Top 9 Popular Python Libraries for AI-ML Applications

AI-ML is one of the most code-dense applications in computer science. Thanks to the simpler syntaxes and versatility of Python, it intrigued a vibrant community of coders to develop and contribute algorithms that suit Machine Learning  (ML) operations. The norm about ‘get more and code less’ has ranked Python to the top. Plus Python in itself is highly intuitive, as a result, it’s a faster alternative for ML engineers to make smarter predictive algorithms. 

All the years of python development have accumulated a fair amount of algorithms that are ready to use. The extensive math work from the coders is curated as numerical libraries which reduce the burden to code and are converted to functional toolkits that can be applied to varied chapters of ML. The standard libraries include a number of internet protocols, string operations, web service tools, and operating system interfaces that are scripted. Another advantage to Python is loyalty-free use even for commercial purposes and OSI services which helps it to take part in the larger part of a larger part of the community-driven by similar sentiments. 

Without further adieu let us take a look at the python libraries that we have listed today. 

1. Numpy - One of the most popular libraries among python users which consists of a large multi-dimensional array and matrix processing algorithms, making it a rich source of high-level mathematical functions. Operations like linear algebra, Fourier transforms, random number calculations, and probability functions can be performed easily by using Numpy solutions. Numpy harbors popular ML libraries which allow scientific computations and with the growing volume of the scripts, the libraries forked into sub-libraries like scipy, sci-kit learn, and others. Plus it is an essential component in the growing Python visualization landscape, enabling researchers to visualize datasets better than native python itself.

2. Scikit-learn- previously known as scikits.learn or Sklearn, which has a wide range of  ML algorithms and simple & efficient tools for predictive data analysis. It hosts an array of features used for various classification, to perform regression analysis, clustering,  multi-dimensional reduction in ML, and pre-processing of data for supervised and unsupervised learning. This spectrum of tools is built based on  NumPy, SciPy, and matplotlib and share open access to users and can be reused in a different context. 

3. matplotlib - A plotting function library for Python which is extensively used for data visualization purposes. Unlike Pandas which is directly related to machine learning, it behaves as numerical mathematics extensions that enable 2D to 3D plotting and are used to provide object-oriented APIs. With the help of matplotlib, a programmer will be able to use general-purpose GUIs toolkits like Tkinter, wxPython, Qt, or GTK. Its Pylab interface closely resembles MATLAB, which branched as an intention of Scipy. In simpler terms, it’s an easy module for data visualization like histogram, error charts, bar charts.

4. TensorFlow- A computational framework extensively used for tensor modeling for ML applications. TensorFlow not only supports a variety of different toolkits for constructing models it also runs smoothly with Python and C++ APIs. It has a flexible architecture and can run computational platforms like CPUs, GPUs, and TPUs. TPU stands for Tensor processing unit, a hardware chip specifically built around TensorFlow for ML-AI applications. Plus it can train and run deep neural networks and widely used for deep learning research and applications.

5. Keras - Keras comes as default after TensorFlow as the library is built based on the toolkits Theano, Microsoft Cognitive, R, PlaidML, and of course TensorFlow. Keras also can run efficiently on CPU and GPU, involving in operations like neural-network building, activation functions, and optimizers. Keras supports convolutional and recurrent neural networks and also has a bunch of features for images and text image editing to writing Deep Neural Network codes. 

6. Pandas- 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 - in this case, Pandas comes in handy as it was developed specifically for data extraction and preparation. It provides high-level data structures and a wide variety of tools for data analysis. It provides many inbuilt methods for groping, combining, and filtering data.

7. Scipy- A popular ML library thus containing different modules for optimization, linear algebra, integration, and statistics. There is a difference between the SciPy library and the SciPy stack. The SciPy is one of the core packages that make up the SciPy stack. SciPy is also very useful for image manipulation.

8. Theano- a python library that evaluates and optimizes mathematical expressions involving multi-dimensional arrays. It is achieved by optimizing the utilization of CPU and GPU and used for unit-testing and self-verification, diagnosis and detection of different types of errors. Theano is a very powerful library that has been used in large-scale computationally intensive scientific projects for a long time but is simple and approachable enough to be used by individuals for their own projects.

9. Pytorch- PyTorch is a popular open-source Machine Learning library for Python based on Torch, which is an open-source Machine Learning library that is implemented in C with a wrapper in Lua. It has an extensive choice of tools and libraries that support 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.

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