Why use Python for Machine Learning and AI? We know about the all-time high popularity of Python and now it is extending its scope in the domains of Machine Learning and Artificial Intelligence. The major reason of the widespread use of the language is its simplicity and ease with which it can be understood. Moreover, it provides amazing libraries and frameworks for ML development, some of which are given below:
TensorFlow is a powerful open-source Python Library which provides an end-to-end platform for developing machine learning applications allowing users to perform a number of complex tasks easily. TensorFlow documentation is one of the best ones available out there with humungous community support when you are looking for a python Machine Learning library. It can be made flexible through Keras Functional API and it offers several other high- and low-level APIs. It is not as easy to use as other libraries and is used for models with huge datasets. It provides exciting features like fast debugging, pipelining, scalability, graphs, parallelism, graphical support etc.
NumPy is a Python module, written in C, which performs useful complex numerical computations on single-dimensional and multi-dimensional data. NumPy performs much faster computations on relatively huge data than other Python modules and libraries. Efficient matrix computations and data reshaping can also be performed through NumPy. Its major subject is numerical data. NumPy provides objects when working on multidimensional arrays. It takes up lesser memory than other python libraries.
SciPy is an open-source Python ecosystem for developing data science, mathematical and machine learning applications. It stands for scientific python and is built on top of NumPy. SciPy is mostly written in Python with a few parts in C. It performs a huge number of high-level python functions and provides support for data manipulation and visualization needed for machine learning pre-processing. Several features provided by this library include image processing, file input and file output, Statistics, Linear Algebra operations, Fast Fourier transforms, Interpolation, Optimizations etc. It can be used to apply the sub-package functions over NumPy arrays but is more powerful than NumPy which contains lesser features.
Scikit Learn is a python extension made on top of SciPy, specially made for machine learning. It is an efficient library which offers a wide variety of supervised and unsupervised machine learning algorithms through a flexible Python interface. The scikit learn stack involves the following technologies
Scikit Learn provides impressive nonfunctional features for enhancing user experience which include proper documentation, efficient performance, effective collaboration and good code quality. The module’s functional features include feature extraction, ensemble methods, feature selection, clustering, cross validation, parameter tuning etc.
Pandas is an important part of Python Data Science stack. While this high-performance open-source library is not mainly a Machine Learning library, but it provides useful data manipulation advantages. It is built on top of NumPy. Python was not capable of carrying out data analysis before Pandas provided the support for it. Pandas works on data in tabular form. It offers DataFrame and Series objects that have proved to be revolutionary for dynamic data analysis. Pandas can accept data from multiple formats like excel, SQL etc. It consumes a lot of memory and performs slow indexing.
Keras is a neural network library in Python which can run on top of TensorFlow or Theano. It is easy to use, shows tie efficient performance and provides an edge to those looking for developing deep learning algorithms. It provides a smooth user experience and allows easy prototyping. Keras is a modular platform that is highly flexible and supports all kinds of deep neural networks. It doesn’t provide an exceptional community support and is suitable for networks which are not trained on huge datasets.
PyTorch is also an open-source Python library with flexible features for development of deep neural networks. It provides easy integration with other Python Machine Learning stack and is easy to use for someone already familiar with Python and NumPy. It supports dynamic graphs and has a smaller community as compared to other Python libraries.
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