Top 10 Python Data Science Libraries

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February 23, 2022

Python is undoubtedly the most popular and user-friendly programming language for implementing data science and machine learning-related tasks. Since Python has a vast collection of advanced packages or libraries, users or developers can use it for seamless implementation of any high-end AI-based tasks. Python has more than 137,000 libraries where each library focuses on implementing a particular function or purpose to your program. When it comes to high-end processes like machine learning and deep learning, there are extensive collections of Python data science libraries that help you in various phases of the ML model development life cycle, including data loading, data visualization, and model building.

This article focuses on the top 10 Python library lists that are used in different phases of the model development lifecycle.

TensorFlow

Developed by Google in 2015, TensorFlow is an open-source and most popular Python data science library for deep learning and artificial intelligence applications. With TensorFlow, you can quickly solve any complex numerical computations and implement large-scale machine learning models, including handwritten digit classification, image recognition, text classification, and recommendation systems. TensorFlow provides a comprehensive ecosystem of tools and APIs for developers, enterprises, and researchers. Such tools help developers to build and deploy scalable machine learning and deep learning applications.

Designed to be a highly compatible library, TensorFlow can run on various devices and platforms, incorporating the advantage of each. TensorFlow is being used by data science and machine learning teams of the world’s top companies to serve and understand customers better. For example, Airbnb uses TensorFlow to build a user-interaction model that automatically guides guests through the payment, cancellation, or refund process. With this, Airbnb provides an instant and smart response to their customers, thereby enhancing the booking experience.

TensorFlow offers standard documentation that guides you through the features, functionalities, and methodologies for implementing machine learning use cases.

Keras

Keras is an open-source and easy-to-use Python data science library for machine learning and deep learning operations. It is also termed as one of the high-level APIs of TensorFlow for implementing neural network strategies on deep learning models. Keras perfectly runs on high-level CPU and GPUs, enabling fast experimentation of neural networks models.

Incorporating multiple neural network models like CNN and RNN in its backend, Keras helps you build high-end and complex deep learning models in less time.

Since Keras is beginner-friendly and fast during model deployments, users can develop high-end deep learning models with minimum codes in less time. As of 2021, Keras is being used by over one million individual users worldwide. Some of the world’s most popular companies like Netflix, Uber, and Instacart use Keras for analyzing customer engagement for delivering a better user experience. For example, Netflix uses Keras to build recommendation systems based on past user preferences.

Keras also offers strong community support to its users by providing standard and easy-to-understand documentation, allowing any user to quickly learn and implement Keras on their own.

OpenCV

Originally developed by Intel, OpenCV is the most popular Python artificial intelligence library for building real-time computer vision, machine learning, and image processing applications. OpenCV was originally written in C++ that comprises over 2500 optimized algorithms. Being one of the widely used libraries, OpenCV allows you to implement computer vision applications like video processing, image recognition, object detection, motion tracking, and much more. It is a cross-platform package that supports various programming languages, including Python, Java, and C++.

Since OpenCV is an open-source and cross-platform library, it can be used across many operating systems, including Windows, macOS, and Linux. OpenCV can run even on Android and iOS, enabling users to build computer vision-based mobile applications. Because of its high-end features and functionalities, most prominent companies like Google, Microsoft, Honda, and Toyota use OpenCV to build models for real-time computer vision applications.

OpenCV is perfectly documented, incorporating respective codes for various methods and functions which is easy to understand, and beginner-friendly.

PyTorch

Developed and released by Facebook’s AI research group in 2016, PyTorch is one of the most popular open-source Python data science libraries. PyTorch mainly focuses on deep learning applications, including image classification, handwriting recognition, and time series forecasting. Being highly compatible with the Python programming style, PyTorch is one of the go-to Python data science libraries for implementing complex neural network use cases. Developers use PyTorch for designing complex and high-end deep learning models because of its fast and flexible experimentation feature.

PyTorch has a distributed training feature that allows developers to distribute the computational tasks among multiple CPUs or GPUs, enabling parallel processing to boost productivity. PyTorch also has a large community of researchers and ML developers who regularly build new tools and libraries to extend the functionality of PyTorch. In addition, some of the most popular companies like Microsoft, Disney, and OpenAI use PyTorch for scaling and optimizing their AI systems.

PyTorch offers clear and perfect documentation for users to easily understand its features and functionalities along with its use cases.

Sci-Kit Learn

SciKit-Learn, also called sklearn, is one of the most popular Python data science libraries that comprises a variety of supervised and unsupervised algorithms for building machine learning models. With the SciKit-Learn library, you can access various algorithms for machine learning use cases, including regression, classification, and clustering.

SciKit-Learn has a rich set of functions and modules that allow you to seamlessly perform all machine learning-related tasks, from loading the dataset to model building to evaluating the metrics. With this Python library, users can perform machine learning operations with minimum code adjustments instead of writing a complex algorithm from scratch.

SciKit-Learn is perfectly documented and has a vast research community where individuals can contribute their newly developed algorithms.

Seaborn

Seaborn is one of the most popular Python libraries for data visualization and exploratory data analysis. With Seaborn, you can create high-level and attractive statistical plots in different styles and colors. In other words, using Seaborn, based on your data, you can create aesthetic and informative plots, including scatterplot, lineplot, and displot. Many other Python data science libraries are used for data visualization and exploration, but Seaborn is widely used among data analysts and data scientists because of its unique features.

Seaborn eases the process of data visualization where you just need to pass your dataset into the seaborn function to instantly get insights into your dataset. With its high-level interfaces and customizable themes, Seaborn allows you to easily customize the plots and charts according to your likings and use cases.

Seaborn is so perfectly documented that even beginners can quickly learn and start implementing data visualizations in Python.

Pandas

Pandas is one of the most straightforward yet powerful Python data science libraries for performing data analysis. It is also one of the most popular open-source libraries for implementing data manipulation and wrangling operations. Pandas provides an easy syntax for performing all data-related analytics operations, making it easier to manipulate and understand data. In other words, you can not only manipulate data but also load, clean, prepare, merge, join, reshape data, and much more.

With Pandas, you can perform data-related operations for data that are represented in the 2D tabular format i.e., rows and columns. Such two dimensional tables consisting of rows and columns are called a dataframe. Since the dataset is represented in a dataframe format, it is easier to fetch and manipulate the data according to your use cases. With Pandas, you can read data from any file format like text, CSV, JSON, and xlsx to easily convert it into dataframe format for further data analysis operations.

Pandas is perfectly documented that covers all its features and functionalities, making it easier for beginners to quickly learn and start implementing data operations.

Numpy

Numpy stands for Numerical Python, which allows you to perform logical and mathematical operations on arrays. In other words, Numpy is an open-source Python library that consists of multi-dimensional array objects and a set of routines like mathematical, logical, statistical operations for performing fast operations on arrays.

Since Numpy comprises pre-defined and high-level mathematical functions, you can quickly solve complex math problems without writing a single line of code. Additionally, when combined with Scipy, a scientific library, and Matplotlib, a visualization library, Numpy can effectively replace MatLab, a technical computing software.

Numpy has excellent documentation that incorporates all its functions and methodologies, making it easy for beginners to understand and implement complex math operations.

NLTK

NLTK stands for Natural Language ToolKit, which is an open-source Python library that allows performing Natural Language Processing (NLP) operations on human language data. In other words, NLTK is a suite that contains libraries and programs for performing language processing operations on text data, including tokenization, stemming, and lemmatization.

With NLTK, you can also perform visualizations and graphical demonstrations on text data, making it easier to understand and analyze the patterns behind texts and sentences. Furthermore, NLTK is a community-driven project that helps all AI enthusiasts, including linguistics engineers, ML engineers, and researchers.

NLTK offers you standard documentation that incorporates all the functions and methods for implementing all NLP-related operations.

Beautiful Soup

Beautiful Soup is one of the open-source Python data science libraries for performing web scraping operations using Python. In other words, this library is used to pull required data out of HTML and XML files. Web scraping is a phenomenon of collecting data from the internet by using different frameworks and tools.

With Beautiful Soup, you can seamlessly extract all data or filter only specific elements of interest from the respective website. After collecting data from a website, you can store those in specific formats like CSV or text according to your likings. Such files can then be loaded and converted into data frames using Python to perform any data-related operations.

Beautiful Soup is perfectly documented, incorporating all the web scraping syntaxes, methodologies, and functions.

MeitY’s Data Policy unlocks Government Data for all

By

Dipayan Mitra

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February 23, 2022

India’s Ministry of Electronics and Information Technology (MeitY) recently published a data policy draft for public consultation that makes government data available for all citizens.

The newly published draft ‘India Data Accessibility and Use Policy 2022’ mentions that except for a few exclusions, all data collected, generated, and retained by government ministries and departments will be accessible and shareable, and all government agencies must adhere to these new standards.

India Data Accessibility and Use Policy 2022 mentions, “This policy will be applicable to all data and information /created/generated/collected/archived by the Government of India directly or through authorized agencies by various ministries/departments/organizations/agencies and autonomous bodies.”

A new regulatory body named Indian Data Council and an agency, the Indian Data Office, will be formed to ensure that the new norms are being followed in the country. The Indian government mentioned that IDC would be made up of IDO and data officers from five different government ministries.

In contrast, IDO will be set up by MeitY to streamline and consolidate data access and sharing across the government and other stakeholders. With this new data policy, the government aims to drastically improve India’s ability to use public-sector data for large-scale social change.

Experts believe that it is crucial for the country to use gathered public data effectively and efficiently to make India a trillion-dollar economy in the coming years.

This new policy will considerably help startups and other organizations harness the power of quality data to bring innovations and provide better services through data licensing, sharing, and valuation within the frameworks of data security and privacy.

Additionally, the draft clearly states that government bodies must define the data retention period for transparency. “A broad set of guidelines would be standardized and provided to help ministries and departments define their data retention policy,” the draft mentioned.

However, the policy has also been subjected to criticism for multiple reasons, one of which is because of the monetization model. Salman Waris from TechLegis, said, “This policy may also see a big pushback from big tech firms as their business models are based on monetizing this kind of large-scale data.”

China develops new Quantum Computing Programming Software isQ-Core

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Preetipadma K

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February 22, 2022

china Quantum Computing Programming Software isQ-Core

The Institute of Software of the Chinese Academy of Sciences has unveiled a new quantum computing programming software named isQ-Core. Researchers have announced that isQ-Core has been implemented on a CAS quantum computing cloud platform, which is currently China’s largest in terms of hardware scale.

According to its principal developer, the Chinese Academy of Sciences’ Institute of Software, it represents a significant step forward in the merging of home-grown quantum computing hardware and software. The isQ-Core, as per the institution, offers simplicity, ease-of-use, great efficiency, solid scalability, and high reliability. In an official statement released by the institute on Thursday, it will help scientists perform quantum computing theory and applied research.

With the recent debuts of “Jiuzhang,” “Zuchongzhi,” and “Zuchongzhi 2,” China is on an accelerating course to dominate the quantum computing industry. Quantum computers, like conventional computers, require software to manage hardware devices, run applications, and provide a user interface. However, due to the fundamental difference between quantum software and classical software, the corresponding quantum software tools are more complex and difficult to design.

The Chinese Academy of Sciences’ Institute of Software previously developed the isQ platform, which contains a number of tools such as quantum programming, compilation, simulation, analysis, and verification. The platform’s primary purpose is to compile a high-level source language into a low-level intermediate representation (IR). Its functions are divided into four sections: the compiler, simulator, model verification tool, and theorem prover.

The isQ platform’s compiler can turn a quantum program written in a high-level language into an instruction set language, which can then be passed on to follow-up tools like simulators and model checking tools for processing. The simulator can replicate the execution of quantum programs on a conventional computer and display the results, which is useful in the early stages of quantum program design and testing. Model-checking tools can be used to investigate a variety of quantum system features. The theorem prover implements the quantum Hoare logic proposed by the team. It is currently the only platform in the world capable of validating the accuracy of a quantum program.

The Institute of Software of the Chinese Academy of Sciences believes both isQ-Core and isQ compiler software tool can lead to fruitful and new developments in the quantum computing industry.

Fractal Analytics to go Public, targets $2.5 billion Valuation

By

Dipayan Mitra

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February 22, 2022

India-based artificial intelligence company Fractal Analytics is all set to go public via an initial public offering by the second half of this year.

According to company officials, Fractal Analytics is expecting to hit a valuation of $2.5 billion. People familiar with the situation mentioned that the firm aims to dilute 15-20% of its stock in an IPO that might be a combination of primary and secondary offerings.

The ongoing COVID-19 pandemic has changed the working system of many companies as now organizations are shifting to cloud-based solutions, which has helped data service providers a massive boost in their growth, including Fractal Analytics.

Economic Times reported that Fractal Analytics had hired JP Morgan, Morgan Stanley, and Kotak Mahindra Capital to manage the fundraising for the IPO.

Earlier this year, Fractal Analytics became the second Indian company to achieve unicorn status in 2022 after receiving $360 million funding from TPG Capital Asia. According to the company, the transaction of the funds is planned to close by the first quarter of this year.

Indian artificial intelligence company Fractal Analytics was founded by Pranay Agrawal and Srikanth Velamakanni in 2000. The enterprise specializes in developing multiple artificial intelligence-powered products like Qure.ai and Crux Intelligence that help businesses make better strategic decisions.

Fractal now employs over 35,000 people worldwide and has operations in 16 countries, including the United States, Singapore, and Australia. Recently Fractal also acquired data analytics services providing firm Neal Analytics to further increase its cloud AI offerings.

“They have built a great client-centric, people-oriented culture and have an impressive track record of solving and scaling AI engineering challenges, especially on the Microsoft platform, for marquee clients,” said Co-founder and group chief executive of Fractal, Srikanth Velamakanni, regarding the acquisition.

Steel Authority of India Wins AI World Awards in Manufacturing category

By

Dipayan Mitra

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February 22, 2022

Steel Authority of India AI World Awards

The Steel Authority of India (SAIL) wins the AI World award for its outstanding performance in the manufacturing category.

AI World Awards is an Indian platform that honors AI solutions, AI design, software, new product development, research, education, and service providers from a variety of industries and specialties.

In the presence of top industry pioneers with AI expertise, the distinguished award was bestowed to Sanjeev Kumar, Chief General Manager-in-Charge, SAIL- IISCO Steel Plant. Amarendu Prakash, Director-in-charge, congratulated Sanjeev Kumar and his entire team for achieving this milestone.

Apart from manufacturing, the AI World Awards also included multiple other sectors, including agriculture, healthcare, retail, media, finance, legal, gaming, aerospace, automobile, and several more.

The AI World Awards is a platform that brings together AI service providers, software designers, software engineers, and end-users to celebrate their achievements. It is a purposeful attempt to bring attention to the work that individuals and businesses are doing with artificial intelligence technology.

The Steel Authority of India was founded in 1954, and is currently the leading steel manufacturing company in the country.

The company not only produces steel for domestic usage but also for large-scale sectors like railway, power, automotive, defense, and more. Additionally, SAIL also is one of the most prominent steel exporters in the country.
SAIL has stated in a post that the company had one of its best physical performances in the quarter and nine months ending December 31, 2021. According to the company, its net profit had grown by 12% in the third quarter of 2021, making the net profit more than Rs. 9500 crore.

Meta’s Social VR platform Horizon hits 300,000 Users

By

Dipayan Mitra

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February 22, 2022

Meta, formerly known as Facebook, announces that its social virtual reality (VR) platform named Horizon has crossed 300,000 users.

It’s time. 10,000 worlds have already been created. Drop in and play, build or just hang out. The possibilities are endless. pic.twitter.com/VWc83PkuDV
— Horizon Worlds (@HorizonWorlds) February 16, 2022

The platform has witnessed a ten-fold growth since its launch in December last year. Horizon Worlds and Horizon Venues are distinct platforms for attending live events in VR that use the same avatars and fundamental concepts.

Meta earlier launched Horizon exclusively for Oculus Quest VR headset users in the United States and Canada. Horizon Worlds said in a tweet that 10,000 unique worlds had been created since the launch of the platform.

In a recent virtual conference, Meta CEO Mark Zuckerberg referred to users as “Metamates” and described Horizon World as the heart of their Metaverse vision.

Meta mentioned, “Rolling out avatars across our platforms is an early step towards making this a reality. We hope your new virtual self enables you to be represented online the way you want – whether that’s to friends and family, your local community, or beyond.”

Recently, Meta also launched a Personal Boundary feature for its Horizon World and Horizon Venues platforms for preventing virtual avatars from approaching one another within a certain distance, giving people more personal space and making it easier for users to avoid unwanted interactions.

Meta’s metaverse concept includes VR and Quest, but it also envisions the metaverse as an interconnected digital environment that includes VR, AR, and more traditional platforms, including smartphones and PCs.

According to Mark Zuckerberg, Meta is planning to launch a mobile version of the platform to make it more accessible to users and also provide an immersive viewing experience beyond virtual reality.

Horizon Worlds has not yet started generating revenue for Meta, but the development and increasing popularity of the platform is a positive development for the company.

Shield AI to work on Swarming Drones for Air Force

By

Dipayan Mitra

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February 22, 2022

Defense-related artificial intelligence-powered products developing company Shield AI announces that it will develop swarming drones and autonomous robocrafts for the United States air force.

Experts believe that artificial intelligence will play a significant role in modern warfare, and Shield AI’s unnamed robocrafts will help the US air force in gaining strategic advantage over its enemies and also make better data-driven decisions.

Late last month, Shield AI revealed that it had acquired a $60 million Air Force contract for several initiatives employing its Hivemind autonomy stack, a common AI framework the business is creating to operate everything from small drones to large aircraft.

According to the plans, the company plans to integrate Hivemind with its in-house-built multiple V-Bat drones. Post integration, Shield AI expects to receive clearance to put Hivemind on Beta Technologies’ ALIA electric vertical takeoff and landing (EVTOL) aircraft.

Co-founder of Shield AI, Brandon Tseng, said, “Instead of having a team of peewee football players, let’s build a single professional athlete and then multiply that professional athlete across the team and have them work together.”

Recently, Shield AI also cracked a deal with VSK Tacticals for the purchase of its V-BAT 128 unmanned aircraft. On 21st January, the deal was signed at the V-BAT manufacturing site in Dallas, Texas.

San Diego-based artificial intelligence company Shield AI was founded by Andrew Reiter, Brandon Tseng, and Ryan Tseng in 2015. The firm specializes in developing solutions and products that cater to national security needs.

To date, Shield AI has raised more than $348 million from investors like Breyer Capital, Point72 Ventures, SVB Capital, Disruptive, and many others over eight funding rounds.
Last year, Shield AI acquired Martin UAV through a definitive agreement on 28th July 2021 with the intent of integrating Martin UAV’s platform with Shield AI’s Hivemind to develop airplanes capable of performing vertical landing and takeoffs for the US Army and air force.

KPMG and Blue J unveil a suite of AI tax analysis tools

By

Preetipadma K

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February 22, 2022

KPMG and Blue J unveil a suite of AI tax analysis tools

In collaboration with Blue J, the leading provider of predictive analysis tools, KPMG has created a set of AI tax analysis tools for the UK market. KPMG, which is one of the Big Four in the accounting industry, aims to enable its tax team by utilizing this AI technology to forecast tax scenario outcomes with 90%+ accuracy, reducing the time spent looking for and analyzing tax legislation and case law. The tool intends to speed up technical analysis to enhance decision-making on complicated tax issues for organizations dealing with the rising pace of legislative change.

The tool, according to KPMG, gives a structured approach to checking tax laws and case law, which was previously done by hand. This enables clients to quickly demonstrate good tax practice and offers documentary evidence of the tax analysis.

KPMG UK Chief Technology Officer, Tax & Legal, Stuart Tait said, “Our tax teams are dedicated to providing the best service to our clients, which is why we are passionate about investing in advanced technologies like those developed by Blue J to improve the speed and accuracy of our advice.” The company is ecstatic to be the UK’s first Blue J adopters and can’t wait to put this incredible technology to use. By implementing a better tech-enabled structure to show their high quality of work, the tool will allow KPMG to be entirely transparent and trusted.

The tool will also aid in the training of junior colleagues following the implementation of hybrid working, according to the company, because juniors may not have as much direct access to senior staff for coaching. This tool will assist them in navigating complicated issues and educating them on tax research, allowing them to advance fast in their careers.

RenewBuy acquires Artificial Intelligence startup Artivatic.ai

By

Dipayan Mitra

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February 22, 2022

Gurgaon-based online insurance providing company RenewBuy announces that it plans to acquire artificial intelligence-powered fintech startup Artivatic.ai.

The new acquisition will considerably help RenewBuy to expand its capabilities and provide a better experience to its customers in multiple areas, including claim settlements, risk assessments, and many others.

According to company officials, the acquisition deal was completed through a combination of cash and share-swap in which Artivatic.ai was valued at $10 million.

Artivatic.ai’s solutions for insurance, IP, and product portfolio will be accessible to RenewBuy to strengthen technological abilities and features, increasing the platform’s versatility and effectiveness.

CEO of RenewBuy, Balachander Sekhar, said, “This acquisition has created a platform where our agents could do business anywhere, anytime, making the physical branch redundant.” He further added that with the combined tech capabilities, they hope to create more and more tech-led solutions for insurance customers.

Bangalore-based artificial intelligence solutions providing startup Artivatic.ai was founded by Layak Singh in January 2018. The firm specializes in providing solutions for complex problems in the Insurance sector for on-boarding, fraud, risk detecting, underwriting, and claims.

An advantage of Artivatic.ai’s platform is that it comes with out-of-the-box support for multiple third-party integrations, including Azure, Hubspot, Salesforce, Google Cloud, Facebook, and more.

To date, the company has received funding of nearly $1.8 million from investors like SenseAI Ventures, KFintech, Scale Ventures, Indian Angel Network, and others over six funding rounds.

“We are looking at a massive business scale up from this acquisition. Partnering with RenewBuy will overnight give us the exposure of reaching out to three million consumers and help deliver cutting-edge product solutions,” said founder and CEO of Artivatic.ai, Kayak Singh.

H2O.ai launches Deep Learning Training Engine H2O Hydrogen Torch

By

Dipayan Mitra

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February 22, 2022

H2O.ai Deep Learning Training Engine H2O Hydrogen Torch

Open-source machine learning platform H2O.ai announces the launch of its new deep learning training engine named H2O Hydrogen Torch.

The unique training engine allows companies of all sizes in any industry to make impeccable images, videos, and natural language processing (NLP) models. According to the company, its new no-code engine was built by the world’s best data scientists.

An advantage of the H2O Hydrogen Torch is that users with no prior knowledge of coding can effortlessly use the platform to build Ml models and more. Traditional methods to build ML models are pretty complex, and not everyone can build one. H2O.ai’s new technology makes the process simpler than ever.

Founder and CEO of H2O.ai, Sri Ambati, said “Accelerated by COVID-19, video streams, speech, audio podcasts, email, and natural language text have become the fastest-growing data for our customers in every industry. Transforming and fine-tuning pre-built deep learning models to deliver high accuracy requires a no-code AI Engine to democratize AI for these use cases.”

H2O Hydrogen Torch allows data scientists and developers to quickly create models for various image, video, and NLP processing use cases, such as detecting or classifying objects, analyzing sentiment, or discovering relevant information in text using a no-code user interface.

The United States-based artificial intelligence company H2O.ai was founded by Cliff Click and Sri Satish Ambati in 2012. The firm specializes in providing solutions to solve complex business problems and accelerate the discovery of new ideas.

To date, the company has raised more than $251 million over eight funding rounds from investors like Commonwealth Bank of Australia, Goldman Sachs, Celesta Capital, Crane Ventures Partner, and many others.

“With H2O Hydrogen Torch as a core AI Engine of the H2O AI Cloud, our customers can train models in deep learning and better serve their customers and challenge tech giants,” said Ambati.

Interested users can visit the official website of H2O.ai to enjoy a free trial of H2O Hydrogen Torch.