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7 Everyday Applications of AI

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7 Everyday Applications of AI

If you Google searched for “artificial intelligence” and somehow came across this article, you just made use of artificial intelligence. Or, if you hailed a cab through an app like Uber or Ola, you just made use of artificial intelligence. The science of AI is all around us, in the smallest aspects of our lives. We take a look at how AI impacts our everyday lives in amazing ways.

Google Maps and Ride-Hailing Applications

Till only recently GPS (Satellite-based navigation) was guiding us through. But now artificial intelligence has come in to revolutionize the game, enabling systems like Google Maps to know exact directions, the optimal route and even road barriers and traffic congestion. Cab hailing apps have also made use of this technology.

Face Detection and Recognition

Making use of virtual filters when taking pictures and using face ID to unlock phones are two of the applications of AI in everyday lives. The former uses face detection and the latter uses face recognition. Smart machines are taught to identify facial coordinates in pictures of faces to enable face detection and recognition features.

Text Editors or Autocorrect

When we type out something onto a word document, inbuilt auto-correct tools begin perusing our script for spelling mistakes or grammatical anomalies.

Artificially intelligent algorithms also use machine learning, deep learning, and natural language processing to identify incorrect usage of language and suggest corrections.

Search and Recommendation Algorithms

Smart recommendations systems, that power our music applications and ecommerce websites, learn user behavior and interests from online activities.

“The personalized experience is made possible by continuous training. The data is collected at the frontend (from the user), stored as big data and analyzed through machine learning and deep learning. It is then able to predict your preferences by recommendations that keep you entertained without having to search any further,” a report says.

Chatbots

Answering questions can be time consuming, especially if they are coming from a customer. Chatbots are taught to impersonate the conversational styles of human beings through NLP (Natural Language Processing) so they can answer customer queries and take and track orders. They will give the impression of a customer representative when, in fact, they are just another example of artificial intelligence.

Digital Assistants

The latest digital assistants are well acquainted with human language and incorporate advanced NLP and ML. “They understand complex command inputs and give satisfactory outputs. They have adaptive capabilities that can analyze your preferences, schedules, and habits. This allows them to systemize, organize and plan things for you in the form of reminders, prompts and schedules.”

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Social Media

Various social media applications are using the support of AI to control problems like cyber crime, cyber bullying, and hate speech. “AI algorithms can spot and swiftly take down posts containing hate speech a lot faster than humans could. This is made possible through their ability to identify hate keywords, phrases, and symbols in different languages. These have been fed into the system, which has the additional capability to add neologisms to its dictionary. The neural network architecture of deep learning is an important component of this process.”

So, you see how AI had come to influence more aspects of our lives than we could have imagined. This essay was brought to you by DexLab Analytics. DexLab Analytics is a premiere artificial intelligence training institute in Gurgaon.

 


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How AI Is Facilitating Digital Marketing

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How AI Is Facilitating Digital Marketing

Artificial Intelligence has transformed the world of digital marketing by making it ultra intelligent and intuitive. Almost every platform used by the digital marketer is powered by some form of AI or an AI-powered machine learning model.

If we were to define what AI marketing is, according to a report by Forbes, it is a method of leveraging technology to improve the customer journey. It can also be used to boost the return on investment (ROI) of marketing campaigns.

How AI Works In Marketing Strategies

AI plays a very important role in eliminating guesswork when it comes to customer interactions online like in email marketing. Big Data Analytics, machine learning and other related processes gain insights into target audience behaviour. “With these insights, you can create more effective customer touch points.”

Moreover, it is gradually automating processes that were once dependent on human beings. Content generation, PPC ads, and even web design and video marketing are all possible applications for AI marketing.

Marketing Campaigns

AI, in the world of digital marketing, can streamline and optimize marketing campaigns. “It can also eliminate the risk of human error”. It acts as a support system to shore up efforts born out of human ingenuity with its data driven reports and analyses.

While AI might be able to launch a marketing campaign all on its own, human attributes like empathy, compassion and the art of storytelling are still needed to shape up the soul of an online marketing campaign.

Content Generation And Curation

“At present, content marketing has ballooned into a global industry. It’s so prevalent that some refer to it as the only type of marketing.” Moreover, AI powered content marketing strategies are also becoming a rage.

AI can be used potentially for both curating and generating content. Already, companies are using AI for automated content generation at a basic level. But in the long run, “AI could generate viable topics for writers, or even develop initial drafts of content based on certain parameters”.

Digital Advertising

AI is also gradually transforming the way businesses advertise. In fact, today’s digital advertising strategies all have a basic level of AI powered models processing them.

AI works with the help of algorithms in its systems. “These systems operate autonomously, placing the right kinds of ads in front of the right kinds of people based on complex algorithms and big data.” This feature service is known as “programmatic advertising.”

Chatbots

Chatbots have become the latest game changer when it comes to the marketing industry. They are the first interface customers encounter on many websites today, giving the website a human touch, excelling at answering customers’ frequently asked questions.

“The key fascination with chatbots is the impact they can have on the customer experience. For some businesses, there aren’t enough employees or hours in the day to answer customer queries quickly. Chatbots allow customers to help themselves.”

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Behavior Analysis And Predictive Analytics

More and more companies are beginning to hire data scientists and programmers for their marketing departments. There “are so many data sets (on the Internet) that humans alone can’t possibly hope to analyze them all.”

“Using machine learning and big data analysis, AI is able to provide businesses with deep insights into their customer(s’ behaviour). Not only will businesses be able to hyper-personalize interactions, but…they’ll also be able to predict future customer behaviours based on the data collected.”

For more information on AI powered systems, do peruse the DexLab Analytics website today. DexLab Analytics is a premiere institute that offers artificial intelligence certification in Delhi NCR.

 


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How AI is used in Banking

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How AI is used in Banking

Artificial Intelligence has revolutionized the banking sector, transforming back end processes into faster mechanisms, making money transfers safer and back-end operations more efficient.

From fraud detection to customer service chatbots, AI is powering several banking wings. Here is a list of operations AI has been facilitating in banks across the world.

Customer Support & Front Office

Millennials have rendered brick and mortar banks dispensable. According to Business Insider, 40 per cent of this generation does not use the services extended in physical bank offices.

AI has come to the rescue, however, by powering chatbots and voice assistants in most major financial institutions. Kasisto, for example, is one such neo-banking institution that has been using AI to the hilt.

“Kasisto’s major contribution is its conversational AI platform, KAI, which banks can use to build their own chatbots and virtual assistants. It’s rooted in AI reasoning and natural-language understanding and generation, which means it can handle sophisticated questions about finance management that other bank customer-service digital assistants…can’t”, says a report.

Kasisto has supported and shored up AI assistants for several reputed banking institutions including the UAE-based digital bank Liv., DBS Bank, Standard Chartered Bank and TD.

“The bank’s KAI-based bot walks customers through how to make international transfers, block credit card charges and transfer you to human help when the bot hits a wall.”

Fraud Protection & Middle Office

Artificial Intelligence has truly transformed “middle office functions” – where banks manage risk and protect themselves from bad actors. These functions include fraud detection, anti-money laundering initiatives and customer identity verification.

“And sometimes that means incorporating AI into legacy, rules-based anti-fraud platforms. But some the most innovative and secure countermeasures are other, from-the-ground-up models, built by companies like the ones below.”

“Up to $2 trillion is laundered every year — or five percent of the global GDP, according to UN estimates.” The sheer number of investigations across the globe, coupled with the complexity of data and reliance on human involvement makes anti-money laundering (AML) difficult work.

AML processes also cost a lot. Ayasdi’s AI-powered AML incorporates three key advancements: “intelligent segmentation, or optimizing the data-sifting process to produce the fewest number of false positives; an advanced alert system, which auto-categorizes alert priorities; and advanced transaction monitoring, which uses machine learning to spot suspicious anomalies”.

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Lending And Risk Management

“AI holds real promise for more equitable credit underwriting — as long as practitioners remain diligent about fine-tuning the algorithms. 

Beyond credit scoring and lending, AI has also influenced the way banks assess and manage risk and how they build and interpret contracts.”

Denying credit to persons because of a class or racial bias is something that ails the banking industry across the world. ZestFinance’s“AI-based software purportedly generates fairer models, essentially by downgrading credit data that it has “learned” results in unfair decisions”.

For more on this, do peruse the DexLab Analytics website. DexLab Analytics is the premiere most artificial intelligence training institute in Gurgaon.

 


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How AI is Helping Tackle Climate Change

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How AI is Helping Tackle Climate Change

While the spread of the COVID-19 pandemic has become a bane for economies across the world, slowing down or bringing to a halt markets and trade, the series of lockdowns declared by states has had a positive impact on the environment.

According to China’s Ministry of Ecology and Environment, data recorded between January and March 2020 reflects an 84.5 per cent increase in days with good air quality in 337 cities, and satellite data from the United States National Aeronautics and Space Administration shows a decline in nitrogen dioxide over China.

This piece of news is certainly welcome. Climate change is one of the biggest crises ailing our world today, with scientists and stakeholders worried. However, technological advancements like those in the field of Artificial Intelligence are to a large extant helping tackle the crisis of climate change. Here is how.

Improved climate predictions

At the intersection of data science and climate science is the piece of technology called climate informatics.

It includes areas like“improving prediction of extreme events such as hurricanes, paleoclimatology, like reconstructing past climate conditions using data collected from things like ice cores, climate downscaling, or using large-scale models to predict weather on a hyper-local level, and the socio-economic impacts of weather and climate.”

AI can also uncover new insights from the massive amounts of complex climate simulations generated “by the field of climate modeling, which has come a long way since the first system was created at Princeton in the 1960s.”

Better predictions can help officials make informed climate policy, allow governments to prepare for change, and potentially uncover areas that could reverse some effects of climate change.

Revealing the effects of extreme weather

AI is helping scientists reveal to common persons the effects of extreme weather conditions so they can work towards reversing the effects.

“To make it (the effects) more realistic for more people, researchers from Montreal Institute for Learning Algorithms (MILA), Microsoft, and ConscientAI Labs used GANs, a type of AI, to simulate what homes are likely to look like after being damaged by rising sea levels and more intense storms.”

This was done to inculcate in people habits that are eco-friendly and ecologically sustainable.

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Measuring sources of carbon

By monitoring coal plant emissions with satellite imagery, Carbon Tracker, an independent financial think-tank, can use the data it gathers to convince the finance industry that carbon plants aren’t profitable.

“A grant from Google is expanding the nonprofit’s satellite imagery efforts to include gas-powered plants’ emissions and get a better sense of where air pollution is coming from.”

AI can help make analysis of power plants images automated to get regular updates on emissions. “It also introduces new ways to measure a plant’s impact, by crunching numbers of nearby infrastructure and electricity use. That’s handy for gas-powered plants that don’t have the easy-to-measure plumes that coal-powered plants have.”

For more on AI and its algorithms or related sciences, do peruse the DexLab Analytics website today. DexLab Analytics is a premiere artificial intelligence training institute in Gurgaon, India.

 


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Application of AI in 8 Business Functions

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Application of AI in 8 Business Functions

Artificial Intelligence has made advancements in various sectors of the economy. But it has not yet taken the business world by storm. Business leaders, however, are excited about implementing AI in their companies’ business functions to start reaping its benefits. Here is a list of ways in which AI and machine learning will impact business functions across the globe.

Marketing

AI can assist in working out business strategies as well as implementing them. “Already AI sorts customers according to interest or demography, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it,” says a report. Also, AI is being used as a marketing tool in the form of chatbots. These chatbotshelp solve problems, suggest products or services, and support sales. Artificial intelligence also helps marketers build and make adjustments to marketing campaigns according to consumer behavior analyzed accurately by AI systems.

Sales

AI improves sales functions by improving forecasting, predicting customer needs, and improving communication.

Research and Development

AI can help analyze a large amount of information in industries like healthcare, pharmaceuticals, finance, and more. It can help us research problems and find solutions to them efficiently and accurately. “AI can automate many tasks, but it will also open the door to novel discoveries, ways of improving products and services as well as accomplishing tasks. Artificial intelligence helps R&D activities be more strategic and effective.”

IT Operations

Also known as AIOps, AI for IT operations is the application of AI and machine learning to IT operations in an organization. “AI is commonly used for IT system log file error analysis, with IT systems management functions as well as to automate many routine processes.”AI helps alert the IT team so they can fix problems before the IT systems crash. AIOps helps the IT component of businesses improve system performance and services.

Human Resources

AI can help human resource acquisition by effectively scouting for talented workers and prospective hires. “AI can help human resources departments with data-based decision-making and make candidate screening and the recruitment process easier. Chatbots can also be used to answer many common questions about company policies and benefits.”

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Contact Centers and Customer Experience

The contact centers of an organization serve as important points of data collection “that can be used to learn more about customers, predict customer intent, and improve the “next best action” for the customer for better customer engagement.” The unstructured data collected from contact centers can also be studies and analyzed by machine learning systems to uncover customer trends and then improve products and services. Also, AI helps improve customer experience by offering loyalty points to customers and recommending what they can shop for according to their preferences.

Manufacturing

Companies like Heineken use data analytics at every stage of the manufacturing process from the supply chain to tracking inventory on store shelves. “Predictive intelligence can not only anticipate demand and ramp production up or down, but sensors on equipment can predict maintenance needs. AI helps flag areas of concern in the manufacturing process before costly issues erupt.”

Accounting and Finance

Human finance professionals will be freed of repetitive tasks so they can focus on more serious activities while the use of AI in accounting will reduce errors. “AI is also able to provide real-time status of financial matters to organizations because it can monitor communication through natural language processing.”

To know more, do peruse the DexLab Analytics website. DexLab Analytics is a premiere artificial intelligence training institute in Gurgaon.


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Machine Learning Algorithms in Self-Driving Cars

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Machine Learning Algorithms in Self-Driving Cars

Machine Learning algorithms have revolutionized sectors like automation in ways one could have hardly imagined a few years ago. For instance, take the self-driving car. According to a report, with“the integration of sensor data processing in a centralized electronic control unit (ECU) in a car, it is imperative to increase the use of machine learning to perform new tasks. Potential applications include driving scenario classification or driver condition evaluation via data fusion from different internal and external sensors – such as cameras, radars, LIDAR or the Internet of Things.”

An expert explains how machine learning algorithms are used in autonomous cars. Supervised and unsupervised algorithms are used to perceive information through the car’s infotainment system. For instance, the system can relay information about the driver’s health status and direct the vehicle to a nearby hospital if something is found to be wrong. “This machine learning-based application can also incorporate the driver’s gesture and speech recognition, and language translation.”

The algorithms can be classified into two major categories on the basis of their learning ability- supervised algorithm and an unsupervised algorithm.

Supervised algorithms “learn using a training data­set, and keep on learning until they reach the desired level of confidence (minimization of probability error).” They can be sub-classified into classification, regression and dimension reduction or anomaly detection.

Unsupervised algorithms “try to make sense of the available data. That means an algorithm develops a relationship within the available data set to identify patterns, or divides the data set into subgroups based on the level of similarity between them.” Unsupervised algorithms can be largely sub­-classified into clustering and association rule learning.

The third set of machine learning algorithms falls somewhere between supervised and unsupervised learning. Reinforcement learning has sparse and time-­delayed labels – the future rewards. “Based only on those rewards, the agent has to learn to behave in the environment.”

One of the main tasks of any machine learning algorithm in the self­-driving car is continuous rendering of the surrounding environment and the prediction of possible changes to those surroundings. These tasks are mainly divided into four sub-­tasks:

  • Object detection
  • Object Identification or recognition
  • Object classification
  • Object localization and prediction of movement

Machine learning algorithms can be loosely divided into four categories: regression algorithms, pattern recognition, cluster algorithms and decision matrix algorithms. One category of machine learning algorithms can be used to execute two or more different sub­tasks. For example, regression algorithms can be used for object detection as well as for object localization or prediction of movement.

Regression Algorithms

This type of algorithm is used to predict events. “Regression analysis estimates the relationship between two or more variables, compare the effects of variables measured on different scales and are mostly driven by three metrics, namely:

  • The number of independent variables
  • The type of dependent variables
  • The shape of the regression line.”

Pattern Recognition Algorithms (Classification)

“In ADAS, the images obtained through sensors possess all types of environmental data; filtering of the images is required to recognize instances of an object category by ruling out the irrelevant data points. Pattern recognition algorithms are good at ruling out these unusual data points. Recognition of patterns in a data set is an important step before classifying the objects. These types of algorithms can also be defined as data reduction algorithms.”

Clustering

Sometimes the images gathered by the system are unclear and it is difficult to detect and locate objects in them. It is also possible that the classification algorithms may miss the object and fail to classify and report it to the system because the images are low-resolution, with very few data points or discontinuous data. “This type of algorithm is good at discovering structure from data points. Like regression, it describes the class of problem and the class of methods.” The most commonly used type of algorithm is K-­means, Multi-­class Neural Network.”

Decision Matrix Algorithms

“This type of algorithm is good at systematically identifying, analyzing, and rating the performance of relationships between sets of values and information. These algorithms are mainly used for decision-making. Whether a car needs to take a left turn or it needs to brake depends on the level of confidence the algorithms have on the classification, recognition and prediction of the next movement of objects.”

Check out the course structure at DexLab Analytics, a premiere artificial intelligence institute and machine learning institute in Delhi for more on the subject.


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The AI Revolution in The Education Sector

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The AI Revolution in The Education Sector

Artificial Intelligence (AI) is revolutionizing innumerable aspects of our lives, education being one of them. AI has transformed the way we learn, the relationship between the student and the teacher and the very manner in which our curriculum is perceived. This article, the third part of a series on the applications of artificial intelligence, delineates how AI has come to transform the education sector, as we know it.

The biggest contribution of AI to the education sector has been towards enhancing and streamlining the system of teaching students with varying needs across the spectrum, from elementary schools to adult learning centers. Students can be mentally developed in the left side of the brain with more analytical skills or they can be mentally developed in the right side of the brain with more creative and literary skills. Likewise, there may be students with different interests and passions. A strictly uniform curriculum does not suit all students of the same class because people differ in their learning ability and interests.

AI-Enabled Hyper-Personalization

AI is thus being used to customise curricula according to specific needs of each student of a single class. This is being done through the power of machine learning via a method called hyper-personalization. The AI powered system studies and examines the profile of a student and prescribes suitable curricula for her/him. According to a report, it is expected that by the year 2024 onwards, almost 50 percent of learning management tools will be powered by AI capabilities. These AI-enabled e-Learning tools will touch over $6 Billion in market size by 2024.

Smart Learning Tools

Machine Learning and AI are also defining the way hyperper sonalized and on-demand digital content is created to digitise the learning environment. Now students do not have to rote-learn chapter after chapter from textbooks. They are absorbing learning material in the form of condensed bits of information in the form of smaller study guides, chapter summaries, flashcards, as well as short smart notes designed for better reading and comprehension. Learning is therefore becoming gradually paperless. AI systems also have an online interactive interface that helps in putting in place a system of feedback from students to professors regarding areas they are facing trouble understanding.

Digital Conversations

AI systems are also being used to develop the system of tutoring with personalized conversational education assistants. These autonomous conversational agents are capable of answering questions, providing assistance with learning or assignments, and strengthening concepts by throwing up additional information and learning material to reinforce the curriculum. “These intelligent assistants are also enhancing adaptive learning features so that each of the students can learn at their own pace or time frames”. 

Adoption of Voice Assistants 

In addition, educators are relying heavily on using voice assistants in the classroom environment. Voice assistants such as Amazon Alexa, Google Home, Apple Siri, and Microsoft Cortana have transformed the way students interact with their study material. In the higher education environment, universities and colleges are distributing voice assistants to students in place of traditionally printed handbooks or hard-to-navigate websites.

Assisting Educators

AI powered systems are not only helping students with course work, they are also empowering teachers with teaching material and new innovative ways to educationally express themselves. It is easier to explain a theory with the help of picture cues and graphical representation than mere definitions. The Internet has become a treasure trove of teaching material for teachers to borrow from. Also, teachers are burdened with responsibilities “such as essay evaluation, grading of exams…ordering and managing classroom materials, booking and managing field trips, responding to parents, assisting with conversation and second-language related issues…Educators often spend up to 50% of their time on non-teaching tasks.”AI powered systems can help streamline these tasks and handle repetitive and routine work, digitise interaction with parents and guardians and leave educators with more time to teach students.

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When it comes to higher learning, in India at least, more and more artificial intelligence and machine learning institutes are opening up. DexLab Analytics is a premiere artificial intelligence course in Delhi that trains professionals in both AI and machine learning.


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The link between AI, ML and Data Science

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The link between AI, ML and Data Science

The fields of Artificial Intelligence, Machine Learning and Data Science cover a vast area of study and they should not be confused with each other. They are distinct branches of computational sciences and technologies.

Artificial Intelligence

Artificial intelligence is an area of computer science wherein the computer systems are built such that they can perform tasks with the same agility as that done through human intelligence. These tasks range from speech recognition to image recognition and decision making systems among others.

This intelligence in computer systems is developed by human beings using technologies like Natural Processing Language (NLP) or computer vision among others. Data forms an important part of AI systems. Big Data, vast stashes of data generated for computer systems to analyze and study to find patterns in is imperative to Artificial Intelligence. 

Machine learning

Machine learning is a subset of artificial intelligence. Machine learning is used to predict future courses of action based on historical data. It is the computer system’s ability to learn from its environment and improve on its findings.

For instance, if you have marked an email as spam once, the computer system will automatically learn to mark as spam all future emails from that particular address. To construct these algorithms developers need large amounts of data. The larger the data sets, the better the predictions. A subset of Machine Learning is Deep Learning, modeled after the neural networks of the human brain.

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Data Science:

Data science is a field wherein data scientists derive valuable and actionable insights from large volumes of data. The science is based on tools developed with the knowledge of various subjects like mathematics, computer programming, statistical modeling and machine learning.

The insights derived by data scientists help companies and business organizations grow their business. Data science involves analysis of data and modelling of data among other techniques like data extraction, data exploration, data preparation and data visualization. As data volumes grow more and more vast, the scope of data science is also growing each passing day, data that needs to be analyzed to grow business.

Data Science, Machine Learning and Artificial Intelligence

Data Science, Artificial Intelligence and Machine Learning are all related in that they all rely on data. To process data for Machine Learning and Artificial Intelligence, you need a data scientist to cull out relevant information and process it before feeding it to predictive models used for Machine Learning. Machine Learning is the subset of Artificial Intelligence – which relies on computers understanding data, learning from it and making decisions based on their findings of patterns (virtually impossible for the human eye to detect manually) in data sets. Machine Learning is the link between Data Science and Artificial Intelligence. Artificial Intelligence uses Machine Learning to help Data Science get solutions to specific problems.

The three technological fields are thus, closely linked to each other. For more on this, do not forget to check-out the artificial intelligence certification in Delhi NCR from DexLab Analytics.


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The Four Important Machine Learning Algorithms in Use

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The Four Important Machine Learning Algorithms in Use

Machine Learning, a subset of Artificial Intelligence, has revolutionized the business environment the world over. It has brought actionable insights to business operations and helped increase profits acting as a reliable tool of business operations. In fact, its role in the business environment has become almost indispensable, so much so that machine learning algorithms are needed to maintain competitiveness in the market. Here is a list of machine learning algorithms crucial to businesses.

Supervised Machine Learning Algorithms

Supervised Learning involves those algorithms which involve direct supervision of the operation. In this case, the developer labels sample data corpus and sets strict boundaries upon which the algorithm operates, says a report.

Here human experts act as the tutor or teacher feeding the computer system with input and output data so the computer can learn the patterns.

“Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships which it learned from the previous data sets,” says another report.

The most widely used supervised algorithms are Linear Regression; Logistical Regression; Random Forest; Gradient Boosted Trees; Support Vector Machines (SVM); Neural Networks; Decision Trees; Naive Bayes; Nearest Neighbor. Supervised algorithms are used in price prediction and trend forecasting in sales, retail commerce, and stock trading.

Unsupervised Machine Learning Algorithms

Unsupervised Learning is the algorithm which does not involve direct control of the developer or teacher. Unlike in supervised machine learning where the results are known, in the case of unsupervised machine learning algorithms, the desired results are unknown and not yet defined. Another big difference between the two is that supervised learning uses labelled data exclusively, while unsupervised learning feeds on unlabeled data.

The unsupervised machine learning algorithm is used for exploring the structure of the information; extracting valuable insights; detecting patterns; implementing this into its operation to increase efficiency.

Digital marketing and ad tech are the two fields where Unsupervised Learning is used to effectively. Also, this algorithm is often applied to explore customer information and mould the service accordingly.

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Semi-supervised Machine Learning Algorithms

Semi-supervised learning algorithms represent features of both supervised and unsupervised algorithms. In essence, the semi-supervised model combines some aspects of both into a unique aspect of itself. Semi-supervised machine learning algorithm uses a limited set of labelled sample data to train itself. The limitation results in a partially trained model that later gets the task to label the unlabeled data. Due to the limitations of the sample data set, the results are considered pseudo-labelled data, says a report. Lastly, labelled and pseudo-labelled data sets are combined with each other to create a distinct algorithm that combines descriptive and predictive aspects of supervised and unsupervised learning.

Semi-supervised learning uses the classification process to identify data assets and clustering process to group it into distinct parts.

Legal and Healthcare industries, among others, manage web content classification, image and speech analysis with the help of semi-supervised learning.

Reinforcement Machine Learning Algorithms

Reinforcement learning represents what is commonly understood as machine learning artificial intelligence.

In essence, reinforcement learning is all about developing a self-sustained system that, throughout contiguous sequences of trials and errors, improves itself based on the combination of labelled data and interactions with the incoming data. The method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk.

Most common reinforcement learning algorithms include: Q-Learning; Temporal Difference (TD); Monte-Carlo Tree Search (MCTS); Asynchronous Actor-Critic Agents (A3C).

Modern NPCs and other video games use this type of machine learning model a lot. Reinforcement Learning provides flexibility to the AI reactions to the player’s action thus providing viable challenges. Self-driving cars also rely on reinforced learning algorithms.

For more on Machine Learning courses in Delhi, check out the DexLab Analytics course structure today.


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