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Tag: Artificial Intelligence

How To Write And Display Easily The Fibonacci Series In Java?

Posted on by Imarticus Learning
Reading Time: 3 minutes

What is Java?

Java is a programming language that was developed by James Gosling in Sun Microsystems in the year 1995. The aim of designing Java was to support a digital Television screening; ultimately it was found out that Java was developed advanced to just be used for a TV network. The first version of Java 1.0 was released in the year 1996.

The latest version of Java is 14 that launched in the year 2020. Presently, innumerous applications are existing on the internet which won’t work without the support of Java. Usage of Java has also been considered beneficial in Artificial Intelligence.

Artificial IntelligenceBeginners in this field may enrol for a Java Programming Training in Analytics.

 Principles of Java 

When Java was developed, it came out with a certain set of goals or the principles. These principles have to be followed while programming in Java.

The Principles of Java are:

  • It must be simple, object-oriented and familiar.
  • It must be robust and secure.
  • It must be architecture-neutral and portable.
  • It must be interpreted, threaded and dynamic.
  • It must execute with high performance.

Some Java-based Applications

Java has encapsulated most of the functions in web-based applications. Some of the fields covered by Java are:

  • Java Desktop GUI Applications
  • Java Mobile Applications
  • Java Web-based Applications
  • Java Web Servers and Application Servers
  • Java Enterprise Applications
  • Java Scientific Applications
  • Java Gaming Applications
  • Java Big Data Technologies
  • Java Business Applications
  • Java Distributed Applications
  • Java Cloud-based Applications

What is the Fibonacci Series?

Fibonacci is the concept which is found to have appeared in Indian History in a connection with Sanskrit Prosody. This concept was given by Parmanand Singh in 1985.

Fibonacci series is a series of some integers, where the Nth term is equal to the sum of N-1th and N-2th (last two terms). The first two numbers in the Fibonacci series are supposed to be 0 and 1 and each of the subsequent terms of the series is the sum of the previous two terms.

An example of Fibonacci series can be:

0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89…..

Artificial Intelligence

Algorithm to Generate Fibonacci Series

While generating a Fibonacci Series, some of the key points need to be focused on. Following is the algorithm to program a Fibonacci Series:

  • First two terms of Fibonacci Series need to be 0 and 1.
  • The last two terms of Fibonacci series have to be stored in “last” and “second last” integer values.
  • The current term of Fibonacci series is always equal to the sum of “last” and “second last” term.
  • The last and the second last integers need to be updated as Second Last= Last and Last= Current.

Ways to Write Fibonacci Series in Java

When writing a series in Java, recursion plays a vital role. The coding has to be done with or without the usage of recursion. Some of the programmers may just consider the use of recursion while coding Fibonacci series in Java but writing Fibonacci in Java without recursion is also a great way of coding which gives out amazing outcomes.

The two main ways of writing and displaying the Fibonacci series in Java are listed below:

  • Fibonacci Series without using recursion
  • Fibonacci using recursion

Ways to Display Fibonacci Series in Java

When it comes to the Display of Fibonacci series, it can be generally done by two ways in Java. Both the ways are listed down below:

  • Fibonacci using For LoopArtificial IntelligenceFibonacci using While LoopArtificial IntelligenceApplication of Fibonacci Series

Fibonacci is used in various application systems. It is used for interconnecting the parallel and distributed systems. It can also be used in the following ways:

  • Computer algorithms are known as Fibonacci Search Technique and Fibonacci Heap Data Structure.
  • A certain specific type of graphs and tables particularly known as Fibonacci Cubes.

Artificial Intelligence Can Assist To Fight Against The CoronaVirus!

Posted on by Imarticus Learning
Reading Time: 2 minutes

Fear and panic like the coronavirus (known as “2019-nCov” or “Covid-19”) has spread across the globe. Covid-19 claimed 33,993 lives and resulted in more than 723,279 documented cases of infected people in 192 countries.

The new coronavirus pandemic has swept the world. This has caused a stir in the socio-economic landscape of various countries.

According to reports from 195, COVID-19 has infected 192 countries worldwide. While the death toll is rising in other countries such as Italy, China is recovering from the Wuhan virus and no new domestic cases have been reported recently since the outbreak.

But how will China recover while others are plunged into unhealthy chaos? The answer is artificial intelligence and other significant destructive technologies. The country is using AI, big data, as a mechanism to fight the coronavirus.

Tech giants such as Baidu and Alibaba are leading innovative initiatives to tackle the crisis. In addition to China, AI-based medical technology and startups from various regions, including the United States, with their innovations to combat the pandemic situation will come to the fore.

Countries around the world – including the United States, South Korea and Taiwan – are using artificial intelligence (AI) to slow the spread of COVID-19. The technology will be used to accelerate the development of test and treatment kits, track the spread of the virus, and provide real-time information to citizens.

Artificial intelligence training and applied science are making it easier, faster and cheaper to understand how viruses spread, how to control them, and how to control their devastating effects.
MAIN REASON A. I WILL WIN THE COMPETITION-
1. AI can predict epidemics.
Artificial intelligence, better known as AI, can warn of an impending epidemic and give us plenty of time to prepare.

In the future, A.I. It can even use social media data to predict human behavior and potential breakouts.

2. AI Can accelerate drug discovery and development.
Not only can AI warn us of impending epidemics, it can help us identify, develop, and scale new treatments and vaccines faster than ever.

“The potential for AI to accelerate detection of vaccines, drugs and diagnostics is enormous, especially with rapidly mutating RNA viruses such as Covid, which require a broad spectrum approach.”

3. AI Can help minimize mortality and optimize disease management

Lastly, AI can help manage outbreaks and minimize deaths by reducing the burden on healthcare professionals and reminding patients of appropriate treatment procedures.

They can help touch excellence in your career and make the country proud. Learn AI and ML from home. Use this time to create a bright future for your nation, and of course yours.

Take part in certification programs from E & ICT Academy and Eckovation in AI & ML

Study from home with IIT teachers. Against the backdrop of the health crisis in the country, not letting your career suffer, living at home and in a protected environment is the only luxury we all have, let’s appreciate and learn. In the event of limited availability in this course, we request that you register immediately and follow the admissions process.

Solve Real-world Text Analytics Problems With NLP!

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Solve Real-world Text Analytics Problems With NLP!

Natural language processing (NLP) helps machines analyze text or other forms of input such as speech by emulating how the human brain processes languages like English, French, or Japanese. NLP consists of ‘natural language understanding’ and ‘natural language generation’ which help machines create a summary of the information or assist in taking part in conversations.

With the advent of natural language processing, services like Cortana, Siri, Alexa, and Google Assistant are finding it easier to analyze and respond to requests from users. This is opening up many new possibilities in human-machine interactions and helping improve existing systems and services.

In this article, we will cover how NLP is helping provide solutions for various requirements of text analytics in different sectors.

Significance of NLP in modern times

data analytics courses

NLP can analyze massive amounts of text-based data with consistency and accuracy. NLP courses help summarize key concepts from large unstructured complex texts. It also helps in deciphering or analyzing ambiguous statements or sentences. It can draw connections and also investigate deeper meanings behind seemingly normal data in the form of text.

With the massive amounts of randomized forms of textual data that is generated on a daily basis, automation is highly necessary for this field to analyze the large amounts of data from text efficiently and effectively. Ranging from text posted on social media to customer service, natural language processing is powering text analytics which is making life easier for both consumers and corporations. 

How text analytics along with NLP is helping businesses? 

Text analytics can be described as a process of analyzing a massive or specifically targeted volume of unstructured textual data and translating it into quantitative information to gain valuable insights through patterns and trends.

With the help of additional visualization of this data, text analytics allows corporations to understand the sentiments, deeper meaning, or compact information behind this data and helps them take data-backed or data-centric decisions for improved results through better performance or profit.

These companies collect massive amounts of unstructured textual data from sources like social media, e-mails platforms, chat services, and historic data from previous interactions or third parties. This could prove to be a challenge without the help of natural language processing which powers text analytics, helping analyze the massive amounts of data without the need to stop or for human interference. 

The same amount of data, being manually processed seems like an impossible, never-ending task. Manually processing even a tiny bit of the colossal amount of data that is generated daily would definitely take a lot of manpower. Hence, it is not cost-effective and would also lead to inaccuracy and duplication. This is where text analytics comes to the rescue.

With the help of text analytics, companies can excavate meaning and sentiments from unstructured textual data sourced from social media posts, content inside e-mails, chat services, and surveys or feedback. 

This helps businesses identify patterns and trends which lead to providing customers with improved experiences by analyzing service or product issues and customer expectations through market research and monitoring with text analytics.

Natural Language ProcessingHere are some real-world applications of text analytics and natural language processing:

Customer care service

Data generated from surveys, chats, and service tickets can help companies improve the quality of customer service by increasing efficiency and decreasing the time taken in resolving problems.

Illegal activity and fraud detection 

Text analytics helps in analyzing unstructured data from various internal or external sources to prevent fraud and warn governments or companies of illegal and fraudulent activities. 

Natural Language ProcessingSocial media analytics

Text analytics is being used by brands to analyze customer preferences and expectations through the extraction of sentiments and summarized opinions from textual data sourced from social media platforms like Facebook and Instagram. 

Text analytics and NLP are increasingly becoming more effective for companies to depend on and encouraging them to take more data-backed decisions. This need is making way for better, more accurate, and faster analytical tools and technologies in the future.

Job Opportunities in The Field of Artificial Intelligence in This Pandemic Time!

Posted on by Imarticus Learning
Reading Time: 3 minutes

To become an Artificial Intelligence (AI) professional, you need to have practical problem-solving skills, logic, communication, and analytical skills. AI is made to create computer programs that can achieve goals and solve a problem better than humans. With lesser mistakes and emotions to hinder the work, AI gives better and jan efficient output.

The scope in AI is vast. You can get into robotics, gameplay, language detection, machine learning, computer vision, speech recognition, and many more.

Some of the factors that characterize a great career in AI are as follows:

  • Robotics
  • Use of sophisticated computer software
  • Automation

Math, technology, engineering, and logic are some of the specific fields that individuals have to specialize in if they are considering a job in this field.

Along with this, learning science including physics and computer studies is beneficial.  Considering the computational approach to AI, knowing the technical, as well as physiological knowledge of the system, is immensely helpful. Knowledge of primary machine language is a must. There are many other courses that you can do to get into the world of AI like, Machine learning.

Data Science Online CourseMany institutes like IIT provide machine learning courses, there are other institutes that provide these courses online and then there are certification courses that you can take up in private institutions.

Some of the career opportunities in AI

  • Robotic Scientist

Robots are gradually taking over the industrial worlds. There is lesser workforce and more robots. To help create such robots that can solve problems as a human would, we need engineers or programmers. For a career in Artificial Intelligence field, a master’s in robotics engineering and having a license from the state can be of help.

  • Software Engineer

In every phone that is there in the market, there is an option for face recognition or finger print recognition. Many companies, including big businesses, security companies, casinos, etc. have face recognition and fingerprint recognition to understand the people who use their services. Hence being a software engineer is one of the opportunities here.

  • Game Programmer

To keep the players challenged and highly anticipated, every gaming company requires candidates that are well known with the basics of AI and can design games that can keep the players engaged and interested.

  • Search Engine Manager

Many big companies, like Google, pay a massive amount to candidates with an AI degree to manage their massive search engines. Many may search for various things on Google, but Google search is able to predict the search even when there are spelling mistakes or grammatical errors. This is done with the help of knowledge and the study of artificial intelligence.

  • Government Sector

There are jobs not just in the Private sector, but there is an intense need for candidates with a degree in AI in the government sector too. The pay is high, and along with that, the amenities provided are even better.

Conclusion

The scope of artificial intelligence is vast. Having a master’s degree or a doctorate is the best if you are looking for a long term job in the field of AI.

The demand for people with knowledge of AI is strong. Companies like Google, Apple, etc. are always on the lookout for candidates who can take the world of AI to another level. The choices are plenty, and the income from working in such a field is high.

‘Eve’, a robot created by the scientist at the University of Manchester, Cambridge, discovered that a common ingredient found in toothpaste is capable of curing malaria. This event, itself, can show how much this field has grown, and the job possibilities are endless.

Top 5 Courses From Imarticus That Empowers Women!

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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!

Posted on by Imarticus Learning
Reading Time: 3 minutes

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.

#KnowledgeBytes: Artificial Intelligence – Customer service Trends!

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Reading Time: 2 minutes

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.

Check our complete #ImarticusPrograms playlist here: https://bit.ly/2JP52hM Subscribe to our channel to get video updates. Hit the subscribe button above.

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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Pattern Recognition – How is It Different from Machine Learning?

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Pattern Recognition and Machine Learning are closely related terms in the field of data analysis. The former is a part of Machine Learning and is used as a technique to detect patterns and irregularities in a pool of data.

There is a very thin line between them which will be covered in the following sections. And a simple way to distinguish between them is to understand their individual functions and qualities.

Pattern Recognition vs Machine Learning

Let’s first understand what Machine Learning is? It is basically a concept that allows systems to learn and adapt in a particular way by means of data.

Take the example of how a user behaves with an automatic food recipe machine. If the appliance uses Machine Learning to understand user behavior in a better way, it would ideally take insights from all the past user actions and adapt itself for better functioning.

The primary (and perhaps the only) goal of Machine Learning is to make good guesses. In consumer tech, this is used to automate actions in an application as suggested in the example above. However, Machine Learning has applications across industries (as noted below). This is why there is a growing demand for professionals with relevant skills, which in turn, has resulted in a boom in Machine Learning courses.

What is Pattern Recognition?

It can be seen as an application or subset of Machine Learning (ML). It is basically an element that detects patterns in an ML algorithm. Unlike ML, it uses previous information to refine its findings.

Let’s go back to the appliance example given above. How would the process change if the appliance was already fed with some patterns that the user is assumed to take? This can have a considerable impact on how the appliance is built in the first place. When used, it only has to match the user actions with those already available in its memory. This can improve user experience considerably.

The prediction made on the basis of this pattern recognition on an ML algorithm is essentially called predictive analytics. It is a growing field and one that can be further studied as part of Machine Learning training programs.

Moreover, there are some features that make Pattern recognition a great addition to the world of ML. Some of them are listed below.

  • It can detect familiar patterns and known issues accurately. (This function is extremely helpful in hi-tech to detect online fraud)
  • Classification of patterns
  • Continuous learning as more streams of data is analyzed and processed.

Overall, Pattern recognition acts as an improvement in ML algorithms as it aids in making certain tasks easier. This is why it is heavily utilized across applications in the fields of image processing, biometrics, seismic analysis, and speed recognition.

A very fine example of the use of Pattern recognition is in the field of DNA testing. It can aid the scientific community in detecting DNA sequences with more accuracy and a low error rate. This is advantageous in forensics as well where accuracy is extremely critical.

To conclude, the thin line between Pattern Recognition and Machine Learning is in their functions within an algorithm. While ML is the main method used to process data and influence outcomes, Pattern recognition acts as a helping hand.

One of the best ways to learn more about the differences between the two is to undergo Machine Learning training. Students and professionals can take advantage of online courses available in this field and make good use of the ample free time available during this lockdown period.

The Perks of Using Machine Learning for Small Businesses!

Posted on by Imarticus Learning
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Machine Learning and Artificial Intelligence have often been associated with top-of-the-rank brands such as Google and Apple. That has led to the perpetuation of an idea that AI just isn’t for everyone… and that’s incorrect.

Artificial Intelligence, specifically Machine Learning, is just as accessible and usable to small businesses as they are to tech and finance titans. When it comes to staying ahead of competitors, the situation is make-or-break– emerging technology is the portal through which smaller companies can gain headway in an already airtight industry, or quickly adapt processes that take months to approve in larger corporations.

As with anything new, the future of artificial intelligence and Machine Learning also presents its own sets of stumbling blocks, some of which may prove to be a detriment for smaller companies with limited budgets and skilled personnel. R&D accounts for a large chunk of the expenditure; training and analysing models takes topline human resources.

However, if firms are willing to take the risk and take the plunge, there are a whole host of perks that will have small businesses emerging victorious:

Making Marketing Campaigns Stronger

Marketing is the be-all and end-all of many brands, especially those that heavily rely on brand image and word of mouth to sell products or services. Machine learning can be put to use in marketing in the following manners:

  • Personalising product recommendations
  • Automating cataloguing of products
  • Optimising content from email subject lines to Facebook ads
  • Researching trends and search terms
  • Revamping keywords and SEO strategies

To achieve the following goals:

  • Innovative products and services
  • Happy customers and lesser returns
  • Intuitive and interactive user experiences
  • Diversified revenue streams
  • Reduced marketing costs and subsequent waste

Driving Sales Numbers

When it comes to sales, insights and analyses of data can be a veritable goldmine– this is where machine learning comes in. A solid ML tool can analyse:

  • customer-product interactions
  • past purchases
  • digital behaviour
  • trending search terms
  • popular products
  • transaction types

Using this, firms can identify what leads are likely to convert and equally pay attention to converting hesitant users into loyal customers.

Upselling and Cross-selling

Upselling means getting the customer to purchase a higher or more upgraded product, while cross-selling means pitching products in the same segment or complementary to the product in their cart. Machine learning can be leveraged to produce personalised recommendations of products and services based on analyses of the existing database. By identifying past purchases or inter-linking products, machine learning tools can upsell or cross-sell appropriately, thereby driving revenue and increasing the number of items sold.

Automating Repetitive Tasks

Small businesses are often faced with having to delegate the most menial tasks to precious employees, leaving the latter overburdened and unable to innovate. Using machine learning to automate repetitive tasks can ensure that routine measures are taken care of at scheduled times and employees are left with time to think strategically and fulfil intended roles. Some tasks that are automatable include:

  • Generating and sending email responses
  • Setting up a sales pipeline
  • Collecting and logging payments
  • Gathering and evaluating client satisfaction

Conclusion

Regardless of the industry, machine learning offers several perks for small businesses to help them grow, expand and generate revenue through different streams. From bookkeeping and manual data entry to voice assistants and exclusive data insights, a machine learning course can put you at the. Forefront of the industrial revolution taking the world by storm today.