IBM has announced an expansion of its embeddable AI software portfolio with the launch of three new libraries created to help IBM Ecosystem partners, developers, and clients more easily, quickly, and cost-effectively build their AI-powered solutions and bring them to market.
Now generally available, the AI libraries were designed in IBM Research and developed to provide Independent Software Vendors (ISVs) throughout industries an easily scalable method to build natural language processing, speech-to-text, and text-to-speech capabilities into applications across any hybrid, multi-cloud environment.
The expanded portfolio enables access to the AI libraries that power famous IBM Watson products. It is designed to assist lower the barrier for AI adoption by helping clients and partners address the skills shortage and development costs needed to build machine learning models from scratch. IT and developer teams also have the liberty to embed the new Watson libraries they prefer into their applications to help create customized products without data science expertise.
With the three new software libraries, developers can access AI capabilities. They can also choose the specific functionality, e.g., natural language processing, that they want to embed in various parts of an application. The libraries include innovations by IBM Research as well as open-source technology and are designed to decrease the time and resources taken by a developer to add powerful AI to an application.
The release is built on the existing portfolio of embeddable AI products of IBM, which includes industry-leading products such as IBM Watson Discovery, IBM Maximo Visual Inspection, IBM Instana Observability, IBM Watson APIs, and IBM Watson Assistant. With IBM’s embeddable AI portfolio, CXOs and other IT decision-makers can utilize AI to assess business insights and build enhanced end-user experiences.
Capgemini plans to acquire Quantmetry as it enters a share purchase agreement with the company and enhances its data transformation capabilities in France. As per the agreement, Quantmetry will aid Capgemini in embracing intelligent industries and businesses with technological transformations.
Quantmetry is an independent AI consulting firm specializing in mathematical modeling and developing technological solutions. Within a decade of being founded in 2011 in Paris, the company has built a global reputation in the retail, consumer goods, energy, and manufacturing sectors.
The acquisition of Quantmetry aims to strengthen Capgemini Invent’s value realization, digital transformation, and capacity enhancement in France. Capgemini Invent is the group’s digital innovation and transformation segment that focuses on curating technology-driven consumer experiences.
Capgemini is looking forward to becoming an industry leader in data and AI consulting with expertise from Quantmetry.
Quantetry’s CEO and founder, Jeremy Harroch, said on the agreement, “Our consultants, engineers and researchers will be able to put our R&D and machine learning expertise at the center of an ecosystem of excellence.”
With the generation of an enormous amount of data regularly, analyzing and interpreting data is the need of the hour. Data science and data analytics fields are blooming and are expected to have exploding employment in the next coming years. According to Forbes, after reading the report ‘Gartner Top 10 Data and Analytics Trends for 2020‘, they suggest paying attention to three main trends in the industry: becoming a data analyst or scientist, automated decision making using AI, and data marketplaces and exchanges. The employment growth in data analytics results from companies’ demand and high-paying job profiles. Although there is tough competition around the job title, many opt to become data analysts for the thrill of data-driven processes and enthusiasm for data.
Critical requirements for Data Analysts
The minimum education qualification for data analysts is graduation or post-graduation in science with at least mathematics or statistics as a subject. It is a plus to have programming and business or finance knowledge. The basic skills required for the job include knowledge of programming, familiarity with data analysis and data visualization tools, and an understanding of statistics and machine learning algorithms.
Responsibilities of Data Analysts
Data analysts seek insight into data for making data-driven decisions in the company. The key responsibilities are:
Provide reports on the analysis of data using statistical methods.
Identifying, analyzing, and interpreting data patterns and trends in datasets.
Collecting, processing, and maintaining datasets and data systems.
Working side-by-side with the management sector to prioritize business needs.
Designing new processes for improving data consumption and extraction.
The data analytics interview questions can vary from company to company as the job profile of data analysts varies greatly. Although there is a specific need for data analyst jobs, the general subjects to keep in mind for a data analytics interview questions are programming in Python or R and SQL, statistics, machine learning, and tools like Excel, Power BI, and Tableau. Here is a list of data analyst interview questions organized according to the career levels of a data analyst. The list consists of data analyst interview questions and answers for preparation.
1. What are the characteristics of a good data model?
A good data model has four characteristics:
Easy consumption of data: The data in a good data model should be clean, transparent, comprehendible, and reflect insights into the data.
Scaling of data: A good data model should be capable of scaling in proportions when a change occurs in data.
Predictable performance: A good data model should have room for performance improvements to get an accurate and precise estimate of the outcomes.
Adaptive and responsive: As growing businesses demand changes from time to time, a good data model should be adaptable and responsive to integrate the changes in the model and data.
2. Define overfitting and underfitting.
Overfitting and underfitting are modeling errors for which models fail to make accurate predictions. In overfitting, the model is fitted too well to the training data, as a result, the model produces accurate output on training data but is not able to make accurate predictions for new test data. On the contrary, in underfitting, the model is poorly fitted to the training data and is not able to capture enough trends or underlying patterns in the dataset to make predictions.
3. What is data cleansing?
Data cleansing or cleaning, or wrangling, is a process of identifying and modifying incorrect, incomplete, inaccurate or missing data. This process is important to ensure the data handled is correct and usable and that it won’t provide any further errors. There are five primary issues under data cleansing: dealing with missing data, duplicate data, structural errors, outliers, and multi-sourced data. Also, each issue can be solved with a different method, like deleting or updating missing data and fixing structural errors by thoroughly analyzing the dataset, and so on.
4. Define data visualization and its types.
Data visualization is the process of representing data graphically to reflect the important information it contains. With visualization, the understanding and analysis of data are easier and more efficient. Many types of data visualization techniques include diagrams, graphs, charts, and dashboards.
5. Differentiate between variance and covariance.
The statistical definition of variance is the deviation or spread of data set from its mean value, and covariance is the measure of how two random variables are related in a dataset. The main difference between variance and covariance is variance talks about the overall dataset, including all data points, and covariance focuses on two randomly chosen variables in the dataset.
6. Which Python libraries are used for data analytics?
The primary Python libraries used for data analytics are Pandas, NumPy, Matplotlib, and Seaborn. Pandas and NumPy are used for mathematical or statistical computations in the data frame, including describing, summarizing, computing means and standard deviations, updating/deleting rows and columns, and so on. And, Matplotlib and Seaborn are used for data visualization, including commands for graphs and plots, representing the correlation between variables in the data frame, and more.
An outlier is a data point or value in the dataset that is far away from other recorded data points. It can indicate either variability in measurement or an experimental error. There are many ways to detect outliers, including the box plot method, the Z-score method, and so on.
8. What are the data validation techniques used in data analytics?
Data validation is the process of verifying the dataset through data cleaning and ensuring data quality. There are four main data validation techniques:
Field level validation: Data validation starts as the data enters the field, and errors can be fixed under ongoing processing of model building.
Form level validation: It is a user-based validation performed while collecting the data. The errors are highlighted as users submit the data and get it fixed.
Data saving validation: This validation technique is used when a file or database is saved entirely, and multiple data forms are validated at once.
Search criteria validation: The validation method is used when searching or querying the data. Validation at this stage provides users with accurate and relevant results.
9. Differentiate between the WHERE clause and HAVING clause in SQL.
The WHERE clause operates on row data, and the filter occurs before any groupings are made. In contrast, the HAVING clause operates on aggregated data and filters values from a group.
The syntax of the WHERE clause is:
SELECT column_name(s)
FROM table_name
WHERE condition
The syntax of the HAVING clause is:
SELECT column_name(s)
FROM table_name
WHERE condition
GROUP_BY column_name(s)
HAVING condition
ORDER BY column_name(s)
10. Define a Pivot table in Excel.
A Pivot table in Excel is a way of summarizing large amounts of data. It brings together information from various locations in a workbook and presents it on a table. It is helpful to present data findings and analyze numerical data in detail, which helps query large amounts of data.
Experienced level
11. What is time series analysis and time series forecasting?
Time series analysis is the technique to learn new information from time series data by analyzing them using different statistical methods. Four primary variations are seen in time series analysis: seasonal, trend, cyclical, and random. Time series forecasting can be considered to be based on time series analysis, but in forecasting, the focus is on building a model for predicting future values from previously stored data.
12. Define collaborative filtering.
Collaborative filtering is a popular technique used in recommender systems where models provide automatic predictions or filter users’ interests based on past choices. The three major components of collaborative filtering are users, items, and interests. This method is based on user behavioral data, assuming that people who agree on particular items will likely agree again in the future.
13. What is Hypothesis testing, and name a few forms of hypothesis tests?
Hypothesis testing is a statistical technique to determine the significance of a finding or statement. Two mutually exclusive statements are considered on a population or sample dataset, and this method decides which statement best reflects or is relevant to the sample dataset. There are many forms of hypothesis tests, including p-test, t-test, chi-square test, ANOVA test, and more. These tests have different criteria for considering a statement to be more relevant to the sample data like t-tests computes the difference between the means of a pair of groups, ANOVA compares more than two pair of groups, and so on.
14. Explain clustering and name properties and types of clustering.
Clustering is the process of classifying data points into clusters or groups using a clustering algorithm. It helps to identify similarities or similar properties between data points, which can be hierarchical or flat, hard or soft, iterative and disjunctive. The types of clustering are based on the similarities in data points and have four basic types: centroid-based clustering, density-based clustering, distribution-based clustering, and hierarchical clustering.
15. State the benefits of using version control.
Version control or source control is a mechanism to configure the software so that the changes to the software code can be tracked and managed. There are five benefits of using version control:
The process of software development becomes clear and transparent.
It helps to distinguish between different document versions so that the latest version can be used.
With version control, the storing and maintenance of multiple variants of code files is easy.
Analysis of changes to a dataset or code file can be reviewed quickly.
It provides security and can help revive the project in case of failure of the central server.
As mentioned earlier, the interview questions on data analytics may vary according to the company’s needs. There can be more in-depth questions on Python libraries, Excel, SQL querying, and data visualization tools. This list is an overview of data analyst interview questions that a candidate must know. Prepare for the data analytics interview as per your interests and goal. All the very best!
The Norwegian government has taken steps to embrace Web3 by establishing a metaverse tax office.
Norway’s central register, The Brønnøysund, and the nation’s tax authority, Skatteetaten, announced that they’re collaborating with consulting firm Ernst and Young (EY) to open an office in Decentraland. The announcement came at the Nokios conference on Wednesday. According to Nokios, the initiative aims to deliver services to tech-native individuals while establishing their Web3 footprint.
Magnus Jones, Nordic blockchain lead at EY, said that he hopes this partnership will spearhead education in the crypto space by educating users about taxes related to non-fungible tokens (NFT) and decentralized finance (DeFi).
The Brønnøysund is also exploring several other Web3 services, such as wallets, smart contracts, decentralized autonomous organizations (DAO), and many more.
Apart from the metaverse, Norway has been slowly integrating crypto services nationally. In June, the government suggested using the Ethereum scaling service Arbitrum to release capitalization tables platforms for unlisted companies. In September, Norway, Israel, and Sweden joined hands with the Bank for International Settlements to assess the possibility of introducing a central bank digital currency (CBDC) for cross-border payments.
As the Scandinavian nation delves deeper into crypto, other countries are also integrating Web3 tools nationally. In July, a policy briefing by the Shanghai city government said it plans to bolster its metaverse industry to $52 billion by 2025. And earlier this month, Japan’s prime minister said the country would incorporate the metaverse and NFTs in its plans for digital transformation.
While many industries were affected by the pandemic and suffered losses, the software applications and information technology industry was at its peak and kept growing. Today, the trends like artificial intelligence, machine learning, the internet of things, cloud computing, and many others swamped the software development market and created a huge impact globally. In the production of software applications, also known as the software development life cycle (SDLC), software testing is one of the most important steps. Software testing is a diverse task to find defects or errors in software. It is a process of examining a software’s performance, behavior, and value under test by validation and verification.
There are various types of software testing, and each type has its own features, advantages, and disadvantages. And based on the requirement, a tester selects the type of software testing to be used. The types of software testing are a long list and have more than 20 types of testing. To make it simpler, the types of software testing can be divided into two parts, manual and automation testing. The manual testing takes the box approach of testing, including white, black, and grey box testing, and further, the black box includes functional and non-functional testing.
There are various software testing methods, and traditionally the in box approach is divided into three types, white-box, black-box, and grey-box testing. The white-box and black-box testing take the approach of describing a tester’s point of view when designing the test cases. And grey-box testing is a hybrid approach that develops tests from specific design elements.
White-box Testing
White-box testing is inspecting every line of code before the tests even start. It verifies the internal structures or working of a program as opposed to the functionality and is also known as clear box testing, glass box testing, transparent box testing, or structural testing. The source code and programming skills are used in white-box testing to design test cases. These test cases involve verifying the product’s underlying structure, architecture, and code to validate input-output flow. Generally, white-box testing is applied at the unit level but can also be applied to integration and system levels of software testing types.
Black-box testing (aka functional testing) is a manual testing technique where testers in software engineering analyze the requirements of the software, look for defects or bugs, and decide to send it back to the development level for rectification. In this approach, the software is treated as a black box, and the examination is done without any knowledge of the source code. The testing includes methods like equivalence partitioning, boundary value analysis, decision table testing, fuzz testing, and use case testing. Black-box testing is categorized into functional and non-functional testing, which is then divided into different types of software testing that are discussed below. Additionally, black-box testing can be applied to all levels of software testing, including unit, integration, system, and acceptance.
Grey-box Testing
Grey-box testing is a type of testing in software engineering that tests the software or application with partial knowledge of the internal structure of the software. The testing uses reverse engineering to determine the errors. The goal of grey-box testing is to find and identify defects resulting from improper code structure and irregular use of the software. Grey-box testing determines intelligent test scenarios from the provided software’s limited information that are applied to data type handling, exception handling, and more.
List of Types of Software Testing
Here the list consists of the main levels of software testing after functional and non-functional software testing. The first four types, unit, integration, system, and acceptance testing, come under functional testing. And the rest types, security, performance, usability, and compatibility, are under non-functional testing.
1. Unit Testing
Unit testing is the first level of functional testing in software testing, performed on an individual unit or component to test for correction. It is called unit testing because the tester examines the software module independently or tests all the module functionality. Testers often use test automation tools like NUnit, Xunit, and JUnit to execute unit testing, and each unit is viewed as a method, function, procedure, or object. The objective is to validate the performance of unit components. Unit testing is a curial part of types of testing in SDLC as most defects can be identified at the unit test level.
2. Integration Testing
Among software testing types, interaction testing is where two or more modules of an application or software are logically grouped and tested altogether. This testing is the second level of functional testing that focuses on the defect of an interface, communication, and data flow between modules. The objective of the testing is to test the statement’s accuracy between each module. Integrating testing is further divided into two parts, incremental and non-incremental, comprising four different types of software testing: top-down, bottom-up, sandwich, and big-bang testing. Top-down and bottom-up incremental integration testing works by adding the modules incrementally at each step and then test the data flow between the modules. And the difference between the two is that the modules added in the top-down must be the child of the earlier module, and in the bottom-down, the module must be the parent of the earlier one. When the data flow gets complex and difficult to classify a module as parent and child, non-incremental integration is applied.
System testing is the third level of functional testing, also known as end-to-end testing, in which the test cases are operated while the test environment and production environment are parallel. In system testing, each attribute of the software goes under test for the working of end features based on the business requirements, and then the software product is analyzed as a complete system. Various software testing types are performed under system testing, including end-to-end, smoke, sanity, monkey, and so on.
4. Acceptance Testing
Acceptance testing (aka user acceptance testing) is a type of testing in software engineering where the client or business tests the software with real-time business scenarios. This is the fourth and final level of functional testing, after clearing the test, the software goes into production. The testing is a quality assurance process determining the degree of success of meeting the clients’ requirements and getting thier approval. Several methods can be used in acceptance testing, such as Alpha testing, Beta testing, and operational acceptance testing (OAT).
5. Security Testing
Security testing is a non-functional type of software testing intended to reveal defects in the security mechanism of an information system protecting the data and maintaining functionality. The testing includes checking how the software or application is secure from internal and external threats and how much software is secure from malicious programs and viruses. The tests check to provide maximum security if any cyber attack happens, going on with how software behaves under a hacker attack and how secure and strong is the authorization. A few security testing methods are penetration testing, vulnerability scanning, and risk assessment.
6. Performance Testing
Performance testing tests a software’s stability and response time by applying load in the software. Testers focus on four things in performance testing: response time, load, scalability, and stability of the software. The goal of performance testing is to identify, rectify, and eliminate the performance bottlenecks in software. The testing consists of different types of testing, including load testing, stress testing, scalability testing, stability testing, volume testing, endurance testing, and spike testing, and each test serves one or more focus points. Performance testing is done with tools like Loader.IO, JMeter, LoadRunner, etc.
7. Usability Testing
Usability testing is another non-functional type of testing in software engineering which works from the users’ point of view to check the user-friendliness of the software application. A user-friendly software has two aspects: the application must be easily understood and look appealing and feel good for working on it. The purpose of usability testing is for the application to look appealing and showcase information at a glance. Some of the testing methods in usability testing are exploratory testing, cross-browser testing, and accessibility testing. Additionally, four questions help guide the usability testing process: screening questions, pre-test questions, in-test questions, and post-test questions.
8. Compatibility Testing
Compatibility testing is a non-functional type of testing that validates how software application behaves and runs in various environments, web servers, hardware, and network environments. The goal of the testing is to ensure that software is capable of working on different configurations, databases, browsers, and their versions. There are two types of compatibility testing, backward or downward and forward compatibility testing.
Google has acquired an artificial intelligence (AI) avatar startup for about $100 million to better compete with TikTok and boost its content game. Alter helps creators and brands express their virtual identities.
The source said the acquisition was completed nearly two months ago. However, neither of the companies disclosed it to the public. Notably, some of Alter’s top executives updated their LinkedIn profiles showing that they joined Google without acknowledging the acquisition. The source requested anonymity as they are sharing nonpublic information.
A Google spokesperson confirmed that the company had acquired Alter but refused to comment on the deal’s financial terms.
The US and Czech-headquartered Alter started its journey as Facemoji, which is a platform that offered plug-and-play tech to assist game and app developers in adding avatar systems into their apps. The startup secured $3 million in seed funding from investors, including Twitter, Play Ventures, and Roosh Ventures.
Facemoji later rebranded as Alter. A person familiar with the matter said Google hopes to use Alter to enhance its content offerings. Alter founders Robin Raszka, and Jon Slimak have not commented on the matter.
Elon Musk has finally closed his deal to buy the social media platform Twitter. The deal’s closure had a deadline of this Friday at 5 pm ET, after which a previously-postponed lawsuit by Twitter against Musk to get ahead with the deal would have been resumed.
First announced in April, the deal hit multiple hurdles along the way, including Musk’s reservations about the number of spam bots on Twitter. However, earlier this month, Musk proposed going ahead with the deal at the initially agreed price of $44 billion, or $54.20 per share.
During the deal, Twitter’s share price was $53.70 at market close, whereas Musk’s favored cryptocurrency, dogecoin (DOGE), was trading down 2.3% at 00:43 UTC, after increasing 16% in the lead till the deal’s completion. Musk said that dogecoin could be used for specific payments at Twitter
Musk had criticized Twitter’s workforce as “lazy and politically biased.” According to sources, Musk has told potential investors he plans to cut Twitter’s staff from about 7,500 employees to just over 2,000. Musk denied the report. There is also speculation about top executives being asked to leave Twitter.
Musk fired Twitter CEO Parag Agarwal, after the acquisition. Legal executive Vijaya Gadde, Chief Financial Officer Ned Segal, and General Counsel Sean Edgett were also fired. ‘The bird is freed’, Musk tweeted.
Potential crypto plans for Twitter remain unclear. In June, Musk discussed the logic for integrating digital payments into its service. Twitter added bitcoin tipping in 2021 under the previous CEO Jack Dorsey. The company also added ether wallets to the feature at the beginning of this year.
Twitter also became the first-ever company to use a new program from payments processor Stripe, which announced a feature allowing payments in USDC via Polygon in April. Musk’s takeover is being seen as a win for the crypto community.
SCS Tech India Private Limited is an IT and ITES company delivering IT services and solutions across the globe and currently has offices in India, Singapore, and Dubai. Mr. Sujit Patel, the CEO and managing director of SCS Tech, has a vision of creating value with innovation and going to the future with top-class products and services with the company. In 2019, The company was rewarded with the finest India skills talent (FIST) award for its ideas of planning, implementing, and operating smart solutions supporting digital transformation. To get insight and a better understanding of the story behind SCS Tech, Analytics Drift interviewed Dr. Prateik Ghosh, vice president of SCS Tech.
Dr.Prateik Ghosh, VC of SCS Tech
SCS Tech’s architecture
The company started in 2010 as a mainstream digital company focused on hardware, large IT infrastructure, and IT solutions, and now moved to software process-driven solutions with integrated hardware products. SCS Tech has expertise in various fields, including cybersecurity, IT infrastructure, digital transformation in AI and ML, smart and safe cities, and enterprise solutions. And the company works in numerous industries, including education, finance, homeland and security defence, emergency and disaster management, and many more. The objective of the company is to become one of the largest digital transformation companies by filling the gaps in the processing of IT infrastructure and software systems and uniting them on a single platform.
There are four main services SCS Tech provides that are spread over the areas of solutions, experience, connectivity, and insight. These services are:
Provide an integrated command and control center for disaster management or emergency protocols. The center provides actionable intelligence in the security sector that helps monitor, detect, prevent, and respond to threats.
Use of digital platforms such as dashboards with a certain set of tools for debriefing reports and analytics with connection to the Internet of things (IoT).
Have a dedicated department of supervision for cybersecurity, networking, and implementing security operations.
Provide an enterprise-level IT infrastructure consisting of data recovery, data center, and complete networking operations that have been the oldest pillar of the company.
Highlights on SCS Tech’s take on digital transformation
With advancements in emerging technologies, enterprise digital transformation help to improve services and also enhance customer experience. As modern digital enterprises are data-driven and demand quick and confident decisions, SCS Tech provides data-driven computations using various AI and ML processes like predictive analytics, statistical analyzing and visualizing, and more. These processes involve tasks of debriefing solutions, analytics, and dashboarding tools to perform continuous data analytics and forecasting.
The company has its own dashboard connected to IoTs for computing large datasets into a common database, bringing efficiency to the business with automation and improved productivity. The dashboard can take both structured and unstructured data collected from various sources, including social media platforms like WhatsApp, Facebook, and Instagram, and run operations to get insights. The company helps enterprises as a whole or levels of enterprises, as a single organization may need different computations at different levels on the same dataset. SCS Tech uses AI and ML tools to provide helpful insight according to the organization’s needs at all levels. As Dr. Prateik Ghosh stated, “Digital transformation takes the perspective of the people,” the company is centered on its client’s needs and tries to resolve their IT-related problems by collaborating and enhancing its systems and programs.
SCS Tech working towards one-stop solution
It gets difficult for the company to take charge of providing an engaging solution, including all four service points mentioned above, as not all companies can train large-scale data. “SCS Tech tries to give clients a ‘one-stop solution,’ where the company takes care of everything from software to hardware interfaces, provides training on how to run the systems, and if needed, handholds the client’s IT infrastructure for a few years and then hand it over,” explains Dr. Prateik. This way, SCS Tech empowers its client to build the best of both worlds with their ideas and the company’s expertise. The company commits to innovation and excellence to deliver consistent customer satisfaction.
Till now, SCS Tech has worked on large-scale projects for security centers and is running one of the largest disaster management systems for Maharastra in India. The upcoming exciting projects of SCS Tech are focused on the power sector and power generation systems, where the company is purposing to run their dashboard along with backend analytics of power sectors for disaster management systems. Here the idea of SCS Tech is to merge the power sector’s products and theirs to create an in-house interface computing necessary operations and insights.
The model created by Assistant Professor of the Department of Biosciences and Bioengineering, Dr. Souptick Chanda, and his team can assess the healing outcomes of various fracture fixation strategies, which allows an optimum strategy to be chosen for the patient depending on their physiologies and fracture type. Such precision models can reduce healing time and lighten the economic burden and pain for patients needing thigh fracture treatment.
The research team has used Finite Element Analysis and the Fuzzy Logic AI tool to comprehend the fracture’s healing process after various treatment methods. The study further analyzed the influence of various screw fixation mechanisms to study the fracture healing efficacies of each process.
IIT Guwahati’s AI-based simulation model can help surgeons choose the proper technique or implant before a fracture treatment. In addition to various patient-specific biological parameters, the model can also consider different clinical phenomena, such as smoking, diabetes, and others. The model can also be modified for veterinary fractures, which are, in various aspects, similar to human patients.
Based on the algorithm, the researchers plan to develop software or applications to use in hospitals and other healthcare institutions for fracture treatment protocols. Research done by IIT Guwahati researchers is helpful because the incidences of thigh-bone and hip fractures have increased significantly due to the increasing geriatric population in the world.
Machine learning is the subfield of artificial intelligence, which is the hot topic around the corner as it focuses on the capability of a machine to imitate human intelligence. It is an algorithm-intense field where a bunch of codes implements complex algorithms in a matter of seconds. According to the report of the state of octaverse, the most widely used coding language for machine learning is Python. Due to Python’s accessibility, user-friendliness, and immense developer community, it is best suited for machine learning algorithms. For large-scale usage of Python for machine learning algorithms, various Python libraries were built to write codes quickly. Here is the list of top Python libraries for machine learning.
Top Python libraries for machine learning
This list consists of the top 10 machine learning libraries in Python used vastly among programmers.
1. NumPy
Numpy is an open-source library that enables numerical computing in Python and is one of the most popular Python libraries for machine learning, useful for fundamental scientific computations. It was created in 2005 as an open-source project on GitHub, built on the early work of Numeric and Numarray libraries. NumPy comprises a collection of high-complexity mathematical functions which can process large multi-dimensional arrays and matrices. The library efficiently handles linear algebra, Fourier transformation, and random numbers. The main functions of NumPy are dynamic N-dimensional array objects, broadcasting functions, and special tools to integrate C or C++ and Fortran code. It lets users define arbitrary data types with a multi-dimensional container for any generic data and easily integrate them with most databases.
2. SciPy
SciPy is an open-source library based on NumPy. It is popular among Python libraries for machine learning because of its scientific and analytical computing capabilities. As SciPy is based on NumPy for its array manipulation, it also includes all NumPy functions with the addition of proficient scientific tools. SciPy was created as a resultant collective package written by Travis Oliphant, Eric Jones and Pearu Peterson in 2001 when there was an increased interest in creating a complete environment for scientific and technical computing in Python. Today, the development of SciPy is supported and sponsored by an open community of developers. In addition, the SciPy community is an institutional partner with Quansight Labs and is directly funded by Chan Zuckerberg Initiative and Tidelift. The library offers a range of modules for linear algebra, image optimization, integration interpolation, special functions, signal and image processing, ordinary differential equations solving, and more in science and analytical computing.
3. Scikit-learn
Scikit-learn or Sklearn is one of the basic Python libraries for machine learning used for classical machine learning algorithms. It is built on top of NumPy and SciPy for effective use in the development of machine learning. Scikit-learn was developed under the Scikit-learn project started by David Counapeau as a Google summer of code project in 2005. Then, in 2010 the first version on Sklearn was released by Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, and Vincent Michel of INRIA (The national Institute of Research in digital science and technology). The library has a wide range of functions supporting supervised and unsupervised learning algorithms. The main functionalities of Scikit-learn are classification, regression, clustering, model selection, preprocessing, and dimensionality reduction. In addition, Scikit-learn is used for data mining, modeling, and analysis.
TensorFlow is an open-sourced end-to-end platform and library used for high-performance numerical computation. It was first released in 2015 by the Google Brain team, and it specializes in differential programming, meaning the library can automatically compute a function’s derivatives. The library is a collection of tools and resources required to build deep learning and machine learning models. TensorFlow can be a great tool in deep learning for beginners because of its architectural and framework flexibility. The specialty of TensorFlow is its easy distribution of work onto multiple CPU or GPU cores by using Tensors. Tensors are containers that can store multi-dimensional data arrays as well as their linear operations. Although the primary function of TensorFlow is in the training and inference of deep neural networks, it can also be used for reinforcement learning and model visualization with its built-in tools.
5. Keras
Keras is an open-source software library in Python that provides an interface for deep learning. It can run on top of TensorFlow, Theano, and CNTK and was developed focusing on fast experimentation with deep neural networks. Among other machine learning libraries, Keras can work with the widest range of data types, including arrays, text, and images. Keras is simple to use, reduces the cognitive load on developers, and is flexible in adopting principles of progressive disclosure of complexity, meaning reducing complexity by introducing information and function at increment levels. Also, Keras is powerful, providing industry-strength performance, and has been used by organizations like NASA and YouTube. These three key features of simplicity, flexibility and power of Keras make it one of the best machine learning libraries in Python. Keras offers fully functional models for creating neural networks integrating objectives, layers, optimizers, and activation functions. The library has many use cases, including fast and efficient prototyping, research work, and data modeling and visualization.
6. Pandas
Pandas is a software library used for data science and analysis tasks in Python. It is built on top of the NumPy library, which provides numerical computing and specifies data extraction. Before building and training machine learning models, there is a need to prepare a dataset to clean and preprocess the data. Pandas help prepare the data with various tools for analyzing data in detail and is designed to work on relational and labeled data. The development of Pandas began in 2008 at AQR capital management by Wes McKinney, by the end of 2009, Pandas became open-sourced, and in 2015 Pandas became a NumFOCUS sponsored project. Now, Pandas is actively supported by a community of innovative developers and researchers worldwide, contributing to using the open-source Pandas library. It is one of the best Python libraries with high stability because of its backend code written in C or Python. Pandas provide high-level data structures, including two main types, one-dimensional series and two-dimensional DataFrame. Moreover, Pandas offers a variety of tools to manipulate series and DataFrames, so that users can prepare the dataset based on their needs.
7. Matplotlib
Matplotlib is a data visualization or plotting library used in Python and is built upon SciPy and NumPy used for graphical representation. It is compatible with plotting data from SciPy, NumPy, and Pandas and provides a MATLAB-like interface that is exceptionally user-friendly. In 2002, John Hunter developed Matplotlib, which was originally a patch to IPython enabling interactive MATLAB-style plotting. Matplotlib provides an object-oriented API using standard GUI toolkits like GTL+, wxPython, Tkinter, or Qt and helps developers to build graphs and plots. The library can generate different types of graphs, including histograms, bar graphs, scatter plots, image plots, and more. Although Matplotlib plotting is limited to 2D graphs, the graphs are high-quality and publish-ready.
Seaborn is an open-source Python data visualization library based on Matplotlib and integrates closet with Pandas data structures. Plotting with Seaborn is dataset-oriented, where declarative APIs are present to identify relationships between different elements and details of how to draw the graph. Seaborn also supports high-level abstractions for multi-plot grids and visualizes univariate and bivariate distributions. With data visualization, Seaborn helps explore and understand data by performing necessary semantic mapping and statistical aggregation internally to produce informative graphs. Seaborn is used in many machine learning and deep learning projects, and its visually attractive plots make it suitable for business and marketing purposes. Moreover, Seaborn can create extensive graphs and plots with simple commands and few lines of code, saving time and effort at the users’ end.
9. NLTK
NLTK (natural language toolkit) is one of the most popular Python libraries for machine learning used for natural language processing (NLP). It is a leading platform for building Python applications to work with human language and provides over 50 easy-to-use interface corpora and lexical resources for text processing. NLTK can be defined as a set of libraries combined under one toolkit for using symbolic and statistical NLP for English. Steve Bird and Edward Loper developed NLTK at the University of Pennsylvania with an initial release in 2001 and a stable release in 2021. There are various tasks like classification, tokenization, stemming, tagging, parsing, and semantic reasoning in NLP, which different text processing libraries in NLTK can perform. As NLTK processes textual data, it is suitable for linguistics, engineers, students, researchers, and industry analysts. Further, the library is used in sentiment analysis, recommendation and review models, text-classifier, text mining, and other human language-related operations in the industry.
10. OpenCV
OpenCV (open source computer vision) is an open-source computer vision and machine learning software library. It is a library dedicated to computer vision and image processing used by major camera companies to make their technology smart and user-friendly. OpenCV was built to provide a common infrastructure for computer vision applications. This library consists of more than 2500 optimized algorithms capable of processing various visual inputs like image and video data to find patterns or recognize objects, faces, and handwriting. Among other Python libraries for machine learning, OpenCV is the only library that focuses on real-time data processing for which OpenCV is used extensively in companies, research groups, and Government agencies.
There are other useful libraries for machine learning in Python, including PyTorch, PyCaret, Theano, Caffe, and more which didn’t make it to this list. However, perform efficiently and serves certain use cases in machine learning.
