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Tag: Machine Learning

Top 5 Courses From Imarticus That Empowers Women!

Posted on by Imarticus Learning
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The 21st century is all about progressiveness; and one of the most important ways, by which this is ensured, is by women empowerment. Throughout history, we have seen the oppression of women. However, in this new age, we strive for equality. Education is an integral part of advancement; and thus, we will venture into the top 5 courses provided by Imarticus which empowers women.

Post Graduate Program in New Age Banking

 

Banking and Finance CareerCurrently, job trends in the market are thriving in the field of banking. Hence, comprehensive Banking and Finance Courses can be extremely beneficial for women.

The Post Graduate Program in New Age Banking course of Imaticus in the banking sector ticks all the boxes as it provides two great courses – an 11-month PGP in new-age banking and a 2-year NMIMS PGDBM course in Banking and Finance Management. Added benefits from Imaticus involve a dual certification along with excellent placement with lucrative increments.

Professional Certificate in Fintech 

Finance and Technology go hand in hand; and in this modern age, this integration of technology with finance plays a major role in the Finance industry.

Fintech CoursesHence, a great opportunity for any woman would be to take a course in Fintech online training. Going forward, this comprehensive FinTech course of Imaticus can open new horizons in one’s life by involving one with Cloud Computing, Blockchain, Machine learning, etc.

Post Graduate Program in Data Analytics

This fast-paced world relies on data; and hence, one of the major subjects which is currently in the limelight is Data Science.

Career in AnalyticsImarticus provides a PGP Data Analytics course that can help one to join a leading MNC and hold the company’s helm. With a huge job demand in the field of data science, this can be an excellent opportunity for someone who is a fresher or has a nominal experience of three years to learn tools like Python, SQL, PowerBI, and Hadoop.

Post Graduate Program in Analytics & Artificial Intelligence

With the world revolutionizing at a fast rate, Artificial Intelligence is going to the new normal in the coming years.

Data Analytics and Artificial Intelligence Courses We have already seen the use of AI in computers and mobiles so why not study something which will be mainstream in the coming years? A comprehensive Analytics and Artificial Intelligence course provided by Imaticus along with dual certification can bring lucrative opportunities to your doorstep.

Post Graduate Program in Digital Marketing

The modern age deals comprehensively in digital media like social media platforms, and owing to the rise in social media outreach, digital marketing has taken the job market by storm.

Digital MarketingFor a woman who is determined to take a step forward towards success, a Digital Marketing Training course provided by Imarticus Learning will be perfect. It will not only provide a good and secure job but will also take one’s Digital Marketing Career to new heights.

Conclusion

This new fast-paced world waits for none. In these present times, a woman must be very tactful in correctly choosing her goals so that she can chase her dreams. Women empowerment can only be possible by proper education and only if women take up more jobs in high places and thus be an inspiration for other fellow women.

The Art of Machine Learning!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Machine learning is the application of Artificial Intelligence (AI) that enables systems to learn automatically and improve from experience without being programmed directly. Its main focus is on the development of programs that access data and use the same for self-improvement.

The machine learning process starts with observing data to look for similar patterns in any form and make better decisions in the future based on these trends. The main aim is to enable the computers to learn automatically and adjust actions accordingly without human intervention or assistance.

Data mining and predictive modeling involve similar processes as machine learning. Both these methods involve searching through data to look for patterns and then adjusting the program actions according to those patterns.

A common example of machine learning for people is shopping on the internet and being served ads related to it. This happens because online ad delivery is personalized almost in real-time by recommendation engines using machine learning.

Along with personalized marketing; detection of fraud, spam filtering, network security threat detection, predictive maintenance, and building news feeds are other common machine learning use cases.

Some machine learning methods
Machine learning algorithms are often categorized as supervised or unsupervised.

  • Supervised machine learning algorithms can apply past learnings to new data with the use of labeled examples to predict future events. It starts with the analysis of a known training dataset based on which the learning algorithm produces an inferred function to make predictions about the output values.Targets are provided by the system for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
  • Unsupervised machine learning algorithms – These types of algorithms are used when the information used to train is neither classified nor labeled. Unsupervised machine learning enables you to understand how systems can infer a function to describe a hidden structure from unlabeled data. The output given by the system is not right, but it explores the data and can draw conclusions from datasets to describe hidden structures from unlabeled data.
  • Semi-supervised machine learning algorithms have qualities of both supervised and unsupervised learning since they use both labeled and unlabeled data for training. Mostly, these algorithms use a small amount of labeled data and a large amount of unlabeled data. Learning accuracy is considerably improves in systems using this method.When the acquired labeled data requires skilled and relevant resources in order to train it or learn from it, semi-supervised machine learning is used. Otherwise, additional resources are generally not required for acquiring unlabeled data.
  • Reinforcement machine learning algorithms is a learning method that collaborated with its environment by delivering actions and finds errors or rewards. The most pertinent characteristics of reinforcement learning are trial and error search and delayed reward.Machines and software agents can automatically determine the ideal behavior within a particular context in order to maximize their performance using this method of machine learning. Simple reward feedback is required for the software specialist to learn which action is best and is known as the reinforcement signal.

Large quantities of data can be analyzed using machine learning. It identifies profitable opportunities or dangerous risks by delivering faster, more accurate results; however, it may also require additional time and resources to train it properly.

Large volumes of information can be processed more effectively if machine learning is combined with AI and cognitive technologies.

For example, Facebook’s News Feed customizes each user’s feed with the help of machine learning. If a user frequently likes or shows any activity on a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the user’s feed.

At the backend, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate his/her News Feed. If the user no longer shows interest to read, like, or comment on the friend’s posts, that new data will be included in the dataset and the News Feed will update accordingly.

The Role of AI in Minimising Physical Contact in Public Spaces!

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The novel coronavirus pandemic has forced a majority of countries around the world to enforce lockdowns. Although met with initial resistance, a large chunk of the global population has stuck to social distancing and shelter-in-place norms, allowing the curve to be flattened.

As countries now begin to emerge out of lockdowns in phases, the focus will turn to maintain high standards of sanitation and hygiene. This is to avoid undoing the work that has been done over the past few months as well as set new norms for effective mitigation and disease controls. Amongst these, processes to minimize the frequent touching of common surfaces in public spaces will certainly feature.

So far, however, all efforts have been wholly dependent on manual efforts and individual dedication to social distancing and mitigation. AI can be pivotal in the efforts to curb the touching of surfaces in public areas without banking on individuals entirely.

Here’s how:

  • Contactless Access Systems

Tech titans are currently exploring the use of technologies for facial recognition to monitor the social distance between staff members. These can also be taken one step further to be combined with thermal scanning; when paired, this system can regulate who enters and exits the front doors in just a few seconds.

Machine Learning

This system also negates the need for touch-and-go biometric scanners or ID scanners which often become a collection point for employee throughout the day. Artificial Intelligence can be used to virtually cordon off some parts of the office as well as maintain control over how many times a person touches their face in a day (which is one of the quickest methods of COVID19 transmission).

  • Leveraging Voice Commands

Voice functionality has penetrated many aspects of human lives– and it’s only set to increase. Voice commands can be used to operate systems in public spaces such as bathrooms, elevators, entryways and cubicles to minimize the risk of contact. It can also be implemented at the water cooler, in the printing room and in office pantries, which are often places that see the highest footfall in large-scale organisations. Voice functionality can be implemented by integrated voice assistants and or smartphone apps. Aside from voice commands, gestures can also be used to minimizing the frequency of touching high-risk surfaces such as flushes, taps, door handles and elevator buttons.

  • Smart Handles and Locks

Doorknobs and handles are high-priority areas for sanitation teams given that we subconsciously handle them every day. AI can be implemented to reduce the need to physically touch handles to open doors. Technology can be used to kick into motion self-locking or gesture-controlled mechanisms. In a case where physical touch is absolutely required, AI can also be used to trigger the dispensing of antibacterial coatings or single-use sanitary sleeves. Newer inventions that use these technologies are able to be retrofitted onto existing doorknobs and handles, making them a quick fix to the sanitation problem in this aspect.

  • Location and Distance Tracking

Although some industries are slowly opening up, others have seen an influx of workers considered essential. However, that doesn’t reduce the need for strict social distancing measures, which is where AI comes into the picture. Artificial Intelligence can be used to account for the location of every employee in the facility and alert them if they have crossed social distancing boundaries.

Additionally, AI can also be used to demarcate spaces in queues and cubicles to maintain distance between employees. This system can be implemented through smartphone apps or wearable devices such as smartwatches.

Conclusion

Even after the pandemic loosens its hold, social distancing is slated to become the new norm. Businesses looking to leverage AI to maintain these rules without manual labour can consider upskilling their IT team through an artificial intelligence course or Machine learning training to ensure they’re achieving their potential.

Thoughts on Cybersecurity in The COVID-19 Era!

Posted on by Imarticus Learning
Reading Time: 2 minutes

It is not possible to name one sector that has not been affected by the Covid-19 epidemic. Around the world, sectors including cybersecurity have taken massive hits in how they perform. A clear sign of this is the growing number of cybersecurity cases involving online fraud, scams, and phishing.

Let’s take a brief look at how the Covid-19 outbreak has affected the world of cybersecurity.

Growing Number of Cyber Issues

Earlier in 2020, someone decided to sell the Statue of Unity online. Then there were attempts to dupe people who were trying to donate to the PM Cares fund. This was done by using fake UPI handles and extorting money from unsuspecting givers.

In India, cyber financial crime is not a new topic. But the uptick in the number of such cases is something to be noted. As more and more people quarantine themselves and try to work from home, there has been a rise in online transactions. Whether it is to pay phone bills or to buy essential groceries. This sudden shift to online transactions (more than what it was in the pre-covid era) has given rise to this uptick.

People around the world are in a state of desperation and anxiety, which fraudsters take advantage of. So, for many people, while transacting online, there is a lack of extra attention that they would have otherwise paid.

Government to the Rescue

Most of these cyber cases are a result of lack of caution on the part of the user. However, the government and its associated bodies are scrambling to further contain this increase in cyber attacks.

According to Medianama, the Indian central government has made sure that certain sectors get heightened security in this period. This is to prevent them from falling victims to online fraud or hijacking, which can further have a negative impact on normal life in the country.

These sectors are transport, government, telecom, financial services, power and energy, and strategic and public enterprises.

Combining Public and Private

The epidemic has also shifted the attention to the ongoing debate of public and private companies working together. Private organizations have the resources to help public companies prevent cyber abuse. This is in the form of VPNs and other advanced cyber machinery that can help weed out fraudsters from even entering a system.

As is known that small preventive steps can help avoid huge cyber disasters, naysayers are also finally accepting the idea of a possible collaboration between the public and the private.

Importance of Cyber Knowledge

One thing that has come to the centre of the discussion is that such situations reinforce the idea of how important basic cybersecurity knowledge is. It is up to each user to exercise caution while transacting online.

It can be as simple as setting different passwords across platforms, not responding to fake or fake-looking messages, and limiting the use of online payment methods. A bit of supervision in these activities can help avoid disasters.

Cybersecurity is one of the most vulnerable sectors in this age of corona virus. It is our collective responsibility to prevent it from harming the already-bleeding economy.

Use of Machine Learning in Social Cause!

Posted on by Imarticus Learning
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Watch Vinay Borhade, Founder and Director of AIQuest Solutions(LLP), Former Sr. Manager-Bank of America discusses how Machine Learning is used in Social Causes today. He goes into detail and shares some examples of its uses in water crisis, climatology, renewable energy, crisis management, and health nutrition.

Imarticus Learning is India’s leading professional education institute, offering certified industry-endorsed training in Financial Services, Investment Banking, Business Analysis, IT, Business Analytics & Wealth Management.

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#KnowledgeBytes: Artificial Intelligence – Customer service Trends!

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In this Imarticus Learning video, Jasjeet Kaur – Head, Western Region, explains how Artificial Intelligence acts as a game-changer in customer service in today’s world. She wonderfully explains to us with an example the fact that with Artificial Intelligence operations become faster, more effective, cheaper, yet more human.

Virtual assistants are no more a mere concept that we can only experience in Hollywood movies, but it has become a part of our daily lives today. Siri and Alexa are some of the most relatable examples of the rise of virtual assistants.

Jasjeet further elaborates on how the Internet of Things (IoT) will help the product companies to provide proactive service for high-end products. Robotic process automation reduces human efforts by overtaking the tasks learned through repetitive actions and performing them in a better way over a period of learning. Jasjeet tells us how companies can use Digital Interactions to transform their customer service.

It enables deeper interaction in the physical world without physical presence. Jasjeet says that all the above aspects lead to the emergence of super agents.

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Why Imarticus?

Imarticus Learning offers a comprehensive range of professional Financial Services and Analytics programs that are designed to cater to an aspiring group of professionals who want a tailored program on making them career ready. Our programs are driven by a constant need to be job relevant and stimulating, taking into consideration the dynamic nature of the Financial Services and Analytics market, and are taught by world-class professionals with specific domain expertise.

Headquartered in Mumbai, Imarticus has classroom and online delivery capabilities across India with dedicated centers located at Mumbai, Thane, Bangalore, Chennai, Pune, Hyderabad, Coimbatore, and Delhi. For more information, please write back to us at info@imarticus.org

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AI in the FinTech Industry: What Will 2020 to 2025 Look Like?

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The financial industry has, for long, been keen followers of technological advancements for their own benefit. Many big names in the industry have been early adopters of disruptive technologies in a bid to streamline processes, reduce manual labor and negate the chances of error.

Artificial intelligence is a paradigm-shifting field that the financial industry has forayed into very recently, sitting still at the tip of the iceberg. Here is a breakdown of the trends, growth and scope of Artificial intelligence Training in the FinTech industry during the years to come.

AI in FinTech: Global Market Share, Size and Investment Analysis

In 2019, the AI in the FinTech market was estimated at USD7.2 billion. By 2025, this figure is expected to reach a staggering USD35.40 billion, according to a Mordor Intelligence report. The Compound Annual Growth Rate (CAGR) has been put at 31.5% for the years between 2020 and 2025.

This double-digit surge is no doubt a result of exponential technological advancements and deeper penetration of the internet. Software tools are expected to receive the largest market share because the need of the hour, and the foundation of all further processes, is the extraction of data.

When it comes to deployment, cloud-based AI developments are expected to rake in the highest CAGR in the following years when compared to on-premise deployments. This goes hand in hand with the shift in data storage and management from on-site servers to remote, centrally-controlled cloud silos to facilitate better access and higher security.

Regionally, AI in FinTech is gaining traction across many geographical splits. The current largest market is North America; however, Asia Pacific is expected to see the fastest growth in the coming years. This comes off the back of massive research and development investments in developed economies in the United States and Canada. Europe, South America, Africa and the Middle East will also see a surge in AI adoption and advancements, though perhaps not at the scale of Asia Pacific as yet.

AI in FinTech: Trends and Growth

Fraud prevention: AI is expected to be deployed the most to ensure fraud detection and prevention. Naturally, this segment will drive most of the IT expenditure in companies of varying sizes. This trend appears in a bid to keep up with the changing face of fraud in the FinTech industry as well as the greater proliferation of digital channels and the need to secure them all.

Transactional bots: As financial entities solidify their online presence, transactional bots and digital assistants will increase to keep up with remote demands. Apart from managing customer relationships, these assistants will also be equipped to deal with term life renewals, cheque or balance notifications, withdrawal limit warnings and more.

Risk profiling: AI will become a massive driving force in evaluating client credit risk and creating profiles. Using historical client data and outliers, logical algorithms can segregate risks by range, allowing advisors and risk managers to make more accurate mitigation decisions.

AI in FinTech: Challenges

Cultural changes: With changing landscapes and evolving customer demands, cultural shifts within the company are inevitable. Employees at all levels must be reoriented so that the introduction of AI becomes helpful rather than disruptive.

Security: Increased exposure to digital forums, ironically, also means being laid bare to cyber-threats. While adopting artificial intelligence in any form, financial entities must strengthen security systems at the same time.

The final word

In light of the changes to come, it is imperative that new-age students enroll in a FinTech online course that encourages deeper thinking. With every shift in the level of computational power, FinTech industry leaders will be seen integrating beyond-human technologies into nearly every critical stage of their operations. The leaders of tomorrow, then, will benefit from a FinTech online course that preps them to make and implement these changes with minimum disruption and maximum confidence.

What Are Important Ways That AI Is Helping E-Commerce Stores?

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The Ecommerce Industry

The e-commerce industry has proved to be a boon for all the shopaholics who are too lethargic for a regular brick and motor engagement. Growing in double digits the expansion in the e-commerce industry is unmatched by any other and with the potential to grow multiple folds in the coming years it has set new highs.

In a broad sense of things, the concept behind the e-commerce world is simple, creating on the online market place with multiple stores available to shop anytime using the means of smartphones and other computerized devices that support web surfing.

The virtual market is not bounded by geography, having its customer base all across the world. What’s different about this shopping escapade is that it makes the entire store available for you to facilitate your shopping spree, all with a few clicks. I wonder how many times it happens that I am not sure about what exactly I need to purchase unless acquainted with the varieties available.

Now if we have to walk by several stores to find out what could be bought it will be tiresome, to say the least. Let’s assume that we somehow managed to step into each of them, how will we compare all the available products in real-time? That’s where the e-commerce industry adds value and steals the show with convenience.

The e-commerce stores not only help to bring everything together but also helps to search select and choose by providing valuable suggestions and insightful product descriptions. It also lets you read into the feedback provided by the users of the products that might help you buy better.

In the tangible world, we have a shop for every need, we have shopping complexes for multiple segments. This evolution went a little further in the era of the internet with e-commerce where we have all the product segments from all the known brands under a few keystrokes.

AI applications in the e-commerce industry

While shopping at stores with a physical address on the map, what attracts the most apart from quality goodies is the presentation and organization of the products.

Similarly when buying goods online what helps increase engagement and purchase? The answer is better to search for tools and classified product segments. This is where AI fits into the e-commerce must-have tools.

The high-tech AI-enabled solutions can also help in searching product descriptions and other relevant details to form a variety of keywords that might match the user’s search and help discover the product better. This doesn’t stop here, the AI-powered solutions also help with product selection by asking some intelligent questions and narrowing down the list for us.

At times it so happens that we know what we are looking for but the name is unknown to us and thus we feed in a variety of keywords to complete our search. The predictive search mechanism provided by Artificial Intelligence training uses the past search and purchases history helping us identify what we might be looking for with relative ease saving a lot of time and keystroke efforts.

Arrangement of products and tidiness are some of the key drivers of customers in the traditional brick and motors store, how do you implicate this approach online? Well, the answer doesn’t require a brainstorming session, it is through the website design.

Making the website aesthetic needs a well-planned web design that not only looks good but also goes along with the objective of the website. From optimized website design testing to improving decisions with auto traffic analysis & better sales funnel structuring, AI delivers on all aspects of customer conversions and engagement.

In present-day scenario conversational chatbots are mainstream for better customer servicing, it could also be seen as a norm, whatever site you visit for your purchase you are bound to be greeted by a bot. This evolution has propelled further with a new wave of intelligent sales chatbot. This new AI by-product is hyper-personal in their functioning, providing customized recommendations and suggestions for better conversion.

Conclusion

AI has improved the e-commerce industry to a great extent by providing better search options for product searches to suggesting an optimized website layout for better conversions. Apart from the mainstream chatbots for customer servicing this new AI wave has welcomed the trendy sales chatbot that uses customer preferences data for good by providing customized and hyper-personal shopping experience.

What Machine Learning Has To Do With Your Personal Finances?

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What Machine Learning Has To Do With Your Personal Finances?

Machine learning is a subset of AI technology that develops complex algorithms based on mathematical models and data training to make predictions whenever new data is supplied to it for comparison. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence through machine learning courses without the use of any human intervention or explicit programming.

Though these two concepts that always go together have been around for ages, the past two decades have seen a phenomenal rise and exploitation of benefits of ML applications.

Let us explore some applications in real-life in the financial services area where they have made huge differences in customer service, fraud and risk management, and last but not least personal finance.

Examples in Customer Service:
Chatbots are the latest feature of financial services being deployed to aid and automate and reply when asked frequently asked questions, common customer service answers and requests, help in bill payments, provide information on services and products and more.

Since they work with NLP-natural language processing they understand the query and answer appropriately. But there are instances when the scenario does not fit the scripted questions and the conversation is beyond their comprehension.

ML is important to teach the chatbots in customer service to assimilate data from interactions where the AI can self-learn how to respond in the future based on the experience they gather. Obviously more the interactions, the better they get.

They are also capable of recognizing emotions like frustration, anger and so on where they can diffuse the tensions by transferring to a live customer service agent for further help or resolution. Often they up-sell products, introduce the newer services and help in transactions like making automated payments.

During the course of such interactions, they can also pick up customer behavior trends like the possibility of defaults due to cash-flows. Imagine how satisfied a customer would be when it is the due date for payment, the account is bereft of money and the chatbot work efficiently offers a different due date, a short-term loan or a customized payment plan.

That’s just a small example of the chatbot and its machine learning courses enriching the customer or user experience.

Examples in Personal Finance:
ML comes to the aid of financial institutions by specializing in the service of customers needing applications for budget management, offering guidance and highly targeted financial advice. Such apps are made for mobile devices and allow their clients to track their daily spending.

Using their innate ability to spot trends they can help with budgeting, saving and investment decisions and plans by watching and learning from the client’s spending and purchase patterns.

Ina real-life example a leading bank spotted the trend of people from a certain segment facing problems with their cash flow and using their credit cards for late-night transactions and withdrawals. By flagging such abnormal behaviour it was found that the segment faced unduly low-interest rates in their savings accounts. Based on such foresight the bank not only improved its savings rates but it also offered the segment increased credit limits to restrict defaults on payments.

ML intelligence worked very well since the bank retained its customers with such an offer and also saw an increase in its savings accounts deposits.

Examples in Fraud and Risk Management:
In the fields of risk and fraud management the daily number of transactions to be scanned, are very large and involve huge sums of money. In modern times online payments have emerged as an ideal spot for fraud perpetration. Paypal the market leaders, have employed machine learning courses specializing in risk management and fraud detection and using Big Data, complex neural networks, and deep learning capabilities. Any abnormal behavior is flagged and forms a sandboxed risk queue within milliseconds.

The cybersecurity challenges are confrontable by smart ML algorithms. The detection of phishing attacks is dependent on the algorithm being able to easily compare the original and fake sites for logos, visual images, and site components. T

hey can also detect unusual behavior once they are trained on recognizing normal patterns on a profile or account. A red flag is immediately raised and the user is asked to verify the transaction.ML is also used in risk scoring, assessing defaults in payments, automating credit scores and compliance issues, assessing loan applications and every transaction in between.

In conclusion:
Machine learning is not restricted to any one field. However, the applications can get very complex and extend far beyond these few examples. ML helps in better security, increasing operational efficiency and delivering better customer service or user experience.

If you would like to learn more, then do the machine learning courses at the Imarticus Learning Institute where technologies of tomorrow are taught and skilled for today.

Bots In Learning AI And Personalized Learning Experience

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Bots In Learning AI And Personalized Learning Experience

The class sizes keep increasing with compulsory education and teachers are often facing many challenges in giving attention and help to the large numbers of students. A big challenge like this has been simplified by incorporating computer programs that allow each student to follow his own pace and learning curve.

Since the ideal teacher-student ratio has long been overtaken, a lot of educational instructors have unobtrusively introduced AI and ML to help with self-scoring assignments, computer-aided assignments and course review modules and videos that help the learning process which tends to be different in style, pace, and manner of learning in each individual student.

However such early initiation has led to students thinking of the quickest and easiest way to beat the system. This was supposed to be a part of the personalized learning process which probably needs a review given that AI and a machine learning course have a huge role to play in the future of technologies.

Learning Bots:

The newer methods of experiential learning at educational institutions use advanced techniques of AI, machine learning and deep learning in instructing and teaching like Chatbots and learning bots.

A few examples of such learning bots are:

  • Botsify is a suite of bots that have bot assistants like the tutoring bots, FAQ bots and more.
  • Mika is a math bot tutor based on AI used widely in schools and higher education institutions.
  • Snatchbot helps administrators and teachers with templates to help customize a bot to the classroom needs and subjects.
  • Ozobot is a specialized coding bot.

AI has thus personalized the teaching and learning experience by incorporating a machine learning course for bots to enable their functioning in the field of education and instruction.

Learning supports with AI:

Individualized learning modules can help find knowledge gaps and personalize the learning materials to fill in the gaps. By so adjusting the learning rate no student in a class is way ahead or too far back on the learning curve. Since learning styles, rates and methods may vary over each student, adaptive learning scores by understanding and identifying the gap in learning and taking corrective action before it is too late.

A differentiated AI style of learning deals with the most effective style to help the student learn. Adaptive AI-based learning curates the learning exercises matching them to the student’s needs and knowledge gaps. Competency-based AI and machine learning course tests aid the students to gauge their learning levels and progress from thereon. Using all these three types of learning AI can test how well the students can adapt their learning to applications of it and thus promote the progress of students based on individual interests.

Tutoring help:

The bots have become extremely popular and the future will probably have specialized tutoring bots where the learners can ask questions and receive answers in real-time. Chatbots, tutoring bots and even bots for teachers to help score examinations, assess large volumes of answer sheets and more are being used to improve the learning and educational process. Tweaking the earlier bots have led to specialized bots that even suggest and provide resources specific to a learning style.

Administrative tasks aids:

Teaching is a challenge and scoring and grading are tasks that are repetitive and time-consuming. Multiple choice questions and online testing are AI forms of grading already in use where learning responses need not be essentially written responses. Thus a lot of paperwork and unnecessary wastage of time is eliminated.

Since bots are able to quickly analyze the responses, feedback can be near-instantaneous. Teachers can now get truly involved in teaching and rectifying the lacunae in the learning process. Besides, the teachers can also get recommendations on how to rectify the issues, what learning materials to use for personalizing the process and much more to help herd the students towards the right levels of comprehension and skills required. This could also be used for learning processes of differently challenged students.

Concluding notes:

Both bot technology and its AI technology has started the process of personalizing and improving the education system of learning. Today bots are not new to students who can exploit their benefits at will and at their own pace to learn advanced subjects. Such advancements in AI, ML and bot technologies spur demand for professionals in this emerging field which has immense potential. Would you like to do a machine learning course at Imarticus Learning and join the ranks of the highly paid professionals who face no dearth of jobs? Start today. Hurry!

For more details, you can also contact to our Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.