Vocabulary Sheet on Common Python Machine Learning Libraries

By Rifana S

1-Numpy-NumPy is a Python library used for working with arrays.

It also has functions for working in the domain of linear algebra, Fourier transform, and matrices.

NumPy was created in 2005 by Travis Oliphant. It is an open-source project and you can use it freely.

It also stands for Numerical Python.

2-Pandas — It is an open-source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. It is extremely helpful in the field of data science.

3-Keras- Keras is an open-source high-level Neural Network library, which is written in Python and is capable enough to run on Theano, TensorFlow, or CNTK. It was developed by one of the Google engineers, Francois Chollet

4-Pytorch- PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook‘s AI Research lab (FAIR). It is free and open-source software released under the Modified BSD license.

5-Librosa: It is a powerful Python library built to work with audio and perform analysis on it. It is the starting point towards working with audio data at scale for a wide range of applications such as detecting voice from a person to finding personal characteristics from audio.

6-TensorFlow: It is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks

7-Scikit learn: Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistent interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy, and Matplotlib.

8-LightGBM: Gradient Boosting is one of the best and most popular machine learning libraries, which helps developers in building new algorithms by using redefined elementary models and namely decision trees. Therefore, there are special libraries that are designed for fast and efficient implementation of this method.

These libraries are LightGBM, XGBoost, and CatBoost.

9-SciPy: SciPy is a machine-learning library for application developers and engineers. SciPy library contains modules for optimization, linear algebra, integration, and statistics. The main features of the SciPy library are developed using NumPy, and its array makes the most use of NumPy.

10-THEANO: Theano is a computational framework machine learning library in Python for computing multidimensional arrays.

RESOURCES USED:

https://www.bing.com/ck/a?!&&p=127d6378fefc6637JmltdHM9MTY3MTA2MjQwMCZpZ3VpZD0wMGE1Y2RiYy0yMDk0LTYyYzMtM2VmZS1kYzc2MjEzOTYzZTkmaW5zaWQ9NTI2NQ&ptn=3&hsh=3&fclid=00a5cdbc-2094-62c3-3efe-dc76213963e9&psq=scikit+learn+python&u=a1aHR0cHM6Ly9kYXRhZ3kuaW8vcHl0aG9uLXNjaWtpdC1sZWFybi1pbnRyb2R1Y3Rpb24v&ntb=1

Introduction to TensorFlow — GeeksforGeeks

LightGBM (Light Gradient Boosting Machine) — GeeksforGeeks

What Is Scikit Learn In Python — Python Guides

Top 10 Libraries in Python to Implement Machine Learning | HackerNoon