Trending March 2024 # Deeper Learning: The Coaching Model # Suggested April 2024 # Top 9 Popular

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Today’s post is from Brandon Wiley from the Asia Society International Studies School Network (ISSN) . Brandon describes the ISSN approach to coaching teachers towards deeper learning for their students using the SAGE strategy.

Do these questions or questions like these sound familiar? Why does learning in many schools around the United States still looks very traditional, lacking the rigor and purposefulness required to prepare students for the 21st Century? Understanding exactly how to develop the knowledge, skills and dispositions students must possess to be successful in the 21st century has become a common discussion point in many schools across the United States and abroad. We express a desire to help our students become better problem-solvers, to be collaborative and to think critically. The truth is, as I reflect on my own time as an elementary and middle school teacher, I wonder sometimes if my instruction truly allowed for students to engage in deeper learning.

Why do we need to know this? Is this going to be on the test?

Instruction that embodies the characteristics of deeper learning certainly can answer the two questions posed above. Developing instruction that engages students with an issue of local or global significance, provides them an opportunity to apply content knowledge in a meaningful way and allows multiple opportunities for reflection, refinement and self-assessment all serve as ways to engage students in deeper learning. To make this all possible though, it’s important to help coach and support teachers as they plan and instruct.

Since 2003, Asia Society has worked in partnership with school districts and charter authorities to create the International Studies School Network (ISSN), a national network of over thirty design-driven schools serving urban, suburban and rural communities in seven states. The mission of each ISSN school is to create an environment for learning and development in which every student is prepared to succeed in college or other post-secondary education and to develop global competence. Global competence is best defined as a student’s capacity and disposition to understand and act on issues of global significance. Instruction that supports the development of globally competent student provides multiple opportunities to investigate the world, recognize and weigh diverse perspectives, communicate ideas and take action.

The core learning approach within each ISSN school is the Graduation Performance System (GPS), which provides clear criteria and a reliable process for students to produce work that demonstrates college readiness and global competence. In a practical sense, the GPS is an iterative process of helping teachers to plan rigorous performance-based learning tasks, deliver targeted instruction and assess student learning in an authentic way. It provides an opportunity for both the teacher and student to receive feedback, while reflecting on the learning experience. When working with teachers, coaching and support is necessary to help them in understanding how to create learning tasks that embody the elements of deeper learning. One strategy we have used to help guide their thinking is to ask them to reflect on the SAGE elements of a learning task:

Student Choice: The task calls on students to plan and assess their work over time through reflection. During the task, students are asked to make key decisions about the direction of their work, focus, and presentation. To support this, the task provides opportunities for teachers to deliver formative and summative feedback to the students throughout the learning process.

Authentic Context: The task provides an experience that resembles what adults do in the real-world. This requires students to communicate, collaborate, think critically, be creative, negotiate with other people, and use digital media in ways that support knowledge building.

Global Significance: The task fosters the capacity and dispositions to understand and act on issues of global significance. Ideally, the task stimulates students to build knowledge that is cross disciplinary.

Exhibition to an Audience: The task provides students with opportunities to showcase or present their work to an appropriate/relevant audience beyond the teacher and classroom. Students are provided opportunities to discuss their work and receive feedback that holds them accountable for their claims.

By helping teachers to identify explicit ways in which the SAGE elements can be developed in a learning task, strategic coaching has assisted them in thinking more deeply about the learning experience they are providing for students. These elements help ensure that students are more engaged in their learning, while giving them a purposeful way to apply 21st century skills. This coaching strategy is but one way we have attempted to help our schools ensure that students are engaged in deeper learning throughout the course of their school year.

For more information about global competence or structures that promote globally-focused instruction, you can download a free copy of the book, Educating for Global Competence by Veronica Boix-Mansilla and Tony Jackson. The book was written and produced through collaboration between Asia Society and the Council of Chief State School Officers (CCSSO) EdSteps Global Competence Task Force.

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What Is The App Shell Model In Javascript?

The App Shell Model is a design pattern that keeps a web app’s UI and data modules apart. Caching the user interface in this design may load content on the fly. This method is widely utilized in progressive web apps (PWAs) because of its many speed and user experience benefits.

Benefits of the App Shell Model in JavaScript Faster Load Times

The user experience is enhanced by reducing the time it takes for the program to load for the first time due to caching the app shell. Users have come to anticipate instantaneous response times from online apps, and any lag in response time will likely be seen as unacceptable. The App Shell Model achieves this separation between the UI and content by rapidly caching and loading the UI.

Enhanced Efficiency

The app’s shell is identical across all screens, so it can be readily improved for speed. Optimizing the app shell and boosting its speed is possible via lazy loading and code splitting by developers, and the end effect is enhanced load times and general user satisfaction.

Improved Performance Offline Capabilities

Users will have a more consistent and dependable experience because of the app shell’s ability to be cached and loaded even when not connected to the internet. Service Workers, a background-running JavaScript API, do this by snooping on network requests. Service Workers allow developers to cache assets and provide offline functionality, making the app shell and content accessible even when the user is not connected to the internet.

How to Implement the App Shell Model in JavaScript? Define the App Shell

A program’s user interface (UI) must have a foundational framework in place, which includes the layout, navigation, and other features shared by all pages and views. The app’s outer shell must be built to load quickly and keep the user interested by using optimized components and a consistent design.

Cache the App Shell

Service Workers, a background-running JavaScript API with network request intercept capabilities, are used to cache the app shell. Developers may cache the app shell and other materials with the help of Service Workers to provide a fast load time and a consistent user experience. The initial load time of the application may be sped up, and UI consistency is ensured across views and pages by caching the app shell.

Load Content Dynamically

The data is dynamically fetched and presented within the app framework. Webpack, a module bundler, can help since it employs code splitting and lazy loading to improve the app shell’s efficiency. Developers can keep the app shell quick and responsive while users switch between views and pages by loading material on the fly.

Optimize Performance

Developers may further enhance the app’s speed by optimizing the app shell. Lazy loading, code splitting, and other optimizations may help you get there. To keep the app shell quick and responsive, developers may utilize technologies like Webpack.

Provide Offline Capabilities

Service Workers may save the app’s shell in a cache to be loaded even when the user is not connected to the internet. Service Workers allow developers to cache assets and provide offline functionality, making the app shell and content accessible even when the user is not connected to the internet. This is especially helpful for PWAs because of their capability to function even in the absence of or with limited access to a network.

Examples of the App Shell Model in JavaScript Google Maps

Google Maps is a famous example of JavaScript that employs the software Shell Model. Google Maps’ user interface is constant across views and pages, making using the map and search features easy. We can keep the app zippy and responsive by dynamically loading material like location data and street view pictures.

Twitter Lite

Twitter Lite is a JavaScript App Shell Model progressive web app. By caching the app shell using Service Workers, we can guarantee that the UI will always load promptly and look the same across all views and pages. Tweets and user profiles, for example, are dynamically loaded to provide a fast and exciting user experience.

Uber

The ridesharing service Uber is another program that leverages the JavaScript App Shell Model. Thanks to its optimized components and uniform design, the app’s shell is built to wow with its speed and polish. The app’s responsiveness and interest are maintained via the dynamic loading of material, including ride data and user profiles.

Conclusion

The App Shell Model in JavaScript is a robust framework with the potential to enhance online applications’ speed and usability dramatically. Developers may improve the app’s speed by caching the app shell and removing unnecessary code by isolating the UI from the content.

Service Workers provide offline functionality so that an app may be used without a network connection. Developers may follow the abovementioned guidelines to successfully implement the App Shell Model in JavaScript, resulting in powerful and user-friendly online apps.

What Is Chat Gpt? The Ultimate Conversational Ai Model

Chat GPT is a powerful language model designed to engage in human-like conversations. It is based on the GPT-3.5 architecture, which uses a combination of deep learning and natural language processing (NLP) techniques to create a system that can process and understand text in a way that is similar to how humans do.

See More : Chat GPT-4 Login: Sign Up, Access and Use

The main goal of Chat GPT is to simulate a human-like conversation between a user and a machine. This is achieved by training the system on large datasets of text input and output, and then fine-tuning it for specific use cases. Chat GPT can understand and respond to text input in real-time, making it ideal for a wide range of applications.

Chat GPT is the result of several years of research and development in the field of natural language processing (NLP). The GPT-3.5 architecture is an extension of the GPT-3 model, which was released by OpenAI in 2023. GPT-3 quickly gained popularity for its ability to generate high-quality text output that was indistinguishable from human-written content.

Building on the success of GPT-3, OpenAI developed the GPT-3.5 architecture, which is optimized for conversational AI applications. Chat GPT is based on this architecture and is the latest addition to the GPT family of language models.

Also Read : ChatGPT Scroll Not Working: How to Fix It?

Chat GPT works by using a combination of deep learning and NLP techniques to process and understand text input. The system is trained on large datasets of text input and output, which it uses to generate responses to new text input.

The training process involves several steps, including pre-processing the text data, training the model on the data, and fine-tuning the model for specific use cases. The resulting model is then deployed in a production environment, where it can process and respond to text input in real-time.

Chat GPT is different from other language models in several ways. Firstly, it is optimized for conversational AI applications, which means that it is designed to simulate a human-like conversation between a user and a machine.

Secondly, Chat GPT is based on the GPT-3.5 architecture, which is an extension of the GPT-3 architecture optimized for natural language processing tasks. This makes Chat GPT more powerful and accurate than other language models when it comes to generating human-like responses to text input.

Finally, Chat GPT is constantly learning and improving. As more data is fed into the system, it becomes better at understanding and responding to text input. This means that Chat GPT can adapt to new use cases and evolve over time.

Chat GPT offers many benefits over traditional language models. Firstly, it can process and respond to text input in real-time, making it ideal for applications that require fast and accurate responses.

Secondly, Chat GPT can generate human-like responses to text input, which makes it more engaging and interactive for users. This can lead to higher levels of user satisfaction and engagement.

Finally, Chat GPT is highly customizable and can be fine-tuned for specific use cases. This means that it can be used in a wide range of applications, from customer service to education and healthcare.

Also Read : How to use ChatGPT on iPhone/iOS

Chat GPT has many potential applications in various fields. Here are some of the most promising use cases:

Chat GPT can be used to provide automated customer service for businesses. This can help to reduce response times and improve customer satisfaction. Chat GPT can be trained on frequently asked questions and common customer issues, allowing it to provide accurate and helpful responses to customer queries.

Chat GPT can be used to create interactive and engaging educational content for students. It can be trained on educational materials and textbooks, allowing it to provide explanations and answers to student questions.

Chat GPT can be used to provide personalized product recommendations and shopping assistance for online shoppers. It can be trained on product catalogs and customer data, allowing it to provide tailored recommendations based on customer preferences and purchase history.

While Chat GPT offers many benefits, it also has some limitations. Firstly, it is highly dependent on the quality and quantity of data that it is trained on. This means that it may not perform well in applications where there is limited data available.

Secondly, Chat GPT may struggle with understanding and responding to complex or ambiguous text input. This can lead to inaccurate or irrelevant responses, which may negatively impact user satisfaction.

Finally, Chat GPT may be susceptible to bias and may unintentionally perpetuate stereotypes or discrimination. This is a concern that is shared by many AI researchers and developers, and efforts are being made to address this issue.

Chat GPT is still in its early stages of development, and there is much room for growth and improvement. As more data is fed into the system, it will become better at understanding and responding to text input. This will lead to more accurate and engaging conversations between users and machines.

In the future, Chat GPT may become a ubiquitous tool for communication and information retrieval. It has the potential to revolutionize the way we interact with machines and could lead to new applications and use cases that we have not yet imagined.

Chat GPT is an innovative language model that is changing the way we interact with machines. It offers many benefits over traditional language models, including real-time processing, human-like responses, and customizable use cases. While it has some limitations, Chat GPT has enormous potential for a wide range of applications, from customer service to education and healthcare. As it continues to evolve and improve, it has the potential to revolutionize the way we interact with machines and could lead to new and exciting developments in the field of AI.

Q. Is Chat GPT an AI-powered tool? Yes, Chat GPT is an AI-powered language model that uses machine learning to understand and respond to text input.

Q. What are some potential applications of Chat GPT? Chat GPT has many potential applications, including customer service, education, healthcare, and e-commerce.

Q. What are some limitations of Chat GPT? Chat GPT is highly dependent on the quality and quantity of data it is trained on and may struggle with understanding and responding to complex or ambiguous text input.

Q. What is the future of Chat GPT? The future of Chat GPT looks promising, with the potential to revolutionize the way we interact with machines and lead to new developments in the field of AI.

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The Future Of Machine Learning: Automl

Do you ever wonder how companies develop and train machine learning models without experts? Well, the secret is in the field of Automated Machine Learning (AutoML). AutoML simplifies the process of building and tuning machine learning models for organizations to harness the power of these technologies. Figure 1 gives a visual AutoML. In this blog, we’ll explore a look at some of its key benefits and limitations. Get ready to be amazed by the power of AutoML.

Learning Objectives

Understand the basics of AutoML and its methods

Explore the key benefits of using AutoML

Understand the limitations of AutoML

Understand the practical impact of AutoML

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is AutoML?

Methods of AutoML: A Comprehensive Overview

Effortless ML: The Merits of AutoML

AutoML: A Closer Look at the Drawbacks

AutoML in Practice: How Companies are Automating Machine Learning?

Conclusion

What is AutoML? The Future of Machine Learning

AutoML is a game-changer in the field of machine learning. It is a training of machine learning models to automate the process of selecting and tuning algorithms. This includes everything from data preprocessing to selecting the most suitable model for the given task. AutoML tools handle hyperparameter tuning and model selection tasks, which typically require time and expertise. With AutoML, users without experience in machine learning can train high-performing models with minimal effort. Whether you’re a small business owner, a researcher, or a data scientist, AutoML helps to achieve your goals with less time and effort. Examples of popular AutoML platforms include Google Cloud AutoML, chúng tôi and DataRobot.

AutoML provides explainable AI to improve the interpretability of the model. This allows data scientists to understand how the model makes predictions, which is particularly helpful in healthcare, finance, and autonomous systems. This can be used to identify bias in data and prevent wrong predictions. For example, AutoML can be used in healthcare fo gnosis by analyzing medical images, in finance for fraud detection, in retail for product recommendations, and in transportation for self-driving cars. Figure 2 shows the AutoML process.

ethods: A Comprehensive Overview

AutoML automates the use of machine learning for real-world problems. This includes tasks such as algorithm selection, hyperparameter optimization, and f rent methods are being developed to tackle the various aspects of the problem. Some popular approaches are given below

Neural Architecture Search (NAS):

This method uses a search algorithm to automatically find the best neural network architecture for a given task and dataset.

Bayesian Optimization: This method uses a probabilistic model to guide the search for the best set of hyperparameters for a given model and dataset.

Evolutionary Algorithms: This method uses evolutionary algorithms such as genetic algorithms or particle swarm optimization to search for the best set of model hyperparameters.

Gradient-based methods: This method uses gradient-based optimization techniques like gradient descent, Adam, etc., to optimize the model hyperparameters.

Transfer Learning: This method uses a pre-trained model on a similar task or dataset as a starting point and then fine-tunes it on the target task and dataset.

Ensemble methods: This method combines multiple models to create a more robust and accurate final model.

Multi-modal methods: This method uses multiple data modalities such as image, text, and audio to train models and improve performance.

Meta-learning: This method uses a model to learn how to learn from data, which can improve the efficiency of the model selection process.

One-shot or few-shot learning: This method can learn to recognize new classes from only one or a few examples.

AutoML is broadly classified into a model selection and hyperparameter tuning, as shown in Fig 3. Many differen integrated into existing workflows.

Effortless Machine Learning: The Merits of AutoML in Machine Learning

AutoML simplifies the machine learning process and brings many benefits, some of which are given below:

Time-saving: Automating the process of model selection and hyperparameter tuning can save a significant amount of time for data scientists and machine learning engineers.

Accessibility: AutoML allows users with little or no experience with machine learning to train high-performing models.

Improved performance: AutoML methods can often find better model architectures and hyperparameter settings than manual methods, resulting in improved model performance.

Handling large amounts of data: AutoML can handle large amounts of data and find the best model even with more features.

Scalability: AutoML can scale to large datasets and complex models, making it well-suited to big data and high-performance computing environments.

Versatility: AutoML can be used in various industries and applications, including healthcare, finance, retail, and transportation.

Cost-effective: AutoML can save resources and money in the long run by reducing the need for manual labor and expertise.

Reduced risk of human error: Automating the model selection and hyperparameter tuning process can reduce the risk of human error and improve the reproducibility of results.

Increased Efficiency: AutoML can be integrated with other tools and processes to increase efficiency in the data pipeline.

Handling multiple data modalities: AutoML can handle multiple data modalities such as image, text, and audio to train models and improve performance.

AutoML offers several benefits for data scientists and engineers that save time and resources by automating tedious and time-consuming tasks. This also improves the interpretability of the model by providing explainable AI. These combined benefits make AutoML a valuable tool in many industries and applications.

AutoML: A the Drawbacks

AutoML has become a popular tool for data scientists and analysts. However, it has limitations. There are following limitations are given below

Limited control over the model selection and hyperparameter tuning process: AutoML methods operate based on predefined algorithms and settings, and users may have limited control over the final model.

Limited interpretability of the resulting model: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions.

Higher costs than manually designing and training a model: AutoML tools and infrastructure can be costly to implement and maintain.

Difficulty in incorporating domain-specific knowledge into the model: AutoML relies on data and pre-defined algorithms, which can be less effective when incorporating domain-specific knowledge.

Potential for poor performance on edge cases or unusual data distributions: AutoML methods may not perform well on data that is significantly different from the training data.

Limited support for certain models or tasks: AutoML methods may not be well-suited to all models or tasks.

Dependence on large amounts of labeled data: AutoML methods typically require large amounts of labeled data to train models effectively.

Limited ability to handle data with missing values or errors: AutoML methods may not perform well on data with missing values or errors.

Limited ability to explain the model’s predictions and decisions: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions, which can be an issue for certain applications and industries.

Overfitting: AutoML methods may lead to overfitting on the training data if not properly monitored, which can result in poor performance on new unseen data.

AutoML is a powerful tool for automating the machine-learning process, but it is with its limitations. It is important to consider these limitations in the presence of expert supervision to validate the results.

AutoML in Practice: How Companies are Automating Machine Learning?

A few practical examples of AutoML are given below:

Google’s AutoML Vision allows users to train custom machine-learning models for image recognition using th mage datasets

H2O.ai’s AutoML enables data scientists and analysts to automatically train and optimize machine learning models without having to write code

DataRobot provides an AutoML platform that can automatically build, evaluate and deploy machine learning models for a wide range of use cases, including fraud detection, customer churn prediction, and predictive maintenance

Amazon SageMaker is a fully managed service that enables data scientists and developers to quickly and , train, and deploy machine learning models at scale

IBM Watson AutoAI is a platform that automates the process of building, training, and deploying machine learning models and provides interpretability and explainability features that help users understand the models’ decision-making processes

Microsoft Azure ML is a cloud-based platform that provides a wide range of tools and services for building, deploying, and managing machine learning models, including AutoML capabilities.

These are a few examples of how companies leverage AutoML in different industries to automate model building and hyperparameter tuning, allowing data scientists to focus on model selection and evaluation.

Conclusion

AutoML automates the process of building and tuning machine-learning models. This method uses algorithms to search the best model and hyperparameters rather than relying on human expertise. AutoML includes increased efficiency and the ability to handle large amounts of data. It can be useful in the shortage of experienced machine learning practitioners. However, there are also limitations to AutoML. It can be computationally expensive and difficult to interpret the results of the automated search process. Additionally, the practical use of AutoML is limited by the data’s quality and computational resources’ availability. In practice, AutoML is mainly used in an indus prove productivity and model performance in scenarios like image, speech, text, and other forms of data.

Key Takeaways:

Simplify the process of building and training models.

AutoML suffers limitations such as a lack of control over the model selection process, huge data requirements, computationally expensive, and overfitting issues.

Expert supervision is important to validate the results of AutoML to counter available limitations.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

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Supervised Learning Vs. Unsupervised Learning

Today, machine learning (ML) has cut a path through sectors, including health care, finance, and entertainment. These developments require machines to analyze datasets using one of two methods: supervised learning and unsupervised learning.

While both approaches have their own pros and cons, they have differing training methods and pre-required datasets that make them beneficial in specific use cases.

See below to learn all about supervised learning and unsupervised learning in the ML market:

Supervised learning requires human supervision to label and tame raw data. Once the data is classified, the model learns the relationship between input and output data for the engineer to apply to a new dataset and predict outcomes.

Compared to the unsupervised approach, supervised learning has higher accuracy and is considered trustworthy, due to the human involvement. Moreover, the approach allows users to produce an input based on prior references and experiences.

Unsupervised learning involves identifying patterns in raw and unlabelled datasets. It is a hands-off approach — the data scientist will set model parameters, but the data processing will continue without human intervention.

Unsupervised learning works without labels, which is a major drawback to analyzing comparative models. However, the technique works well for exploratory analysis by identifying data structures. Unsupervised learning is the go-to method for a data scientist looking to create customer segmentation with given data. Moreover, the approach is ideal for offering initial insights when human predictions or individual hypotheses are likely to fail.

Training data:

Supervised learning requires both labeled input and output data variables.

Learning method:

Under supervised learning, the model interprets the relationship between labeled input and output data to predict outcomes.

Resource-intensive:

Supervised learning is resource-intensive due to the requirement of data scientists to label data.

Complexity:

Relatively simpler programs like R and Python are used in supervised learning.

Algorithm used:

Supervised learning uses classification trees, vector machines, linear and logistics regression, neural networks, and random forests.

Number of classes:

Known

Drawback:

The training involved in the supervised learning approach can be time-consuming. Although labeling might seem like a simple task, it is quite a tedious job. Therefore, the labeling of the input and output data can only be done by an expert data scientist.

Training data:

Unsupervised learning involves the processing of raw and unlabeled data. Moreover, only input data is accommodated in the process.

Learning method:

Unsupervised learning learns patterns via an unlabeled, raw training dataset to find the inherent trend.

Application:

Unsupervised learning is done to cluster similar data points to identify patterns.

Resource-intensive:

Compared to supervised learning, unsupervised learning is less resource intensive and requires no human intervention.

Complexity:

Unsupervised learning requires computationally complex programs to work with large amounts of unlabelled data.

Algorithms used:

Unsupervised learning uses K-means, cluster algorithms, and hierarchical clustering.

Number of classes:

Not known

Drawback:

It is difficult to give a sufficient level of explanation or to validate the output variables without human intervention.

Before picking a machine learning approach, consider:

Evaluation of the dataset:

Check whether your data is labeled or unlabeled. If it is unlabeled, do you have the required expertise to carry out the labeling of the data?

Know your goals:

Do you want to go for classification or regression (supervised learning) or clustering or association (unsupervised learning)?

Size of the dataset:

Is your dataset too large to be handled by supervised learning? Are you looking to generate accuracy or precision for your data trends?

Regression typically establishes causality between an independent and dependent variable using linear, logical, and polynomial techniques, which is ideal for predicting numerical values, like annual revenues, shares, and market projections. for a company.

Classification problems sort test cases into separate classes for better identification through decision trees and linear classifiers. If you want to divide spam mails from your inbox, then classification criteria are at play here.

Unsupervised learning usually involves representation learning, clustering, and dataset density estimation without official labels through an autoencoder algorithm. Benefits of unsupervised learning include:

The method has a use case in image compression and user segmentation, which is ideal for data clustering based on similarities and differences.

Association analysis determines the variable relationship in market conditions, search engines, and product carts of e-commerce websites. Next time you see the Based on Your Search results, know unsupervised learning is at work here.

Dimensionality reduction is an ideal technique for heavy datasets. The method compartmentalizes inputs into manageable sizes while also maintaining their integrity.

Content recommendation:

A streaming provider’s supervised machine learning algorithm can produce personalized recommendations based on an individual’s previous activity and favorite genres as well as content consumed by other users with similar interests. 

Spam detection:

Supervised learning can help clear your inbox by detecting spam. Email providers deploy supervised learning techniques to recognize and segment emails with specific keywords into the spam folder.

Identity verification:

Most websites employ Recatch to verify authentic users through supervised ML tools. Facial recognition systems use supervised learning to differentiate and identify individuals. Traffic lights can operate on a similar concept to fine users violating traffic rules.

Bioinformatics:

Supervised learning can help in storing genetic information, like retinal screening, fingertips, iris textures, swabs, and eyes. A smartphone can use the technique to unlock itself every time a user puts their fingerprint on the sensor.

Anomaly detection:

Unsupervised learning is used to pinpoint specific logistical barriers and detect mechanical issues during predictive maintenance. The technique can also help in fintech to spot scams and save resources.

Targeting specific consumer market:

Unsupervised learning deploys clustering tools to classify and segment users with similar traits to create personas for targeted marketing.

Clinical studies:

Studying and reading genes and tissue expression and making predictive analysis for early stage diseases are examples of unsupervised learning’s

clustering approach.

Relational Data Model In Dbms

What is Relational Model?

Relational Model (RM) represents the database as a collection of relations. A relation is nothing but a table of values. Every row in the table represents a collection of related data values. These rows in the table denote a real-world entity or relationship.

The table name and column names are helpful to interpret the meaning of values in each row. The data are represented as a set of relations. In the relational model, data are stored as tables. However, the physical storage of the data is independent of the way the data are logically organized.

Some popular Relational Database management systems are:

DB2 and Informix Dynamic Server – IBM

Oracle and RDB – Oracle

SQL Server and Access – Microsoft

In this tutorial, you will learn

Relational Model Concepts in DBMS

Attribute: Each column in a Table. Attributes are the properties which define a relation. e.g., Student_Rollno, NAME,etc.

Tables – In the Relational model the, relations are saved in the table format. It is stored along with its entities. A table has two properties rows and columns. Rows represent records and columns represent attributes.

Tuple – It is nothing but a single row of a table, which contains a single record.

Relation Schema: A relation schema represents the name of the relation with its attributes.

Degree: The total number of attributes which in the relation is called the degree of the relation.

Cardinality: Total number of rows present in the Table.

Column: The column represents the set of values for a specific attribute.

Relation instance – Relation instance is a finite set of tuples in the RDBMS system. Relation instances never have duplicate tuples.

Relation key – Every row has one, two or multiple attributes, which is called relation key.

Attribute domain – Every attribute has some pre-defined value and scope which is known as attribute domain

Relational Integrity Constraints

Relational Integrity constraints in DBMS are referred to conditions which must be present for a valid relation. These Relational constraints in DBMS are derived from the rules in the mini-world that the database represents.

There are many types of Integrity Constraints in DBMS. Constraints on the Relational database management system is mostly divided into three main categories are:

Domain Constraints

Key Constraints

Referential Integrity Constraints

Domain Constraints

Domain constraints can be violated if an attribute value is not appearing in the corresponding domain or it is not of the appropriate data type.

Domain constraints specify that within each tuple, and the value of each attribute must be unique. This is specified as data types which include standard data types integers, real numbers, characters, Booleans, variable length strings, etc.

Example:

Create DOMAIN CustomerName CHECK (value not NULL)

The example shown demonstrates creating a domain constraint such that CustomerName is not NULL

Key Constraints

The example shown demonstrates creating a domain constraint such that CustomerName is not NULL

An attribute that can uniquely identify a tuple in a relation is called the key of the table. The value of the attribute for different tuples in the relation has to be unique.

Example:

In the given table, CustomerID is a key attribute of Customer Table. It is most likely to have a single key for one customer, CustomerID =1 is only for the CustomerName =” Google”.

CustomerID CustomerName Status

1 Google Active

2 Amazon Active

3 Apple Inactive

Referential Integrity Constraints

Referential Integrity constraints in DBMS are based on the concept of Foreign Keys. A foreign key is an important attribute of a relation which should be referred to in other relationships. Referential integrity constraint state happens where relation refers to a key attribute of a different or same relation. However, that key element must exist in the table.

Example:

In the above example, we have 2 relations, Customer and Billing.

Tuple for CustomerID =1 is referenced twice in the relation Billing. So we know CustomerName=Google has billing amount $300

Operations in Relational Model

Four basic update operations performed on relational database model are

Insert, update, delete and select.

Insert is used to insert data into the relation

Delete is used to delete tuples from the table.

Modify allows you to change the values of some attributes in existing tuples.

Select allows you to choose a specific range of data.

Whenever one of these operations are applied, integrity constraints specified on the relational database schema must never be violated.

Insert Operation

Whenever one of these operations are applied, integrity constraints specified on the relational database schema must never be violated.

The insert operation gives values of the attribute for a new tuple which should be inserted into a relation.

Update Operation

You can see that in the below-given relation table CustomerName= ‘Apple’ is updated from Inactive to Active.

Delete Operation

To specify deletion, a condition on the attributes of the relation selects the tuple to be deleted.

In the above-given example, CustomerName= “Apple” is deleted from the table.

The Delete operation could violate referential integrity if the tuple which is deleted is referenced by foreign keys from other tuples in the same database.

Select Operation

In the above-given example, CustomerName=”Amazon” is selected

Best Practices for creating a Relational Model

Data need to be represented as a collection of relations

Each relation should be depicted clearly in the table

Rows should contain data about instances of an entity

Columns must contain data about attributes of the entity

Cells of the table should hold a single value

Each column should be given a unique name

No two rows can be identical

The values of an attribute should be from the same domain

Advantages of Relational Database Model

Simplicity: A Relational data model in DBMS is simpler than the hierarchical and network model.

Structural Independence: The relational database is only concerned with data and not with a structure. This can improve the performance of the model.

Easy to use: The Relational model in DBMS is easy as tables consisting of rows and columns are quite natural and simple to understand

Query capability: It makes possible for a high-level query language like SQL to avoid complex database navigation.

Data independence: The Structure of Relational database can be changed without having to change any application.

Scalable: Regarding a number of records, or rows, and the number of fields, a database should be enlarged to enhance its usability.

Few relational databases have limits on field lengths which can’t be exceeded.

Relational databases can sometimes become complex as the amount of data grows, and the relations between pieces of data become more complicated.

Complex relational database systems may lead to isolated databases where the information cannot be shared from one system to another.

Summary

The Relational database modelling represents the database as a collection of relations (tables)

Attribute, Tables, Tuple, Relation Schema, Degree, Cardinality, Column, Relation instance, are some important components of Relational Model

Relational Integrity constraints are referred to conditions which must be present for a valid Relation approach in DBMS

Domain constraints can be violated if an attribute value is not appearing in the corresponding domain or it is not of the appropriate data type

Insert, Select, Modify and Delete are the operations performed in Relational Model constraints

The relational database is only concerned with data and not with a structure which can improve the performance of the model

Advantages of Relational model in DBMS are simplicity, structural independence, ease of use, query capability, data independence, scalability, etc.

Few relational databases have limits on field lengths which can’t be exceeded.

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