:

Video Source: Machine Learning Jack