Top 20 python libraries for machine learning and deep learning

In this article, you will learn about Top 20 python libraries for machine learning and data science.

1. Statsmodels
2. Numpy
3. Scipy
4. Pandas
5. Matplotlib
6. Scikit-learn
7. Seaborn
8. Bokeh
9. Ploty
10. Nltk
11. Spacy
12. Tensorflow
13. Theano
14. Keras
15. Gensim
16. Scrapy
17. XGBoost
18. LightGBM
19. CatBoost
20. Pytorch

1. 1. Statsmodels: It is a Python module that provides classes and functions. It is helps to works on  different statistical models. We can conduct easily statistical tests for data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. It’s Released under open source Modified BSD (3-clause) license. You can read the documentation from here. We need to import the statsmodels library to work on it.
   2. Numpy: It is a library for the Python programming language. It adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. It was first release on 1995 as numeric, then released as numpy in 2006. Its written in python and c. The Current stable version release is 1.16.0. The License type is BSD-new license. The Author for Numpy is Travis Oliphant. You can follow the documentation from here.
   3. Scipy: It is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering
   4. Pandas: In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license
   5. Matplotlib: It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.
   6. Scikit-learn:  Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
   7. Seaborn:  Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
   8. Bokeh : It is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
   9. Ploty: Plotly is a company that makes visualization tools including a Python API library. (Plotly also makes Dash, a framework for building interactive web-based applications with Python code). For this article, we’ll stick to working with the plotly Python library in a Jupyter Notebook and touching up images in the online plotly editor. When we make a plotly graph, it’s published online by default which makes sharing visualizations easy. Learnmore
   10. NLTK : The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language. Learnmore
   11. Spacy: It is an open-source software library for advanced Natural Language Processing, written in the programming languages Python and Cython. Learn more
   12. Tensorflow: TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. Learn more
   13. Theano: Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures
   14. Keras: Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible
   15. Gensim: Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance
   16. Scrapy: Scrapy is a free and open-source web-crawling framework written in Python. Originally designed for web scraping, it can also be used to extract data using APIs or as a general-purpose web crawler. It is currently maintained by Scrapinghub Ltd., a web-scraping development and services company
   17. XGBoost: XGBoost is an open-source software library which provides a gradient boosting framework for C++, Java, Python, R, and Julia. It works on Linux, Windows, and macOS. From the project description, it aims to provide a “Scalable, Portable and Distributed Gradient Boosting Library”.
   18. LightGBM: LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: Faster training speed and higher efficiency. Lower memory usage, Better accuracy, Support of parallel and GPU learning, Capable of handling large-scale data.
   19. CatBoost: CatBoost is an algorithm for gradient boosting on decision trees. Developed by Yandex researchers and engineers, it is the successor of the MatrixNet algorithm that is widely used within the company for ranking tasks, forecasting and making recommendations. It is universal and can be applied across a wide range of areas and to a variety of problems.
   20. Pytorch: PyTorch is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing. It is primarily developed by Facebook’s artificial-intelligence research group, and Uber’s “Pyro” software for probabilistic programming is built on it.