The researchers at IIIT Allahabad have proposed T2CI-GAN, a novel deep learning model that generates compressed images from input text. It was developed by researchers from the Computer Vision and Biometrics Laboratory at the institute and will form a robust groundwork for future image-storing and content-sharing technologies.
Existing methods of generating images from input texts utilize GANs (generative adversarial networks) for image generation and then compress the generated images in the following step.
This novel method expands the existing ones by directly generating compressed images reducing the workload and processing time.
The researchers created two GAN-based models to generate compressed images. The first was trained using a dataset of compressed JPEG DCT (discrete cosine transform) images, and the second used a set of RGB photos. The second model was developed to enhance the production of JPEG-compressed DCT representations.
T2CI-GAN will be essential as machines need data to be read or understood in compressed forms. The model currently only produces JPEG-compressed images. Therefore, the long-term objective of the researchers is to expand it to produce photos in any compressed form without any limitations on the compression algorithm.
Researchers at Georgia Tech propose a new sampling algorithm called ‘LABOR.’ This new technique combines the traditional neighborhood and node sampling techniques while addressing dependency issues in the existing neighborhood sampling method alone.
Traditional sampling techniques use Neighborhood Explosion Phenomenon (NEP) to sample. However, this technique poses a high dependency of node embeddings (in graphical neural networks) on their neighbor’s embeddings. The NEP has the most significant effect on node-based sampling techniques.
However, it was found that node-based approaches sample subgraphs with insufficient depth. Layer-based sampling, in which sampling is done collectively for each layer, was therefore suggested.
Unlike node- and layer-based sampling methods typically sample recursive layers, subgraph sampling methods use a single subgraph for all levels. Researchers, therefore, explored sampling a subgraph of the batch’s nodes to solve the dependency issue.
But sampling subgraphs resulted in higher biases than their node and layer-based counterparts. Thus, the research ultimately focused on using a combination of these techniques in developing LABOR.
LABOR’s main contribution was using Poisson’s Sampling technique. A significant decrease in computation, memory, and communication is realized for the sampled points due to correlating the methods of layers and nodes.
Also, LABOR and neighbor sampling employs the same hyperparameters; they can be used interchangeably.
A new class of materials has been developed by engineers at UCLA in California that can adjust in real-time to dynamic external forces by learning behaviors through time and creating its own “muscle memory.” Known as mechanical neural networks (MNNs), the materials consist of a structural system made up of adjustable beams that may change the shape and behaviors of the material in response to shifting stimuli.
A mechanical neural network built from beams of variable stiffness Lee et al., Sci. Robot.
The study’s findings, which have potential in building construction, aviation, and imaging technology, were published in Science Robotics on Wednesday. According to the authors, the experimental study establishes the groundwork for AI-architected materials that can be used in the construction of buildings, aircraft, and imaging technologies.
It was in 1944 when Warren McCullough and Walter Pitts, two University of Chicago scholars who later transferred to MIT in 1952, made the first theoretical proposal for neural networks. A neural network is made up of hundreds or even millions of intricately intertwined simple processing nodes that are vaguely modeled after the human brain. The majority of neural networks used today are composed of node layers that include an input layer, one or more hidden layers, and an output layer.
The authors explained that mechanical neural networks are lattices of linked, tunable beams that unite at nodes and are propelled by input and output forces or displacements. To train the lattice so that it can learn desired mechanical behaviors (such as shape morphing, acoustic wave propagation, and mechanical computation) and bulk properties (such as Poisson’s ratio, shear, and Young’s modulus, and density), the stiffness values of the interconnected beams are optimized as network weights. This is how the new class of architected materials—also known as mechanical metamaterials—got introduced in this research. These materials learn after being exposed to unexpected ambient stress conditions over time.
Prior to this research, acoustic metamaterials, such as the acoustic analog computing (AAC) system, have been proposed by others, but since they are not neural networks, they cannot learn. In 2019, Tyler Hughes et al. suggested an acoustic metamaterial that mimics the behavior of a trained neural network. However, a fabricated version of the proposed design was unable to learn new behaviors since training is done during the design process by simulating the adjustment of the mass within a vibrating plate. While alternative mechanical concepts have also been suggested and tested over the last two years using just simulation, this is the first time the mechanical neural network concept mentioned in this UCLA study has been physically and experimentally verified.
The concept can also be expanded to complicated three-dimensional (3D) lattices that can fill arbitrary-shaped volumes and meet required fixturing requirements for practical material applications. Further, mechanical neural networks function as deep neural networks that may learn several complicated behaviors at once. This is because they generally have several layers of nodes comparable to the neurons in artificial neural networks. One major advantage of the mechanical neural network is that it has the ability to relearn previously mastered behaviors and learn new behaviors as needed with exposure to changing environmental scenarios, in case it is broken, chopped to occupy an alternative volume, or fixtured differently. This feature is not present in other neural networks.
Lee et al., Sci. Robot.
The researchers present an illustration of how the metamaterial might be applied to airplane wings. To increase efficiency and maneuverability, the mechanical neural networks may learn to change the form of the wings in response to changing wind patterns while the aircraft is in flight or incurs internal damage. A mechanical neural network-based wing might stiffen and relax its connections in response to each of these situations to maintain desirable properties like directional strength. The wing gradually adopts and maintains new qualities by iterative algorithmic changes, adding each new behavior to the rest of its repertoire in a manner akin to muscle memory. In applications involving infrastructure, where earthquakes or other natural or man-made disasters pose a concern, study authors pointed out that it is also possible that the material might aid increase stiffness and general stability.
Ryan Lee of the University of California, Los Angeles, and his colleagues created a network of 21 beams, each 15 cm long, and placed them in a triangular lattice. Each beam is equipped with a voice coil, flexures, and strain gauges. According to the researchers, these characteristics allow the beam to modify its length, adapt to its changing surroundings in real-time, and interact with other beams in the system. When forces are applied to the beam, the voice coil is employed to start a precisely tuned compression or expansion. The algorithm controls the learning behavior using information gathered by the strain gauge from the beam’s velocity. The system’s movable beams are connected to it via the flexures.
Every beam has sensors that determine how far each “neuron,” or beam joint, is out of alignment, as well as a tiny linear motor that can change the stiffness of the beam. By adjusting the beam stiffness, a computer can train the network as a result. After this is completed, the structure no longer has to be calculated externally, and the different beam stiffnesses are fixed.
The network’s response to applied forces is controlled by an optimization algorithm, which uses data from all of the strain gauges to create a combination of rigidity values. In order to verify the actions done by the strain gauge-monitored system, cameras were trained on the system’s output nodes. Although early versions had problems with the delay between input and reaction, the team worked for five years to work out the issues until the mechanical neural network material could learn and react in real-time.
Even though the system is around the size of a microwave oven, the researchers want to make the mechanical neural network design simpler so that thousands of networks could be produced on the micro-scale within 3D lattices for useful material applications.
The researchers propose that mechanical neural networks could be included in armor to deflect shockwaves or in acoustic imaging technologies to harness soundwaves in addition to being used in cars and building materials.
An autonomous vehicle startup, Argo AI, which emerged in 2017 with a $1 billion investment, is now shutting down. According to people familiar with the matter, its parts are being absorbed into its two principal backers: Ford and Volkswagen (VW).
During an all-hands meeting on Wednesday, Argo AI employees were informed that some people will receive offers from the two automakers. It is unclear how many would be hired into VW or Ford and which companies would get Argo’s technology.
Employees were told they would receive a severance package including insurance and two different bonuses — an annual award plus a transaction bonus upon the deal close with Ford and VW. All Argo employees will receive these. Those who Ford or VW does not retain will receive termination and severance pay, including health insurance.
Ford said in its third-quarter earnings report released Wednesday that it decided to shift its resources to developing advanced driver assistance systems, and not autonomous vehicle technology that can be applied to robotaxis. The company said it recorded a $2.7 billion non-cash, pretax impairment on its Argo AI investment, which resulted in an $827 million net loss for the third quarter.
That decision appears to have been fueled by Argo’s inability to attract new investors. Ford CEO Jim Farley acknowledged that the company anticipated being able to bring autonomous vehicle technology broadly to market by 2021. Ford had recently announced that it would launch self-driving cars by the end of this year in association with Agro AI.
Argo’s other primary backer, VW, has indicated plans to shift resources and will no longer invest in Argo AI. The company said it would use its software unit Cariad to drive forward the development of highly automated and autonomous driving with Bosch and, in the future, in China with Horizon Robotics.
Nubank, a Brazillian fintech company, plans to use Polygon technology to create their own crypto assets as loyalty tokens in a new rewards program. Nubank will launch the rewards program in the first half of 2023. 70 million customers will access the plan and receive NuCoins to acknowledge their engagement with the bank.
2000 patrons will be invited to discuss the development, features, and essential web3 components required for the NuCoin loyalty program. Nubank officials believe this will help refine the product ahead of the public launch and ensure people’s needs are met.
Fernando Czapski, NuCoin’s GM, said that the project was a way to develop blockchain technology and democratize the sale-purchase of cryptocurrencies via the Nu app. Sandeep Nailwal, the founder of Polygon, also acknowledged the utility of blockchain and said that the collaboration is a “strong testament” to the technology.
Nubank is one of many banks that make its debut in the crypto and web3 markets by launching NuCoin. JPM Coin is another cryptocurrency launched by a leading investment banking company JP Morgan Chase. It is a stablecoin that seeks to keep the dollar as its primary benchmark for value.
Since last year, several other investment banking companies, including Goldman Sachs, HSBC, and the Swedish Central Bank, have used web3 and crypto to address problems in traditional finance.
Paraphrasing generators are advanced AI tools that help many writers. So, how does one go about using it?
Paraphrasing tools are a life-saving device for many writers. Removing plagiarism, altering content tone, and rewriting content within seconds are all common traits and reasons for their usage. That’s why their employment in recent times has increased significantly.
When around 80% of college students admit that plagiarizing content is a part of their routine, such tools are even more important to use. Thus, using them is being taught at academic and professional levels.
Today, we’ll understand what paraphrasing tools are and why you must use them. But, most importantly, we’ll be looking at how you should use an AI-based paraphraser. So, let’s begin:
Understanding A Paraphrase Generator: Defining Traits
A paraphrase generator is an AI-enthused tool used for various purposes. Now, an AI-based paraphrasing tool can be software that helps with the writing process. It can be used by copywriters to rephrase content or by students to rewrite essays.
These paraphrase tools are designed to assist and make the writing process more manageable. However, paraphrasing tools are one of the most popular types of AI-based tools. These AI-based assistants even generate content ideas at scale.
But, their primary usage is to recreate or revamp content when needed. To sum it up, here are some of the common elements provided by paraphrasers:
Rewriting content quickly
Finding alternative synonyms and phrases to describe the same ideas
Removing plagiarism by changing the content
Offering various content tones
Changing up to hundreds of words at a time
These factors are some of the commonly provided aspects of a paraphraser. Since a paraphrasing tool is based on advanced AI algorithms, speedy rewriting is one of the chief traits of a paraphrase tool.
Reason To Use Paraphrase Tool As An Assistant
A paraphrasing tool is a software that helps people rewrite content without plagiarizing. It does this by replacing the original text with synonyms, phrases, or a combination of these.
The goal is to make the rewritten content sound like the original text but still have it be different enough so that it won’t get flagged for plagiarism. To help you understand, here are three main reasons to use AI Paraphrasing tools as an assistant:
Paraphrasing generators are a quick and easy way to rephrase content. They are also useful for plagiarism detection, tone alteration, and rewriting content quickly
Paraphrasing tools can be used for plagiarism detection by comparing the original text with the paraphrase to check if it is an exact copy of the original
Paraphrasing tools can help you quickly rewrite content without worrying about plagiarism and tone
Therefore, paraphrasing tools are great for people who want to rewrite their own blog posts or articles but don’t have time to do so. It’s also good for people who want to change the tone of their writing but don’t know how and want an unbiased opinion on how they should do it.
Paraphrasing Text With The Help of Paraphrase Generator
Using a paraphrase generator is a straightforward paraphrasing process. But, to help you understand the right way of using it, we’ve formulated a basic process that every writer can employ. So, let’s get started:
Step One: Pick A Paraphrasing Tool
The first step is to pick a paraphrase tool. But what do you need to look for when finding one? Besides the tool featuring outstanding AI algorithms, it must have a few key features, such as:
Extensive word-count limit, preferably 1000 and above
Various supported languages
Quick rephrasing
Easy UI design
For demonstration, let’s pick a paraphrase generator by Editpad.org. It has all the key features we’ve just discussed, allowing us to rephrase our content quickly.
Step Two: Identify The Purpose
Now that you have a tool, you need to identify the purpose of rephrasing your content using a paraphraser. In most cases, academic or professional, the common goals include:
Removing plagiarism
Changing content tone
Refreshing content, i.e., making it new/better
Making content flow better
If your purpose is one of the following four, then you need to keep that in mind from the get-go.
Step Three: Pick Content Tone (If Available)
The third step is to pick a content tone. Granted, not many tools offer those, but some do. Therefore, use it if you have options such as:
Fluent
Standard
Creative
If not, the tool knows which content tone is best for you. So, there might not be a need to pick one. However, if the tool offers it, it’s suggested that you try out each one before you find the one that matches your natural writing tone.
Step Four: Revamp/Rewrite Content
The fourth and main step to paraphrasing text with the help of an AI paraphrasing tool is to paste or upload your content to the tool. Once you do, click on the paraphrasing button. However, some tools might require a captcha check before doing so.
Once the tool begins rewriting, it’ll show you a progress bar, as seen here. When the tool has finished rewriting, this is the outcome you’ll expect:
The content marked in bold is the changed content. You can try to rewrite it once more by copying the rephrased content and pasting it inside the editor. Moreover, you can try additional options like summarizing the rewritten text or checking it for plagiarism.
Step Five: Proofread
The final step you’ll take is to proofread your content. Now, why do you need to proofread if a paraphraser can paraphrase content emphatically? Because:
It’ll allow you to match your original content tone
Remove any unnatural-sounding phrases/words
Find any rare grammatical errors
Change or remove the text elements you don’t need
Therefore, it’s imperative that you proofread, regardless of how well a paraphrasing tool rewrites your text.
Conclusion
This is the process every writer should employ, regardless of their setting. For both students and pro writers, this approach can help them paraphrase text with an AI tool quite quickly and effectively. Therefore, identify your purpose and paraphrase away.
General Motors Co. (GM) has temporarily suspended advertising on Twitter after the head of rival automaker Tesla, Elon Musk, acquired the social media platform on Thursday.
The Detroit automaker, gearing up to catch up with Tesla in electric vehicle (EV) development, said on Friday that it is in talks with Twitter to discuss how the platform will transform. GM said it will stop advertising until the company has a better understanding of what will happen to it with Musk now at the helm.
GM spokesperson David Barnas said that the company is engaging with Twitter to better understand the platform’s direction under its new ownership. He added that GM has temporarily paused their paid advertising on Twitter, as is the ordinary course of action with a major change in any media platform. GM will continue to carry out its customer care interactions on Twitter.
On October 27, Musk completed the acquisition of 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. There are also speculations about top executives being asked to leave Twitter.
Since completing his acquisition Thursday, Musk has said he will convene a content council to decide on standards for users and their tweets. Among the considerations will be whether public figures suspended, such as former US President Donald Trump, should be allowed back on the platform.
Software testing is essential to the software development life cycle (SDLC) since it finds and corrects software bugs. It is a method of evaluating the effectiveness and quality of software applications created with a specific goal in mind and if necessary, making adjustments. Software testing is tedious and repetitive, but new software methodologies combine manual and automated testing. If this excites you and you want to learn software testing, here is a list of top software testing online courses in 2022.
1. Selenium 4 WebDriver with Java – Udemy
“Selenium 4 WebDriver with Java” is an advanced-level software testing course on Udemy. The course has 48 sections and 462 lectures describing Selenium, Selenium WebDrivers, testing frameworks, allure reporting, Selenium grid, database testing, and more. This course is highly extensive, including seven live projects, teaching how to automate web-based applications and implement various frameworks like data-driven, hybrid, page object model, page factories, cucumber BDD, etc. You also learn major reporting and customization, including TestNG reports, ReportNG, extent reports, allure reports, and cucumber JVM reporting.
“ISTQB Software Testing Foundation” is a self-paced, software testing online course on Reed. This course is aimed at professionals and beginners who want practical knowledge of software testing fundamentals. The syllabus is designed by the International software testing qualification board (ISTQB) with major chapters on the process of testing, ensuring effective testing, test design techniques and management, choosing test techniques, and the test development process. There are six modules in the course containing different chapters. Additionally, the skills one can learn in this course are the seven testing principles, debugging, static testing, dynamic testing, and keeping software under control. There is no prerequisite for the course, but being familiar with software testing and its terminology helps. The target audience of this course is junior software testing professionals or any job related to software testers. This is a paid software testing course, and also you need to pay separately for exams and assessments to get certification.
3. The Complete 2022 Software Testing Bootcamp – Udemy
“The Complete 2022 Software Testing Bootcamp” is a software testing course on Udemy. The course has 32 sections and 309 lectures, laying down software testing concepts from beginner to advanced level in 27 hours. This vast course includes manual and agile testing basics, API & web service testing, performance testing, freelance testing websites, unit testing, black-box testing techniques, and white-box testing techniques. This course is a source of everything a software tester needs to learn and has no prerequisites. The course targets people who want to begin a new career and those who are up for a part-time or freelance job in software testing.
4. Software Testing and Automation Specialization – Coursera
“Software Testing and Automation Specialization” is a series of courses in software testing that provides extensive training in software testing for approximately four months. The course is available on Coursera and offered by the University of Minnesota. This specialization consists of four courses, introduction to software testing, black-box and white-box testing, introduction to automated analysis, and web and mobile testing with selenium. These software testing online courses are intended for beginners to intermediate-level software testers and developers who want to develop skills in software testing, practice, and master theory, techniques, and tools to use the software effectively. The course covers black-box testing techniques, white-box testing techniques, unit testing, static analysis, testing automation, writing test plans and defect reports, execution of tests, and understanding testing theory. This specialization course also includes a hands-on project to be completed successfully to earn certification.
5. In-Depth Software Testing Training Course From Scratch – Udemy
“In-Depth Software testing Training Course From Scratch” is a 26-hour software testing course provided by Udemy, intended for beginner to advanced-level students and professionals. The course contains eight sections and 17 lectures with comprehensive information on software testing. This course covers unique topics and skills and helps learners gradually make their way into the testing world. The course also offers a live end-to-end software testing project to give a practical learning experience. The chapters in this course are the introduction to software testing, test scenario, test cases and test plan writing, test execution, test strategy and defect management, JIRA & Bugzilla tools, and automation overview with QTP. There are no prerequisites for this course, anyone with basic computer knowledge can easily take this course and learn software testing.
6. Automated Software Testing: Unit Testing, Coverage Criteria, and Design for Testability – edX
“Automated Software Testing: Unit Testing, Coverage Criteria and Design for Testability” is an online software testing course on edX. It is a self-paced series course containing two courses, unit testing, and coverage criteria and design for testability that takes approximately five weeks to complete. The first course teaches types of testing, including specification-based testing, boundary testing, unit vs system testing, and test code quality. The second course covers test adequacy, code coverage, mock objects, and design for testability. Testers will learn how to test any software system using current state-of-art techniques, how to derive test cases dealing with an exceptional, corner, and bad-weather cases, how to develop testable architectures and write maintainable test code, and learn the limitations of current testing techniques. By the end of the course, software testing will never be the same again, and the tester will be able to choose the best testing strategies for different projects. Overall, the course provides a highly practical approach with various test programs using different techniques throughout lessons. The prerequisite for the course is to have an introductory knowledge of programming, especially Java.
“Software Testing” course on the National programme on technology enhanced learning (NPTEL), co-ordinated by IIT Kharagpur, is an elective online course in software testing. NPTEL is an initiative of seven Indian Institutes of Technology and the Indian Institute of Science, Bangalore, to provide quality education to everybody. The course duration is four weeks focusing on four major topics, one a week, including an introduction to software testing and test process, black-box testing, white-box testing and integration, regression, system testing, and test automation. The target learners are elective courses of UG and PG students and anyone interested in software development and testing. The prerequisite for this course is to have basic knowledge of programming. The course is free, but to get the certification, you need to qualify for an examination conducted by NPTEL that has some charges.
8. Business Analyst: Software Testing Processes & Techniques – Udemy
“Business Analyst: Software Testing Processes & Techniques” is a software testing course for business analysts who want to run software tests efficiently and accurately. Organizations are demanding more and more from business analysts, and software testing is only one aspect. This course provides training in software testing and teaches the repeatable fundamentals, testing processes, and techniques. The topics covered in this course are software testing basics, testing documentation, defect tracking, and eight steps to successful testing. This course follows the BA’s Guide technique of ‘TEACH, SHOW, DO,’ ensuring total comprehension of the topics at hand and retaining maximum information after the course.
“Automated Testing: End to End” is a practical software testing course that teaches how and what to test at the unit, integration, and functional UI levels of software testing and then brings them all together with the continuous integration build server. This course is available on Pluralsight and is 3.3 hours long, combining all sessions, making it the shortest course in this list among other software testing online courses. The information in this course is concise and comprehensive to deliver a simple understanding of automated testing. As automated testing can detect defects earlier than manual testing, soon automated testing will significantly streamline testing operations.
“Monday Productivity Pointers” is a beginner-level productive and technology management course on the LinkedIn learning platform. This extensive 11-hour and 45 minutes long course introduces tools and tips to use software and services more efficiently and powerfully. It is a weekly series of practicing productivity with the insulators Jess Stratton, Garrick Chow, and Nick Brazzi. This course provides skills like productivity improvement, productivity software, computer skills (Mac and Windows), social networking skills, and using Google platforms. Anybody can take this course, as the prerequisites are simple knowledge and experience in technology and software. Although it is a concise and informative course, they don’t provide certification because it’s an ongoing course.
Data processing is a high-priority task because of the exponential rise in data consumption. Data is analyzed by being manipulated to get insights and useful information according to one’s requirement, beginning with collecting or scraping data, conducting analysis, and producing dashboards. Retaining raw data into information and using it to perform further business predictions drives enterprises toward data-driven decisions. For this, the industry of data science and analytics is booming. Although data management and data modeling are crucial aspects of data analysis, data wrangling in data science has been the core emphasis since the beginning. Data wrangling is a collection of processes that transform raw data and can be labeled as the prerequisite to a successful data analysis. Let’s see what data wrangling is in data science, its importance, the steps for data wrangling, and the skills for data wrangling.
What is Data Wrangling?
Data wrangling, also known as data munging, or data remediation, is a collection of processes, including cleaning, organizing, structuring, and enriching raw data to transform it into a readily usable format. The methods for data wrangling vary greatly depending on the dataset and the objective of the project. This is an important prior step to data analysis to provide data quality.
Though new technologies have empowered shortcuts to ease heavy workloads, it is not so much in data wrangling. For which, the whole implementation of data wrangling remains manual. The process being manual consumes a lot of time, and according to Forbes, data scientists and analysts spend 80% of their time on data wrangling. And for a fact, data wrangling is not the most enjoyable part of the role. The reason why data wrangling is time-consuming is the whole process is fluid, and the steps to begin and end are vague or not definite for all datasets. However, there are six steps to data wrangling that give a general idea of what one must look for data quality and data reliability. Also, data wrangling methods need to adapt to a particular dataset, which is iterative, making data wrangling a labor-intensive process. Overall, the data wrangling process depends on factors like the source of data, the quality of data, the data architecture of the firm, and the aim of data analysis.
Importance of Data Wrangling
Data wrangling is necessary for the data science process as it delivers information through analysis. Any analysis eventually brings helpful insight into information or trends in a business, be its data analysis for modeling and prediction, building dashboards, or making reports. The process serves as an initial step to remove the risk of errors ensuring the data is reliable for further analysis. Alike laying a foundation initially goes long for a strong establishment, data wrangling enables the transformation of data into the desired format, which then produces valuable outputs. And if data wrangling is avoided, it may lead to significant downfalls, missed opportunities, and erroneous models, costing you time, money, resources, and the firm’s reputation.
The primary tasks that data wrangling tools help with are –
Increasing data usability: Making raw data usable by transforming it into another format and securing data quality.
Ease of data collection: Gathering data from various sources into a single centralized location.
Clean data: Detection of noise or flaws and missing observations is simpler when sorting data of the preferred format.
Business-oriented approach: Gathering raw data in one place and converting it to the required format eases the tasks of identifying the business’s best interest and improves the targeting of audience.
Quick decision-making: As most errors and mistakes are eliminated already in data wrangling, further data processing is smoother to provide rapid data-driven decisions or models.
Visualization of data: Visualization is the key to understanding anything at first glance. Many data analysts and scientists prefer to include a visual representation in data wrangling and exploratory analysis, ensuring the best aspects of the data are reflected. Once the data is wrangled, export it to a visual analytics platform that will summarize, sort, and analyze the data.
Each step in data wrangling offers to manipulate data to understand better and extract information hidden in the data.
1. Discovery
The first step of data wrangling is to discover. As simple as it sounds, discovering data means getting to know the data and conceptualizing how you can use it. Manually getting familiar with the dataset is crucial in ultimately catching patterns and pushing the limits of what one can do with it. In discovery, the easiest errors to find is missing or incomplete values, and plan to structure the data in an organized manner.
2. Structuring
As the collection of data may come from more than one source, it may contain numerous data formats and sizes. The data required to be restructured and organized to make it more manageable for the analytical model. This step includes general standardization like string for names, integers for salary, date format for date, and so on.
3. Cleaning
Data cleaning consists of tasks dealing with errors, including duplicate entries, invalid values, and null values. Many people think data wrangling and data cleaning are the same, but that is not true. Data cleaning is included in the step of gaining wrangled data. It includes tasks like making corrections, removing errors, handling outliers, eliminating unnecessary data points, etc. Data cleaning can be performed swiftly with programming languages like Python, R, and SQL.
4. Enriching
This step determines if the data need to consider external data for better performance. This especially helps data miners address certain labels not included in the dataset beforehand but prove to bring out relevant information in the dataset. Here, the goal is to fill the gaps in the data (if any) to derive meaningful information and, in the end improve the analysis. Enriching is optional in data wrangling but holds great significance if the current data cannot provide better insights.
5. Validating
As data is continuously manipulated and edited in data wrangling, this step checks the quality of the wrangled data. The process is to verify whether or not the data has quality, consistency, accuracy, security, and authenticity. The validation is extensively thorough, using some automated techniques in programming. And if the data doesn’t fit the requirement, the issues are resolved using different techniques, and the whole process is iterative until you reach the desired or best possible outcome.
6. Publishing
Publishing is the final step in data wrangling, where the wrangled data output is ready for analytics. The data is to be published in an easily accessible location for the team to work on it, such as a new data architecture or database server. The output dataset here is a standardized version of itself, without the errors, sorted and categorized.
Good data wrangling skills are one of the most essential skills of a data scientist and analyst. Knowing the dataset entirely allows you to enrich the data by integrating information from multiple sources and solving common transformation problems and data quality issues. To promote data wrangling skills for the job, companies prefer to train interns and freshers with skills like data annotations, web scraping, and data transformation, including merging, ordering, aggregation, and so on. This training helps to induce the mindset to find and fix errors by knowing where the errors could come. Though the source of errors is vague, the idea is to eliminate errors by seeing through the raw data.
The tools used for data wrangling include programming languages, software, and open-source data analytics platform. Some tools are MS Excel Power Query, Python and R, Alteryx APA, and more. Some visual data wrangling tools like OpenRefine, Trifacta, and Tableau are also designed for beginners and non-programmers. Each tool has its specifications, such Trifacta features cloud integration, standardization, and easy flow, MS Excel features broad connectivity with data sources and combines tables, Tableau features visual appeal, high security and real-time sharing, and so on. There is no best or all-rounder tool for data wrangling yet in the market, as the use depends on the requirement and goal for analysis using a dataset.
As data wrangling consumes a lot of time, new automated solutions are developed that use machine learning algorithms. Yet the development of automated solutions for data wrangling is tough as the process requires intelligence and not only a repeated process of work. These automated tools aim to validate data mapping and inspect data samples thoroughly at each step of transformation. There are few automated software available today using end-to-end machine learning pipelines, performing the three domains of automation in data wrangling, cleaning, structural curation, and data labeling.
Machine learning has given an idea of innovation and power to young minds. With the advancements in computer technology, it has become the stepping stone into the future. Machine learning provides various algorithms for solving different problems, one of which is classification. Classification recognizes, understands, and groups ideas and objects into categories or classes. Now, let’s see some types of classification algorithms in machine learning.
1. Logistic Regression
Logistic regression is a supervised machine learning technique used for classification problems. Here, we predict the categorical dependent variable using the given independent variables. The predicted outcome is binary: yes or no, 0 or 1, etc. The working of logistic regression is that the relationship between the dependent and independent variables is statistically analyzed with the sigmoid (aka logistic) function to carry out prediction, which is close to linear regression where a regression line is fitted to data. It is on top of the types of classification algorithms in machine learning because of its working principle, which is as simpler as linear regression. The sigmoid function is a mathematical function that is an S-shaped curve used to convert values into probabilities.
Mathematically, we define the probabilities of outcomes and events by measuring the impact of multiple variables in the given data. Logistic regression plays an important role in machine learning because of its ability to provide probabilities and classification of new data using historic continuous or discrete data. There are two assumptions for logistic regression:
The nature of the dependent variable must be categorical
where, log[y / 1-y] is the logarithm of the likelihood of the dependent variable
b0 is the y-intercept
x1,x2, x3… are the independent variables
b1, b2, b3… are the slope coefficients
Based on the number of outcome categories, logistic regression are of three types.
Binomial – There are only two possible outcomes, 0 or 1, yes or no, etc.
Multinomial – When there are three or more unordered possible outcomes. For example, categories of water bodies, ‘sea’, ‘lake’, or ‘river’.
Ordinal – When there can be three or more ordered possible outcomes, such as ‘good’, ‘better’, or ‘best’.
2. K-nearest Neighbor
K-nearest neighbor or KNN is a non-parametric supervised learning classifier. It is one of the simplest classification techniques in machine learning used for both regression and classification problems. This algorithm works on the neighbors-based classification, a type of lazy learning as it does not learn from the training data immediately but stores it for the execution stage. KNN identifies an object or new data by finding the similarity between new data and the stored training set. It puts the new data into the category it finds the most similar to the available data. The classification computation is done by the majority of votes of the k-nearest neighbors of each data point. Mathematically, the Euclidean distance between the new data point and training data points is calculated. Then the new data point is assigned to the category or class which has the highest number of k-neighbors to the new data point.
‘K’ indicates the number of neighbors a data point has. It is considered a hyperparameter in KNN, which has to be decided beforehand to get the most suitable fit for the data set. When k is small, it gives the most adjustable fit to the data but will have low bias and high variance. Meanwhile, when k has a higher value, it is more flexible to outliners and has a lower variance but high bias. There is no right way to find the best value of ‘k,’ it depends on the dataset, but the most preferred value is 5.
Decision tree is a supervised learning algorithm that uses a tree representation to solve the problem by producing a sequence of rules that can classify the data. This algorithm can be visualized among the other types of classification algorithms in machine learning, making it simple to understand. A decision tree requires little data preparation and can handle both numerical & categorical data. The algorithm is a flowchart tree-like structure in which each leaf node corresponds to a class label and represents attributes on the internal nodes. Decision trees can be defined as a graphical representation for getting all possible solutions to a problem based on the given conditions.
A decision tree works to predict the class of a given dataset. It starts from the root node and goes to leaf nodes. The algorithm works as a classification model by comparing the values of root attributes and record (dataset) attributes based on this, we follow the branch and jump on the next node. This process of comparison continues until you reach the leaf node. To find the best attribute in the dataset attribute selection measure (ASM) is used. ASM is a technique of selecting the best attribute in the given dataset performed either by information gain or Gini index.
The information gain is the change in entropy after segmentation of a dataset, or it is the measure of how much information an attribute provides about a class. The objective of the decision tree is to maximize the information gain, and the node with the highest information gain is split first.
The formula calculates information gain,
Information Gain= Entropy(S)- [(Weighted Avg) *Entropy(each feature)
And the formula for entropy is,
Entropy(s)= -P(yes)log2 P(yes)- P(no) log2 P(no)
where, S= total number of samples, P(yes)= probability of yes, and P(no)= probability of no
Gini Index is a metric measuring impurity or purity of an element. It calculates the amount of probability of a specific attribute classified incorrectly when selected at random. The decision tree prefers a low Gini index attribute to create binary splits. The formula for calculating the Gini index is,
Gini Index= 1- ∑jPj2
where Pj is the probability of an element being classified for ‘j’ distinct class.
4. Support Vector Machine
Support vector machine (SVM) is one of the most widely used supervised learning classification methods because of its significant accuracy with less computation power. The objective of SVM is to fit a hyperplane to the data points in an N-dimensional space that distinctly classifies data points and helps to categorize a new data point. Hyperplanes are decision boundaries that can segregate the N-dimension space into classes. The dimension of the hyperplane depends on the number of features present in the given dataset.
SVM chooses extreme points or support vectors that help create the hyperplane, thus, the algorithm’s name. Support vectors are defined as the data points closer to the hyperplane, which influence the position and orientation of the hyperplane. With these support vectors, SVM tries to maximize the margin of the classifier. The shortest distance between the observed data points and the threshold is called the margin, and the threshold is the largest distance between the two classes. See the diagram below for a better understanding of the hyperplane.
Linear SVM – When the dataset is linearly separable, that is, if the data is classified into two classes by a single straight line. Then, the classifier is called a linear SVM.
Non-linear SVM – When the dataset is non-linearly separable, the data can not be classified using a straight line. Then, the classifier is called a non-linear SVM.
Naive Bayes is a probabilistic supervised learning algorithm based on the Bayes theorem used to solve types of classification problems. It makes predictions based on the probability of an object. Naive Bayes classifier is widely used in text classification, spam filtration, and sentiment analysis. It is one of the most simple but fast, accurate, and reliable algorithms in machine learning. Now, Bayes’ theorem or Bayes’ law is the basis of the algorithm, which is used to calculate the probability of a hypothesis with prior knowledge and works on conditional probability. Conditional probability is a measure of the probability of an event occurring, given that another event has occurred.
P(A|B) = Posterior probability, i.e the probability of hypothesis A on the observed event B.
P(B|A) = Likelihood probability, i.e the probability of the evidence given that the probability of a hypothesis is true.
P(A) = Prior Probability, the probability of hypothesis before observing the evidence.
P(B) = Marginal Probability, the probability of evidence.
The fundamental assumption of the Naive Bayes classification model is that each feature makes an independent and equal contribution to the outcome. To be noted, the assumption is not generally found in real-world situations. In fact, the independence assumption is never correct but often works well in practice.
There are three types of Naive Bayes classification models,
Gaussian – If predictors take continuous values instead of discrete, the dataset’s features follow the normal distribution. Then, the Naive Bayes model is called a gaussian model.
Multinomial – When the data is multinomially distributed, the classifiers use the frequency of words for the predicators to assign a category called the multinomial model.
Bernoulli – Similar to the multinomial model, the predictor values are independent boolean variables.
Machine learning provides various classification techniques, and we have discussed the five most simple and basic types of classification algorithms. The above-stated algorithms are easy and straightforward for implementation yet give good accuracy. These algorithms use mathematically & statistically proven methods & laws to perform analytical tasks.
