Kedar Kanekar

What are machine learning algorithms? How do machine learning algorithms work? What are examples of machine learning algorithms? Which algorithm is best for machine learning? Keep reading this blog post to find out.

Machine Learning is a subset of Artificial Intelligence. ML imitates the human learning process and helps us automate tasks. In addition, ML assists in decision-making, pattern recognition, risk assessment, image classification, predictive analysis, data processing, and a lot more.

What are machine learning algorithms?

Machine learning algorithms are a set of instructions that guide a computing system to process historical data and produce the output within a given range. Prediction, classification, regression, forecasting, and data modeling are some of the major applications of machine learning algorithms. 

Types of machine learning algorithms

1. Supervised learning
2. Unsupervised learning
3. Semi-supervised learning
4. Reinforcement learning

List of popular machine learning algorithms:

1. Linear regression (Supervised Learning/Regression)

   Linear regression is a simple and popular machine learning algorithm commonly used for predictive analysis. Logistic regression allows users to study the relationship between dependent and independent variables by defining a line and its equation. This line is the regression line, and its linear equation is y=mx+c.
   
   Here, y is the dependent variable, x is the independent variable, m is the slope of the line, and c is the intercept. The m and c are calculated by minimizing the sum of the squared distance between the two points and the regression line.
   
   This linear regression algorithm is commonly used to predict stock market movements.
   

2. Logistic regression (Supervised learning – Classification)
   

   Logistic regression is a supervised machine learning algorithm that uses binary values (0 and 1) from a set of independent variables. Logistic regression predicts categorical and discrete values. This algorithm comes in handy to solve classification problems and predict the probability of an event.
   
   ML experts also use logistic regression for binary classification of data and fraud detection.
   
   Logistic regression uses a transformation function. The logistic function h(x) = 1 / (1 + ex) forms an S-shaped curve that lies between 1 and 0. Logistic regression is applied to predict the probability of a yes/no event, such as whether or not a patient has a heart attack, whether or not a debtor will default, and more.

3. Decision tree (Supervised Learning – Classification/Regression)
   

   The decision tree is a machine learning algorithm that classifies both categorical and continuous dependent variables. The decision tree divides the data into two or more similar sets based on the attributes and variables.
   
   A decision tree starts with a root node and ends with a leaf node. The branches show the decision rules/conditions, the internal node represents the dataset features, and the leaf node represents the output.
   
   The decision tree algorithm has real-world applications like identifying cancerous and non-cancerous cells, suggesting products and services to potential buyers, and more.
   
   Related Post: What is Machine Learning Ops (MLOps)? How to get started?

4. Support vector machine (Supervised Learning – Classification)
   

   Support vector machine is a classification algorithm that allows you to plot raw data as points in an n-dimensional space. N represents the number of features you have defined. The value of each feature is associated with specific coordinates.
   
   The SVM algorithm creates a hyperplane or a decision boundary that separates data sets into different classes. The support vectors are the data points that define the hyperplane. Classifiers split the data and plot those data points on a graph.
   
   Support vector machines have real-life applications, such as image classification, face detection and identification, drug discovery, and more.

5. Naive Bayes (Supervised Learning – Classification)
   

   The Naive Bayes algorithm is based on the Bayes theorem – calculate the probability that an event may occur. The algorithm is naive because the variables are independent of each other. The Naive Bayes is a supervised machine learning algorithm that is based on conditional probability.
   
   Here is the equation…
   
   P(A|B) = P(B|A) * P(A)P(B)
   
   Here P(A|B) = posterior probability. Posterior probability calculates the probability of event A for data B is calculated.
   
   P(B|A) is the likelihood, i.e., the probability of data B if event A occurs.  P(A) is class prior probability, and P(B) is predictor prior probability.
   
   The Naive Bayes algorithm is ideal for large datasets, such as text classification.
   
   Related Post: Artificial Intelligence vs Machine Learning vs Deep Learning: What’s the Difference?

6. K-nearest neighbors (Supervised Learning)

   The K-nearest neighbors are a supervised learning algorithm for the classification and regression of data. This algorithm estimates the likelihood of a data point being a member of one or another group. To determine the group of data points, the overall points are analyzed with reference to a single data point.
   
   The K-nearest neighbors algorithm assumes similarities between new and available data points to classify them. The Euclidean distance between data points on a graph classifies them into separate categories. KNN is applied for text mining, agriculture, finance, medical, facial recognition, and more.
   
   KNN is also a lazy-learner algorithm, as it uses the entire dataset as its training set.

7. K-means clustering (Unsupervised Learning – Clustering)
   

   The K-means is an unsupervised machine learning algorithm that can solve clustering problems. Datasets are classified into K-number clusters based on their similarities and differences between other data points. The process is repeated continuously until data points are assigned to each cluster.
   
   Centroids are the center point of each cluster, and the distance from each data point to the centroids is calculated. The data point is assigned to a cluster that is nearest to the centroid. The algorithm then creates new centroids and repeats the process until centroids are not interchanged.
   
   The k-means clustering algorithm is applied for market segmentation, document clustering, image segmentation, image compression, and more.

8. Random forest (Supervised Learning – Classification/Regression
   

   The random forest algorithm follows ensemble learning techniques to combine multiple algorithms and achieve better results. The random forest is a collection of decision trees that classifies new objects according to their attributes. The trees vote for a class, and the forest selects the classification that has the majority votes.
   
   The decision trees represent individual subsets of data and calculate the average to improve the accuracy of the prediction/classification model. The random forest ideally contains 64-128 trees. The input is entered at the top of the decision tree. Then, the input travels down to the subsets based on the attributes/variables.
   
   Random forest algorithm is applied for predicting customer behavior, consumer demand, market fluctuations, fraud identification, diagnosis, and more.

9. Apriori algorithm (Unsupervised Learning)
   

   The apriori algorithm is an unsupervised learning algorithm that can solve association problems. The association problems are aimed at finding interesting associations and relationships among large sets of data items.
   
   The apriori algorithm uses frequent item sets to generate association rules that determine how strongly two objects are connected to each other. This algorithm works on databases, which contain transactions or similar forms of comparable information.
   
   R. Agrawal and Srikant wrote the apriori algorithm in 1994. The algorithm uses a breadth-first search process and a hash tree to calculate item sets. To find frequent item sets from the larger dataset, the process is repeated continuously.
   
   Some of the common applications Apriori algorithm include market basket analysis to find products that can be bundled together, drug reactions in patients, and more.

10. Principal component analysis (Unsupervised Learning)
    

    Principal component analysis is an unsupervised learning technique for dimensionality reduction. This algorithm reduces dataset dimensionality, i.e., it reduces the number of correlated features. The PCA follows a statistical process to convert observations of correlated features into linearly uncorrelated features.
    
    PCA considers the variance of each attribute to check for low variance. A high variance displays better splits between the classes and reduces dimensionality.
    
    The principal component analysis comes in handy for exploratory data analysis and predictive modeling. The applications of PCA include a movie recommendation system, image processing, power allocation optimization for electrical grids, and more. 

Bottom line

A sound knowledge of machine learning algorithms can help you excel as an ML engineer. Knowing when to use a specific algorithm is very important for a machine learning engineer. Using the machine-learning algorithms mentioned above, ML engineers can start implementing ML systems for classification, regression, data analysis, modeling, and more.

Do you wish to work at a top US company as an ML engineer? Are you a machine learning enthusiast looking for high-paying remote jobs? Try Turing.

Turing offers high-paying remote machine learning jobs for developers across the globe. For more information, visit the Apply for Jobs page.

FAQs

1. How do machine learning algorithms work?

   Machine learning algorithms rely on computational techniques to gather information from the data instead of using a predetermined equation as a reference model. The ML algorithms adapt and improve their performance as the sample data increases. The algorithms consider the input variables during training and find the best solution to a given problem.
   

2. What are examples of machine learning algorithms?

   Some of the common machine learning algorithms are linear regression, logistic regression, Naive Bayes, K-nearest neighbor, principal component analysis, random forest, support vector machine, and more.
   

3. Which algorithm is best for machine learning?

   Selecting the best algorithm for machine learning depends on your exact requirements, your sample/learning dataset, your expected format of output, classification/regression calculations, and a few other factors. 

What are the greatest inventions in programming ? Who first invented programming? What was the first computer? What were the first programming languages? Keep reading to find out.

Since August 1998, the United Inventors Association of USA (UIA-USA), Inventors Digest, and the Academy of Applied Sciences have celebrated August as National Inventors Month. 

To commemorate National Inventors Month, we have curated a list of the most remarkable inventions in computer programming. Find the ten most crucial computer programming inventions, their inventors, and their significance in this post. 

Greatest inventions in computer programming

Far ahead of their times, these inventions paved the way for modern computing. Our modern computers, software, and devices would not exist without them. We rely on these technologies, often unaware of their rich history and the inventors that created them.

• Konrad Zuse: First working programmable computer – Z3 (1941)
  
  Konrad Zuse, a German engineer and computer scientist, developed the mechanical computer Z1 in 1936. Perfecting the design further with Z2, Zuse created the first working programmable computer, Z3, in 1941.
  
  Z3 was the world’s first fully-automatic digital computer. The Z3 was built with 2600 relays and had a clock frequency of 5 to 10 Hertz. The initial values were entered manually, and the program was stored on a punched celluloid tape.
  
  The original Z3 was never put to use and was destroyed on 21 December 1943 by the Allied Bombing in Berlin. The Z3 could perform addition in 0.8 seconds, multiplication in 3 seconds, and had a weight of around 1 tonne.
  
  Related Post: 5 Things Alan Turing Taught Us About Problem Solving
  
  
• John Vincent Atanasoff and Clifford Berry: First electronic computer – Atanasoff Berry Computer (ABC) (1942)
  
  John Vincent Atanasoff, an Iowa State College professor, teamed up with Clifford Berry, a graduate student, to invent the first automatic electronic digital computer, ABC. The Atanasoff Berry computer was neither programmable nor Turing-complete. But the ABC was the first ever ALU (arithmetic logic unit) that is an integral part of modern processors. 
  
  As the first computer to use vacuum tubes for calculations, ABC made computing faster than electro-mechanical computers. The ABC was designed to solve problems of linear equations. The computer had some of the main elements of modern computing, such as binary arithmetic and electronic switching elements.
  
  
• Tommy Flowers: First programmable electronic computer – Colossus (1943)
  
  Thomas Harold Flowers, a British engineer, created the Colossus in 1943. As the first programmable electronic computer, Colossus decrypted German messages of the Lorenz cipher. The Colossus offered a strategic advantage to British intelligence during World War 2.
  
  Colossus used thermionic valves (vacuum tubes) to perform Boolean counting operations. The Colossus was programmed with electronic switches and plugs. Colossus reduced the time required for decrypting messages from weeks to hours.
  
  
• John Mauchly and J. Presper Eckert: First general-purpose programmable electronic computer – ENIAC (1945)
  
  American physicist John Mauchly and electrical engineer J. Presper Eckert developed the first programmable, electronic, general-purpose digital computer in 1945. The United States Army funded the Electronic Numerical Integrator And Computer (ENIAC) to calculate artillery firing tables for the Ballistic Research Facility.
  
  ENIAC was a large modular computer with branching capabilities and memory for 10-digit decimal numbers. ENIAC was 101000 times faster than electro-mechanical machines then, with programmability as an added advantage. ENIAC calculated ballistic trajectories within 30 seconds, which required 20 hours for a human.
  
  
• Frederic C. Williams and Tom Kilburn: First stored-program computer – Small-Scale Experimental Machine (SSEM) (1948)
  
  British engineer Sir Frederic C. Williams, English mathematician Tom Kilburn and electrical engineer Geoff Tootil developed SSEM, the first computer featuring a stored program, in 1948. The Small Scale Experimental Machine (SSEM) overcame the shortcomings of delay-line memory, using the first random-access digital storage device: the Williams tube. Binary programs were fed into SSEM that used 32 switches as input.
  
  The SSEM was called Manchester Baby, as it was built at the University of Manchester. It used a standard Cathode Ray Tube (CRT) as the analog display output. The Manchester Baby had a word length of 32 bits and a total memory of 32 words or 1 kilobit (1024 bits).
  
  
• Edmund Berkeley: First personal computer – Simon (1950)
  
  American computer scientist Edmund Callis Berkeley, with help from two grad students at Columbia University, built the first personal computer Simon. The Simon used punched paper or five keys on the front panel as input and five lights as output. Simon could perform four operations: addition, negation, greater than, and selection, with only two bits of memory.
  
  The punched paper also served as memory storage. Simon’s program used relay logic architecture and operated on paper tape. This computer was useful as an educational demonstration of digital computers. 

The invention of programming languages

• John Backus: First high-level programming language – FORTRAN (1954)
  American computer scientist John Backus invented FORTRAN, the first general-purpose, high-level programming language. FORTRAN is a compiled, multi-paradigm imperative programming language ideal for scientific computing and numerical computation. FORTRAN was originally developed by IBM.
  
  The name FORTRAN stands for Formula Translating System. This programming system simplified the process of writing programs. FORTRAN greatly reduced the number of programming statements required to run a machine.
  
  Related Post: 8 In-Demand Programming Languages to Learn in 2022
  
  
• Ole-Johan Dahl: First object-oriented programming language – Simula (1967)
  
  Ole-Johan Dahl and Kristen Nygaard updated Simula 1 to create Simula 67. Simula 67 became the first programming language to have a system of objects, classes, and subclasses. Simula 67 introduced inheritance, virtual procedures, coroutines, garbage collection, discrete event simulation, and subtyping. 
  
  Simula 1 was designed for performing simulations and Simula 67 was a general-purpose programming language. Simula has been a major influence on almost all modern programming languages. Simula is used even today for simulating VLSI designs, communication protocols, process modeling, algorithms, and other applications. 
  
  Related Post: Top Object-Oriented Programming Languages to Learn in 2022?
  
  
• Bjarne Stroustrup: High-level multi-paradigm OOP – C++ (1985)
  The Danish computer scientist, Bjarne Stroustrup, invented C++ as a high-level, multi-paradigm, object-oriented programming language. C++ was developed for embedded systems and system programming, focusing on efficiency, flexibility, and performance.
  
  Even today, C++ is a very popular coding language with many applications. C++ is used for desktop applications, servers, video games, space probes, and more.
  
  C++ features four types of memory management, polymorphism, encapsulation, operators, operator overloading, and more.
  

Invention of microprocessors

•  Fredrico Faggin: First microprocessor – Intel 4004 (1971)
  Italian engineer Fredrico Faggin and his team developed the first microprocessor Intel 4004, in 1971. The Intel 4004 had 2300 transistors with 10 microns spacing into a 12 square millimeter chip. This microprocessor had a maximum clock speed of 740 kHz.
  
  The Intel 4004 could perform more than 60,000 operations per second. The Busicom 141-PF calculator was the first device to use this microprocessor. Intel 4004 was the first noteworthy example of large-scale integration (LSI) and featured MOS silicon gate technology (SGT). 

How have inventions in computer programming paved the way for modern computing?

Through innovation and research, computer scientists improved the performance, efficiency, and computing power of these devices. The majority of modern computer-like devices have roots in the inventions mentioned above. The programming languages we use nowadays are derived from these first programming languages.

The improvements made it possible to create modern computers, mobiles, and even the supercomputers we use today. The rich history and science of these technological marvels inspires researchers, hardware and software developers. This National Inventors Month, Turing hopes to inspire software developers to pioneer, excel, and innovate in their field.

Are you a software developer looking for a job? Try Turing.com. Turing offers high-paying remote software development jobs for developers with 3+ years of experience. Visit the Apply for Jobs page to learn more. 

FAQs

1. What was the first computer?
   Charles Babbage and Ada Lovelace developed the first computer: The ENIAC, the Colossus, the SSEM, the ABC, and the Z3 were the first computers in their unique ways. 
   
   
2. What was the first programming language?
   Konrad Zuse’s ‘Plankalkül’ was the first high-level programming language. However, it did not become famous in the mainstream computer industry. FORTRAN was the first commercially available programming language with many practical applications.
   
   
3. When were computers invented?
   Even though Charles Babbage conceived the world’s first computer in 1822, it was not completely built until 1991. Some of the earliest computers were ENIAC (1945), Colossus (1943), Manchester Baby (1948), and Micral N (1973). 

What is Vue framework? Is VueJS a library or a framework? What is the best VueJS framework? Keep reading this post for answers to these questions and detailed insights about VueJS frameworks.

What is Vue.js?

Evan You created an open-source front-end JavaScript framework in February 2014 and named it Vue.js. Vue is a model-view-viewmodel (MVVM) JavaScript framework that is used to build user interfaces and single-page applications. Vue is very easy to code, easy to learn, and features an incrementally adaptable architecture. Are you new to this framework? This post will help you understand the basics of the Vue.js framework and its applications.

VueJS focuses on declarative rendering and component composition. Vue is simple, user-friendly, and highly customizable. Vue enables developers to build lightweight web applications and easily integrate existing projects into Vue. 

The core VueJS library focuses on the user interface only. Thus, developers need Vue frameworks to implement advanced features and develop complex applications.

Here are the top 10 VueJS frameworks for web development in 2022.

Vue frameworks for UI components:

1. Bootstrap Vue
   

   Bootstrap Vue offers an all-inclusive implementation of Bootstrap 4 UI components and the grid system. Bootstrap Vue supports more than 45 plugins and offers nearly 85 UI components. The framework also provides multiple starter packs and ready-to-use themes for efficient web development.
   
   Bootstrap Vue offers automated WAI-ARIA markup to build mobile-first ARIA-supported websites and hybrid mobile applications. 
   

2. Vuetify
   

   Vuetify is a Material Component framework of UI components for Vue.js 2. Vuetify offers more than 80 UI components with customizable options and events. It supports server-side rendering applications, allows code scaffolding, and hosts three Vue CLI templates for beginners.
   
   Related Post: Vue vs React: Which Framework Should You Choose?
   

3. Vue Material
   

   Vue Material is a VueJS framework that contains UI components that meet Google’s ‘Material Design’ specifications. Vue Material offers an easy-to-use API with multiple themes to improve the developers’ experience. The framework contains scalable Vue UI components for effective front-end web development.
   
   Vue Material is a lightweight Vue.js framework that helps developers create well-designed web applications. Marcos Moura developed Vue Material as a modular tool for web development. Vue Material supports all modern browsers, hosts a great array of components, and offers extensive documentation for implementation.
   

4. Quasar Framework
   

   Quasar is a multi-platform framework used for developing progressive web applications, responsive websites, hybrid apps, and desktop applications. Quasar framework hosts more than 120 UI components for integrating simple and advanced features like data tables, calendars, WYSIWYGs, and more. 

Static website generators based on Vue.js framework

Vue-based static website generator relies on templates and input data to create HTML websites. Some of the static Vue.js frameworks are:

1. Vue Press
   

   Evan You and his team created VuePress, a static website generator. VuePress helps developers build user interfaces for simple web pages and single-page applications (SPAs). Though VuePress framework is a part of Nuxt.js, it also works as a standalone framework.
   
   VuePress generates pre-rendered HTML webpage, which is fed into a SPA. VuePress offers markdown file compilation, multiple customizable themes, and supports many languages.
   

2. Gridsome
   

   Gridsome generates static websites and SEO-friendly progressive web apps with Vue.js. As an open-source framework, Gridsome enhances the website deployment process. Gridsome supports the Push, Render, Lazy-load, Pre-cache (PRLP) pattern.
   
   Gridsome supports code-splitting, GraphQL for queries, and automatic image compression. This VueJS framework allows developers to build PWAs quickly for any source of data. Gridsome can be effectively connected to APIs, headless CMSs, or local files.
   

Mobile frameworks for Vue.js

1. Vue Native
   

   Vue Native hosts mobile UI elements for developing cross-platform native mobile apps with Vue.js. Vue Native uses the Vue CLI, watchers, virtual DOMs, and live synchronization. It uses HTML, JavaScript, and CSS to develop a lightweight user interface for mobile applications.
   

2. Vux
   

   Vux is a mobile UI component library that was originally designed for WeChat messaging app. The library contains many components of the WeUI, and all of its documentation is written in Chinese. Vux is very popular as a component library that supports Vue.js.
   

3. Mint UI
   

   Mint UI is a Vue-based lightweight framework of mobile UI components. It hosts CSS and JavaScript components that can be used for designing interactive mobile apps. Mint UI offers customizable and easy-to-use UI components for web developers and mobile/iOS app developers.

Server-side rendering

1. Nuxt.js
   

   Nuxt.js is an intuitive and versatile open-source Vue.js framework used for faster and easier web development. Nuxt.js can be used for SPAs, Vue-based mobile apps, static websites, and PWAs. Nuxt helps to develop server-side rendered apps with Vue, which works on the client-server.
   
   Nuxt features SEO improvement, meta-tag handling, preset configurations, and compatibility checks. 

Selecting the best VueJS framework for your projects

Vue.js is a lightweight JavaScript framework that lacks higher levels of customizability. With such Vue.js frameworks, creating UI becomes easy for front-end developers. Developers can improve their front-end development workflow with these Vue frameworks.

According to a 2021 survey, more than 44 percent of web developers prefer using Vue.js over React, Angular, or other JavaScript frameworks. In addition, the overall demand for VueJS developers has increased steadily over the past years.

Are you a software developer looking for high-paying, remote Vue.js jobs? Try Turing!

Turing helps software developers secure the best remote jobs in top US companies. Turing offers long-term career growth as a remote software developer with unmatched compensation. Visit the Apply for Jobs page to know more.

FAQs

1. What is Vue framework?
   Vue frameworks are used with Vue.js JavaScript framework to create user interfaces for websites and web apps. Vue frameworks expand the capabilities of Vue.js to run complex processes. Vue frameworks are also used to implement advanced features in simple single-page applications (SPAs). 
   
   
2. Is VueJS a library or a framework?
   VueJS is a JavaScript framework used to develop simple SPAs and progressive web apps efficiently. VueJS is customizable, user-friendly, has two-way binding, and offers a range of other features. VueJS is highly preferred among front-end developers due to its simplicity and easier learning curve.
   
   
3. What is the best VueJS framework?
   The best Vue frameworks are Bootstrap Vue, Vuetify, Vue Material, Quasar framework, Vue Press, Gridsome, Vue Native, Vux, Mint UI, and NuxtJS.

What is AngularJS framework? Which framework do we use for AngularJS? Which framework is best for Angular? For answers to these questions and some insights on AngularJS frameworks, keep reading this post.

AngularJS is a structural framework that uses JavaScript programming language. AngularJS is an open-source front-end development tool, created and maintained by Google. AngularJS is useful for testing and developing client-side applications.

Front-end developers use attractive designs and unique graphics to develop engaging websites. Developers use front-end frameworks like AngularJS to design functional, appealing, and interactive user interfaces.

AngularJS helps developers create single-page applications and dynamic, progressive web applications. With AngularJS, developers can quickly design expressive and readable web pages.

Here are the top 10 AngularJS frameworks used by developers:

1. Mobile Angular UI

   The Mobile Angular UI helps developers build HTML5 mobile apps using Bootstrap and AngularJS. It offers essential mobile components and robust libraries as a mobile UI framework. Mobile Angular UI contains switches, sidebars, overlays, scrollable areas, and positioned top/bottom navigation bars.
   
   With simple AngularJS directives, the Mobile Angular UI makes it easy to build a mobile application. The UI follows the Bootstrap 3 syntax that simplifies converting a desktop web application into a mobile one. A small CSS file is included in the bundle, for a fully responsive touch-enabled UI for mobile applications.

2. Ionic

   Ionic is an adaptive open-source software development kit that uses web technologies to build native-like mobile applications. Ionic is a cross-platform front-end framework for developing hybrid mobile apps. Ionic needs AngularJS to work at its full potential for simplifying the front-end development process.
   
   Ionic is created to help developers convert powerful HTML5 applications to mobile platforms. Ionic supports technologies like Cordova, Phonegap, Trigger.io, and ElectronJS. Ionic uses AngularJS components, directives, and extensions to create progressive native and hybrid applications. 

3. Angular Material

   Angular Material is a UI component library developed by Google for Angular projects. Angular Material helps Angular developers speed up the development of clear, consistent, and interactive user interfaces. As an Angular CSS framework, it consists of templates, admin dashboards, and more than 30 UI components.
   
   AngularJS Material contains tools to customize CSS components with typography, buttons, checkboxes, themes, and more. The AngularJS material library enables developers to create web apps that follow conventional design practices, device independence, and browser portability.
   

4. Angular UI Bootstrap

   Bootstrap is a free and open source front-end framework that includes components, layouts, and JS tools. The Angular UI Bootstrap is an AngularJS framework that is developed using Bootstrap.
   
   Bootstrap requires jQuery to function. Using Bootstrap directly with AngularJS conflicts with some of Angular’s functions. Using Angular UI Bootstrap eliminates the dependence on jQuery, which makes it possible to use Bootstrap functions within AngularJS code.
   
   The Angular UI Bootstrap framework relies on some of the built-in AngularJS directives based on CSS and includes some of the Bootstrap markup elements.
   
   Related post: Bootstrap vs Angular Material: Which Fronted Library Should You Choose?

5. UI Grid

   UI Grid was previously known as ‘ng-grid’. The Angular UI Grid has zero jQuery dependence, as it is fully written in AngularJS. UI Grid has a central grid module, and all its traits define the Angular modules and directives.
   
   The Angular UI Grid has a component based on the Angular UI Suite and Angular UI Bootstrap. The component exhibits data elements inside grids that are designed with AngularJS.
   
   Using UI Grid makes it easy to perform actions like group, sort, virtualize, filter, export, edit in grid, and more. Developers can manage vast data sets with Angular UI Grid. It also helps for row expanding virtualization, column pinning, and internationalization.
   

6. LumX

   LumX is a front-end framework designed with Google’s material design. It follows Google’s guidelines and enables developers to create UIs that fit Google’s guiding principles. LumX makes managing Google metrics easy while designing the UI.
   
   LumX uses AngularJS to build websites with the MVC (model-view-controller) pattern. LumX relies on jQuery to improve the performance of the web application. LumX functions as a Sass preprocessor and uses technologies like Bourbon and Gulp to enhance customization and performance.  
   
   Related post: Angular 13 Features and Updates

7. Supersonic

   Supersonic is a hybrid UI framework that is forked from Ionic. Supersonic combines web components with JavaScript and CSS to create native user interfaces based on AngularJS and HTML5.
   
   Supersonic has mobile-first CSS components and Native UI components with CSS-based styling. The declarative UI makes it easy to integrate APIs within the components of web applications. Using Supersonic improves the speed of development and designing, as UI changes do not require recompilation.
   

8. Radian

   Radian is a scalable, dynamic, powerful, and robust AngularJS framework. Radian makes it possible for designers and developers to embed plots directly onto HTML-based web pages. Radian customizes HTML elements to develop plots and graphs based on the parameters.
   
   Radian uses AngularJS framework to implement the HTML elements. Radian binds the HTML and JavaScript attributes by using D3.js plotting library. Browsers render the SVG elements to display plots within the web page.

9. Suave UI

   Suave UI is a simple and easy-to-use framework that uses AngularJS to develop user interfaces for web applications. The CSS definitions like colorful buttons, layered elements, grids, forms, and more are included in Suave UI.
   
   Suave UI provides the component library for designing the UI as bundled files. This reduces the developer’s efforts to include individual components. A single command using Bower can be used to include all the components at once.
   

10. Angular Foundation

    Angular Foundation is one of the most advanced front-end frameworks. Angular Foundation is a combination of multiple frameworks that are used for designing websites, applications, and emails for all platforms.
    
    Angular Foundation is practical for designing websites that include semantic HTML components. The Angular Foundation framework is flexible, customizable, and highly readable.
    

Which AngularJS framework is the best for you?

AngularJS continues to be one of the most popular JavaScript frameworks that are currently used by major companies across the globe. Though Angular is an upgrade from AngularJS, many organizations continue to use AngularJS even today. AngularJS developers continue to be in demand for developing applications on the AngularJS legacy systems.

Deciding which AngularJS framework is the best for you, depends on your project requirements. Your choice of AngularJS framework will improve the speed, accuracy, and quality of the user interface.

Designing intuitive, responsive, and beautiful user interfaces is possible, with experienced frontend developers and designers. Turing helps companies hire pre-vetted, experienced AngularJS developers for their projects at unbeatable rates in just 3-5 days. For more information, visit Turing.com/hire.

Are you an AngularJS developer seeking AngularJS jobs? Do you want a high-growth, high-paying remote software job with a US-based company? Try Turing! Turing can help you supercharge your career as a remote AngularJS developer. For more information, click on the ‘Apply for Jobs’ button below.

FAQs

1. What is AngularJS framework?

   AngularJS framework is a free and open-source JavaScript framework. It is used for building web apps that follow the AJAX-style web development process. The web pages developed with AngularJS are capable of performing the CRUD operations.
   
   

2. Which framework do we use for AngularJS?

   Mobile Angular UI, Ionic, Angular Material, Angular UI Bootstrap, UI Grid, LumX, Supersonic, Radian, SuaveUI, and Angular Foundation are some of the popular AngularJS frameworks.
   
   

3. Which framework is best for AngularJS?

   Depending on the exact requirements, Angular Foundation, Angular UI Bootstrap, Angular Material, Ionic, and Mobile Angular UI are some of the best frameworks for AngularJS. 

What is Android 13? Android 13 is the thirteenth and upcoming major release of the Android mobile operating system, developed by the Open Handset Alliance led by Google. Google is all set to release the Android 13 version, codenamed ‘Tiramisu,’ in September 2022. 

Timeline of release dates

Four months after the release of Android 12’s stable version, Google announced the launch of Android 13 on the Android blog. Google released the first developer preview, soon after the announcement on February 10. In the following month, Google released the Developer Preview 2. Google released the Beta 1 version of Android 13 in April.

During the Google I/O event dated May 11, the search engine giant revealed the Beta 2 version. Google will release the upcoming beta versions by June and July, as platform stability is expected with Beta 3. The Beta 3.1 release adds a feedback app for developers.

Developer Productivity features of Android 13

Android 13 Beta versions released many new features. Some of the new updates in Android 13 are as follows: 

1. An exclusive user interface for copy-paste tasks
   
   A new UI will appear whenever a piece of content is added to the clipboard. The UI offers a preview of the content and provides visual confirmation of successful copying. The UI also has buttons to share the content directly or across connected devices like PC, tablets, screens, and more. 
   
   
2. A predictive back gesture
   
   With the predictive back gesture, pressing the back button in an app will display an intermediate window. The window will display a pop-up of the app running in the background and on the home screen. The feature will allow users to choose whether to stay on the existing app view or press the ‘back’ action.
   
   Users can toggle the feature on and off. The predictive back gesture also allows individual apps to design customized back navigation and functionality.
   
   
3. Customized theme app icons 
   
   The latest Android 13 users will be able to view theme-based app icons. Through this feature, apps will have the ability to display their icons in three different ways. Beyond the standard icons, apps running on Android 13 will have adaptive icons and monochromatic icons. 
   
   Application developers must develop themed icons to support this feature. Icons will match the color as per the user’s selection. When toggled, the theme launcher will adapt icons to the theme’s colors or the wallpaper’s shades.
   
   
4. Quick settings tile placement API
   
   Quick settings have been a convenient method to access simple toggle-based tasks and settings within the notification bar. Apps that have developed the custom tile service can use a new tile placement API with Android 13. A one-step system will allow users to add a custom tile with a dialog box without switching from the app.
   
   
5. Improved support for multilingual users
   
   Android 13 will allow users to change their language preferences for individual applications. Users can change the language of their mobile applications through system settings and custom in-app language selection. The feature will also display better text wrapping for the Japanese language.
   
   Android 13 will also enhance the line heights for non-Latin scripts such as Hindi, Tamil, and other languages. Android 13 adds significant updates to the Unicode library. The Japanese and Chinese text conversion API is improved to require fewer steps for completing tasks like searching for users.
   
   
6. Rendering support for color vector font
   
   Android 13 will support the COLR version 1 fonts rendering. The COLRv1 font format renders very fast, without compromising the quality, sharpness, and resolution. The COLRv1 format will also update the system emojis.
   
   
7. Bluetooth Low Energy audio
   
   The Bluetooth Low Energy (LE) audio allows users to receive Hi-Fi audio, without compromising battery life. Android 13 will provide in-built support for LE audio to compatible devices. 
   
   Bluetooth LE audio is a significant update from the Bluetooth classic, with multiple use cases for sharing, broadcasting, accessibility, and more.
   
   
8. MIDI 2.0 support
   
   Android 13 supports the MIDI 2.0 devices. Users can connect MIDI hardware through the USB port. Enhanced resolution for MIDI controllers, better non-western intonation, and per-note controllers are some new features that will accompany Android 13.

Privacy and Security updates in Android 13

Android 13 eliminates multiple vulnerabilities within the applications. Some of the privacy and security features in Android 13 are as follows:

1. Secure exports within context-registered runtime receivers
   
   Android 13 will prevent apps from sending unprotected broadcasts to dynamically-registered receivers of other apps. This feature enhances safety by reducing app vulnerabilities. Developers can configure each app’s receivers and select only the broadcasts that are allowed.
   
   The feature offers developers better control over the background data that is sent and received by applications. Additionally, the data exchanged between applications is further secured to prevent harmful applications from extracting sensitive data. 
   
   The context-registered runtime receivers can share broadcasted data across secure data channels. The data cannot be accessed by other apps, users, and hackers unless explicitly defined within the runtime code.
   
   
2. Photo Picker for apps and their images
   
   The Photo Picker in Android 13 will provide a standardized UI for individual apps and their pictures. Instead of permitting access to the entire media library, users can define which applications get access to which photos or images.
   
   
3. Revoke developer permissions
   
   Users can revoke previously-granted permissions for apps in Android 13. The API allows users to revoke unused permissions and allow ‘best privacy practices’ permissions for applications. 
   
   
4. New runtime permission for nearby Wi-Fi devices
   
   Android 13 updates the NEARBY_DEVICES runtime permissions for apps managing Wi-Fi access points surrounding the device. The apps must declare the NEARBY_WIFI_DEVICES permission while running the Wi-Fi APIs. Additionally, unless apps don’t request physical location from the Wi-Fi API, the ACCESS_FINE_LOCATION permission doesn’t need to be declared.
   
   
5. Better error reporting in Keystore and Keymint
   
   Keystore and Keymint provide detailed and accurate error indications for Android 13 applications that generate keys. An exception class is added under ‘java.security.ProviderException’. Retriable error codes and Android-specific exceptions, including error codes for Keystore and Keymint are added. Key generation, signing, and encryption methods can be modified.
   
   Google has updated the API Signature scheme with version 3.1 released for Android 13. With an expansive list of security updates, Android 13 will be more secure and reliable than the previous versions.
   
   Check out this post to learn more about Android 12: Snow Cone updates, and compare them with Android 13: Tiramisu’s new features. 
   

Other updates of Android 13

1. Programmable graphics: Android 13 offers Android Graphics Shading Language (AGSL) to control programmable shaders. AGSL creates ripple effects, animated shaders, blur, stretch overscroll, and other visual effects for android applications. The RuntimeShader enables developers to use the Android rendering engine to customize painting with Android Canvas.
2. Preferential audio routing: An AudioManager class within the audio routing API will help media applications identify audio routes. The API will allow users to exercise better control over which devices can play audio, and choose to play the audio streams directly.
3. System-wide audio description: With the Android 13 update, users can enable the audio description feature for all apps on their devices. The feature will ensure better accessibility of android devices for blind and visually impaired users. Developers will be able to access the code to implement this feature within their apps.
   The Kotlin and Java code for implementing these features is available on Android 13’s official website. Developers can implement the code snippet depending on the coding language their application is written in.
   
   Refer to this post for a comprehensive comparison of Kotlin vs Java .
   
   
4. OpenJDK 11: Android 13 will add core functionalities within its code, to adhere to the recent Open Java Development Kit 11 update.

Implementing Android 13 updates for your platform

A lot of coding, deployment, and testing will be necessary to implement these features across third-party applications. Companies will need to hire expert android developers to include these features in their applications. 

With Turing, companies can pick android developers from the top 1 percent in a talent pool of 1.5 million developers.  

Visit the Hire page to learn more about hiring the best android developers for your company. 

FAQs

1. Is there an Android 13?
   Yes, the recent update to the Android versions, Android 13: Tiramisu, will be officially launched around September 2022. The Beta 2 version has been already launched in the Google I/O meet on 11 May 2022. Android 13 will include a lot of new and exciting features. 
   
   
2. What is Android 13 called?
   Android Tiramisu is the internal codename of Android 13. The upcoming and unreleased phones like Google Pixel 7 and Google Pixel 7 Pro will be equipped with the Android 13 update. The rest of the android phones will receive this update towards the end of 2022 and early 2023.