This repository has introductory programs of 9 ML libraries with definitions

NumPy is a very popular python library for large multi-dimensional array and matrix processing.

It is very useful for fundamental scientific computations in Machine Learning.

It is particularly useful for linear algebra, Fourier transform, and random number capabilities.

High-end libraries like TensorFlow uses NumPy internally for manipulation of Tensors.

SciPy is a very popular library among Machine Learning enthusiasts as it contains different modules for optimization, linear algebra, integration and statistics.

SciPy is also very useful for image manipulation.

Scikit-learn is one of the most popular ML libraries for classical ML algorithms.

It is built on top of two basic Python libraries, viz., NumPy and SciPy.

Scikit-learn supports most of the supervised and unsupervised learning algorithms.

Scikit-learn can also be used for data-mining and data-analysis, which makes it a great tool who is starting out with ML.

Theano is a popular python library that is used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays in an efficient manner.

It is achieved by optimizing the utilization of CPU and GPU.

It is extensively used for unit-testing and self-verification to detect and diagnose different types of errors.

TensorFlow is a very popular open-source library for high performance numerical computation developed by the Google Brain team in Google.

As the name suggests, Tensorflow is a framework that involves defining and running computations involving tensors.

It can train and run deep neural networks that can be used to develop several AI applications.

Keras is a very popular Machine Learning library for Python.

It is a high-level neural networks API capable of running on top of TensorFlow, CNTK, or Theano.

It can run seamlessly on both CPU and GPU.

Keras makes it really for ML beginners to build and design a Neural Network. One of the best thing about Keras is that it allows for easy and fast prototyping.

PyTorch is a popular open-source Machine Learning library for Python based on Torch, which is an open-source Machine Learning library which is implemented in C with a wrapper in Lua.

It has an extensive choice of tools and libraries that supports on Computer Vision, Natural Language Processing(NLP) and many more ML programs.

It allows developers to perform computations on Tensors with GPU acceleration and also helps in creating computational graphs.

Pandas is a popular Python library for data analysis.

It is not directly related to Machine Learning. As we know that the dataset must be prepared before training.

It provides high-level data structures and wide variety tools for data analysis.

It provides many inbuilt methods for groping, combining and filtering data.

Matplotlib is a very popular Python library for data visualization.

Like Pandas, it is not directly related to Machine Learning. It particularly comes in handy when a programmer wants to visualize the patterns in the data.

It is a 2D plotting library used for creating 2D graphs and plots.

A module named pyplot makes it easy for programmers for plotting as it provides features to control line styles, font properties, formatting axes, etc.