10 Bankable Python Libraries That Improve AI Accessibility

SpaCY

According to its website, spaCY is well-versed in tasks related to large-scale information extraction. Since its inception in 2015, spaCY has gradually risen to become an industry staple supported by a vast ecosystem. In this library, users have the freedom to choose from various plugins, integrate them with their machine-learning stack, and build customized workflows. This library is apt for processing entire web dumps.

Features of spaCY:

• Supports 72+ languages.
• Blazing speed.
• Enables multi-tasking with pre-trained transformers like BERT.
• 80 trained pipelines for 24 languages.
• Embedded with pre-trained vectors.
• Provides support for custom models in frameworks like PyTorch and TensorFlow.

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Tensorflow

If you are a beginner or an advanced developer looking for python libraries to create machine learning models for different mediums like cloud, desktop, mobile, and web, TensorFlow fits the bill. When working on the web, use TensorFlow to create new machine learning projects and also deploy existing models using JavaScript. Created by Google, this python library is apt for numerical computing. TensorFlow is not restricted to just numerical computing, it can also be used in both production as well as research and development.

Features of TensorFlow

• A complete platform for machine learning.
• Build and fine-tune models.
• Helps you with model tracking, model retraining, and performance monitoring.
• Use the TensorFlow ecosystem to build and fine-tune models.
• It runs smoothly with single CPU systems, GPUs, mobile devices, and large-scale machines.
• Offers multiple data tools to clean, consolidate, and preprocess data.
• Embedded with tools to validate and transform big datasets.
• Comes with standard datasets for initial training.

PyTorch

If a seamless transition between eager and graph mode is your prime requirement, PyTorch is just what you need. The open-source machine-learning library involves the training and development of a neural network that is based on deep-learning models. PyTorch is compatible with Python as well as C++. Undoubtedly, python has a more polished interface. Big brands like Facebook, Uber, and Microsoft support PyTorch.

Features of PyTorch

• Uses dynamic computation for enhanced flexibility in building complex architectures.
• The rich ecosystem of tools supports computer vision and NLP development.
• Seamless development and effortless scaling.
• Easy switch between eager and graph modes.
• Supported on all major cloud platforms.

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NLTK

NLTK, also known as Natural Language Toolkit consists of libraries and programs for statistical language processing. Considered amongst the most dynamic and powerful libraries, NLTK has packages that are used to make machines that understand human language and respond appropriately. It is suitable for educators, engineers, students, linguists, researchers, and industry users. The best part about MLTK is that it is a free, open-source, community-driven project. It is often referred to as a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

Features of NLTK

• Comes with text-processing libraries for tokenization, tagging, semantic reasoning, parsing, and classification.
• Embedded with libraries and programs for statistical language processing.
• User-friendly interfaces to over 50 corpora and lexical resources such as WordNet.

Genism

This is a python library for document indexing, similarity retrieval with large corpora, and topic modeling. The main target audience for this library is the natural language processing and information retrieval communities. It comes with a host of efficient tools and algorithms including Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation, to work with unstructured data.

Features of Genism

• All algorithms are memory-independent.
• Comes with an intuitive interface that enables users to plug in their own input corpus/datastream and extend with other Vector Space algorithms.
• It comes with a host of efficient tools and algorithms including Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation.

Scikit-learn

This open-source python library comes with a myriad of tools especially for data analysis and modeling. It consists of algorithms that cluster similar objects into sets, categorize objects, decrease the number of random variables, create regressions, and pre-process data.

 Features of Scikit-learn

• Identifies the category.
• Easy to access and reusable.
• Commercially usable.
• Automatically groups similar objects.
• Predicts the continuous-valued attribute associated with a specific object.

PyNLPI

PyNLPI is a python library mainly for the natural language processing community. Pronounced as pineapple, this library contains several modules for various natural language processing tasks. Some of its text analysis and generation tools include tokenization, stemming, and sentiment analysis. Users can perform a few basic tasks like building simple language models, and extraction of n-grams and frequency lists.

Features of PyNLPI

• Text analysis and generation tools include tokenization, stemming, and sentiment analysis.
• Different modules for different natural language processing tasks.
• Perform basic tasks like extraction of n-grams and frequency lists.

OpenCV

This open-source library is apt for image processing and computer vision. This free library provides the user with several efficient tools like object detection and recognition. If you are looking to add computer vision infrastructure to a project, feel confident to use OpenCV as it has a strong and active community besides thorough documentation.

Features of OpenCV

• Capture, and save videos.
• Process images.
• Comes with efficient tools like object detection and recognition.
• Analyzes the video by subtracting background, tracking objects, etc.

Keras    

This neural network API is compatible with TensorFlow, CNTK, or Theano. Its primary functions are to create and test deep learning models. It is said to be user-friendly if you compare it with TensorFlow. Keras comes with numerous implemented parameters and layers that can be used for the construction, configuration, training, and evaluation of neural networks.

Features of Keras

• It comes with prelabeled datasets like CIFAR10 small image classification, and IMDB movie review sentiment classification.
• Easy import of prelabeled data sets.
• Consists of several implemented layers and parameters like optimizers, evaluations metric, etc.
• Construction and configuration of neural network.
• Embedded with Multiple methods for Data Preprocessing.
• Comes wirh .add(), .compile(), and .fit() methods.

PyBrain

This python library for machine learning offers numerous tools for data analysis and modeling. PyBrain, aka Python-Based Reinforcement Learning library, has some user-friendly training algorithms for datasets, networks, and trainers to train and test the network. This is apt for any newcomer who wants to learn about machine learning.

Features of PyBrain

• Uses BackpropTrainer and TrainUntilConvergence to train and converge the datasets.
• Offers tools to build a network.
• Supports SupervisedDataSet and ClassificationDataSet to test, validate and train the network.
• Compatible with neural networks including Feed-Forward Networks, and Recurrent Networks.

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