In the 21st century, most of the applications developed by companies are somehow built using Artificial Intelligence, Machine Learning, or Deep Learning that uses Python Machine Learning library. Usually, AI projects are distinct from conventional projects in the software industry. Variations in development approaches lie in the application framework, the necessary skills needed for the AI-based application, and the need for in-depth analysis.

Hence, one of the important factors involved in developing AI-based applications is the use of a suitable programming language. We should employ a programming language that is efficient in making the applications stable and extensible. For this, companies use the Python programming language as it offers a lot of libraries and packages for the development task, and hence, it is widely used for working on AI-based projects.

Here are a few of the benefits of using Python:

1. Simple and compatible: Python provides a descriptive and interactive code. Although complicated algorithms and adaptable workflows are behind Artificial Intelligence and Machine Learning, the simplicity of Python Machine Learning library and framework, enables application developers to develop reliable systems.
2. Platform-independent: One aspect adding to the success of Python is that it is a language that is independent of the platform on which it is being operated. There are various platforms that support Python, such as Windows, macOS, and Linux. For the most commonly used software, Python language code can be used to build discrete executable programs. This ensures that Python programs can be quickly deployed, and we can use them without having a Python interpreter on operating systems.
3. Large community: According to a survey by Stack Overflow, it is one of the top 10 programming languages used by various software industries. Also, Python is one of the most searched programming languages than any other. It is considered the best language for Web Development as well. It has a large community of developers that can help the newbies starting with Python programming to learn and grow with experienced developers. Now, as we have discussed Python and its benefits, let’s look at the top 10 Python libraries for Machine Learning.

Website: https://numpy.org/

Github Repository: https://github.com/numpy/numpy

Developed By: Community Project (originally authored by Travis Oliphant)

Primary purpose: General Purpose Array Processing

• NumPy is regarded as being one of the most widely used and best Python libraries for Machine Learning. Other libraries, such as TensorFlow and Keras, use NumPy to implement various operations on tensors.
• Created on the top of an older library Numeric, the Numpy is used for handling multi-dimensional data and intricate mathematical functions. Numpy is a fast computational library that can handle tasks and functions ranging from basic algebra to even Fourier transforms, random simulations, and shape manipulations. This library is written in C language, which gives it an edge over standard python built-in sequencing. Numpy arrays are better than pandas series in the term of indexing and Numpy works better if the number of records is less than 50k. The NumPy arrays are loaded into a single CPU which can cause slowness in processing over the new alternatives like Tensorflow, Dask, or JAX, but still, the learning of Numpy is very easy and it is one of the most popular libraries to enter into the Machine Learning world.

Few of the points in favor of NumPy are:

• Support for mathematical and logical operations

• Shape manipulation

• Sorting and Selecting capabilities

• Discrete Fourier transformations

• Basic linear algebra and statistical operations

• Random simulations

• Support for n-dimensional arrays

Website: https://pandas.pydata.org/

Github Repository: https://github.com/pandas-dev/pandas

Developed By: Community Developed (Originally Authored by Wes McKinney)

Primary Purpose: Data Analysis and Manipulation

• One of widely used Python’s Machine Learning library is Pandas. Pandas is the best Python library that is majorly used for data manipulation. It uses handy and descriptive data structures such as DataFrames to create programs for implementing functions. Developed on top of NumPy, it is a quick and easier-to-use library.
• Pandas provides data reading and writing capabilities using various sources such as Excel, HDFS, and many more. If you are planning on a use case for a real-world Machine Learning model, then, sooner or later, you would use Pandas for implementing it.

Some of the great features of Pandas when it comes to handling data are:

• Dataset reshaping and pivoting

• Merging and joining of datasets

• Handling of missing data and data alignment

• Various indexing options such as Hierarchical axis indexing, Fancy indexing

• Data filtration options

Website: https://matplotlib.org/

Github Repository: https://github.com/matplotlib/matplotlib

Developed By: Micheal Droettboom, Community

Primary purpose: Data Visualization

• Matplotlib is a library used in Python for graphical representation to understand the data before moving it to data-processing and training it for Machine learning purposes. It uses python GUI toolkits to produce graphs and plots using object-oriented APIs. The Matplotlib also provides a MATLAB-like interface so that a user can do similar tasks as MATLAB. This library is free and open-source and has many extension interfaces that extend matplotlib API to various other libraries.

Website: https://scikit-learn.org/

Github Repository: https://github.com/scikit-learn/scikit-learn

Developed By: SkLearn.org

Primary Purpose: Predictive Data Analysis and Data Modeling

• Scikit-learn is mostly focused on various data modeling concepts like regression, classification, clustering, model selections, etc. The library is written on the top of Numpy, Scipy, and matplotlib. It is an open-source and commercially usable library that is also very easy to understand. It has easy integrability which other ML libraries like Numpy and Pandas for analysis and Plotly for plotting the data in a graphical format for visualization purposes. This library helps both in supervised as well as unsupervised learnings.

Scikit-learn comes with the support of various algorithms such as:

• Classification

• Regression

• Clustering

• Dimensionality Reduction

• Model Selection

• Preprocessing

Website: https://www.tensorflow.org/

GitHub Repository: https://github.com/tensorflow/tensorflow

Developed By: Google Brain Team

Primary Purpose: Deep Neural Networks!

• From the Python Machine Learning library list, we have another most important one that is TensorFlow. It is one of the best open-source libraries used for building Machine Learning and Deep Learning models. It was created by Google’s research team for developing Google products. Eventually, it gained a lot of popularity, and it has proved to be a resourceful library for many business projects by now.
• TensorFlow has a powerful ecosystem of tools and resources for the community. Such kinds of toolsets enable engineers to perform research work on Machine Learning and Deep Learning to build efficient applications. Also, Google continues to add a variety of valuable features to TensorFlow to keep up with the pace of the highly competitive world. However, there are some advantages and disadvantages of using Tensorflow, and they are discussed below.

Some of the essential areas in ML and DL where TensorFlow shines are:

• Handling deep neural networks

• Natural Language Processing

• Partial Differential Equation

• Abstraction capabilities

• Image, Text, and Speech recognition

• Effortless collaboration of ideas and code

Website: https://keras.io/

Github Repository: https://github.com/keras-team/keras

Developed By: various Developers, initially by Francois Chollet

Primary purpose: Focused on Neural Networks

• Keras is a widely used framework/library for fast and efficient experimentation related to deep neural networks. It is a standalone library comprehensively used for building ML/DL models that help engineers develop applications, such as Netflix, Uber, and many more.
• It is a user-friendly library designed to reduce the difficulty of developers in creating ML-based applications. Further, it provides multi-backend support that helps developers integrate models with a backend for providing the application with high stability

Keras features several of the building blocks and tools necessary for creating a neural network such as:

• Neural layers

• Activation and cost functions

• Objectives

• Batch normalization

• Dropout

• Pooling

Website: https://pytorch.org/

Github Repository: https://github.com/pytorch/pytorch

Developed By: Facebook AI Research lab (FAIR)

Primary purpose: Deep learning, Natural language Processing, and Computer Vision

• PyTorch is a framework that enables the execution of tensor computations. It helps create effective computational graphs and provides an extensive API for handling the errors of neural networks. Pytorch is a Facebook-developed ML library that is based on the Torch Library (an open-source ML library written in Lua Programming language). The project is written in Python Web Development, C++, and CUDA languages. Along with Python, PyTorch has extensions in both C and C++ languages. It is a competitor to Tensorflow as both of these libraries use tensors but it is easier to learn and has better integrability with Python. Although it supports NLP, but the main focus of the library is only on developing and training deep learning models only.

The various modules PyTorch comes with, that help create and train neural networks:

• Tensors — torch.Tensor

• Optimizers — torch.optim module

• Neural Networks — nn module

• Autograd

1. https://www.zenesys.com/blog/top-10-python-libraries-for-machine-learning
2. https://intellipaat.com/blog/top-python-libraries-for-machine-learning/#no10
3. https://towardsdatascience.com/best-python-libraries-for-machine-learning-and-deep-learning-b0bd40c7e8c