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Tag: Artificial Intelligence

How Machine Learning is Reshaping Location-Based Services?

Posted on by Imarticus Learning

Today life is a lot different from what it used to be a decade ago. The use of smartphones and location-empowered services is commonplace today. Think about the driving maps, forecasts of local weather and how the products that flash on your screen are perhaps just what you were looking for.

Location-enabled GPS services, devices that use them and each time we interact and use them generates data that allows data analysts to learn about our user-preferences, opportunities for expansion of their products, competitor services and much more. And all this was made possible by intelligent use of AI and ML concepts.

Here are some scenarios where AI and ML are set to make our lives better through location-based services.

Smart real-time gaming options without geographical boundaries.
Automatic driver-less transport.
Use of futuristic smartphone-like cyborgs.
Executing perilous tasks like bomb-disposals, precision cutting, and welding, etc.
Thermostats and smart grids for energy distribution to mitigate damage to our environment.
Robots and elderly care improvements.
Healthcare and diagnosis of diseases like cancer, diabetes, and more.
Monitoring banking, credit card and financial frauds.
Personalized tools for the digital media experience.
Customized investment reports and advice.
Improved logistics and systems for distribution.
Smart homes.
Integration of face and voice integration, biometrics and security into smart apps.
So how can machine learning actually impact the geo-location empowered services?

Navigational ease:

Firstly, through navigation that is empowering, democratic, accurate and proactive. This does mean that those days of paper maps, searching for the nearest petrol station or location, being late at the office since the traffic pileups were huge and so many more small inconveniences will be a thing of the past. We will gracefully move to enhanced machine learning smartphones that use the past data and recognize patterns to inform us if the route we use to commute to office has traffic snarls and provide us with alternative routes, suggest the nearest restaurant at lunchtime, find our misplaced keys, help us locate old friends in the area etc all by using a voice command to the digital assistant like Alexa, Siri or Google.

Machine Learning can make planning your day, how and when to get to where you need to be, providing you driving and navigational routes and information, and pinging you on when to leave your location a breeze. No wonder then that most companies like Uber, Nokia, Tesla, Lyft and even smarter startups that are yet to shine are investing heavily on ML and its development for real-time, locational navigational aids, smart cars, driverless electric vehicles and more.

Better applications:

Secondly, our apps are set to get smarter by the moment. At the moment most smartphones including Google, Apple, Nokia among many others are functioning as assistants and have replaced those to-do lists and calendar keeping for chores that include shopping, grocery pickups, and such.

Greater use of smart recommendatory technology:

And thirdly, mobile apps set smartphones apart and the more intelligent apps the better the phone experience gets. The time is not far off when ML will be able to use your data to actually know your preferences and needs. Imagine your phone keeping very accurate track of your grocery lists, where you buy them, planning and scheduling your shopping trips, reminding you when your gas is low, providing you with the easiest time-saving route to commute to wherever you need to go and yes, keep dreaming and letting the manufacturer’s know your needs for the future apps. The smart apps of the future would use your voice commands to suggest hotels, holiday destinations, diners, and even help you in budgeting. That’s where the applications of the future are headed to.

In summation, ML has the potential to pair with location-using technologies to improve and get smarter by the day. The future appears to be one where this pairing will be gainfully used and pay huge dividends in making life more easily livable.

To do the best machine learning courses try Imarticus Learning. They have an excellent track record of being industrially relevant, have an assured placement program and use futuristic and modern practical learning enabled ways of teaching even complex subjects like AI, ML and many more. Go ahead and empower yourself with such a course if you believe in a bright locational enabled ML smart future.

What Are the Algorithms in Machine Learning? How Does It Work?

Posted on by Imarticus Learning

Machine learning is a vast field comprising of various data related operations such as analysis, prediction, decision making and much more. These applications require a set of well-defined steps to proceed with the idea designed for model construction. A set of well-defined instructions that produces some output or accomplishes a particular task is called an algorithm. The machine learning algorithms are broadly classified into 3 categories – Supervised, Unsupervised and Reinforcement Learning.

To choose an appropriate algorithm in machine learning, identifying the kind of problem is very necessary as each of these algorithms obeys a different plan of attack to deal with the proposed problem. Supervised learning uses an approach where the output is already known to the user or the individual while unsupervised learning concentrates on the concept of similarity in properties of the objects. Reinforcement learning differs from both of them and uses the art of learning from experiences.

Supervised learning

Supervised learning is used in machine learning tasks such as classification, regression, and analysis. It is considered as a concept that deals with labeled values. This means that the objects are categorized or assigned to different classes based on their properties. The algorithm implementation in supervised learning is done by a two-step procedure namely model construction and model utilization.

Firstly, the given data is cleaned and divided into training and testing sets. The model gains the ability to produce output by learning from the instances contained in the training set. The test set gives a measure of the model performance by producing accuracy. The accuracy indicates the amount or rather the percentage of unseen data that was computed correctly by the applied algorithm.

There are several metrics to determine the performance of the model and improve it if the performance is not up to the mark. This includes performing tasks like cross-validation, parameter tuning, etc. Hence, we can conclude that supervised learning uses labeled classes and target values to classify an unseen data point.

Unsupervised learning

In contrast to the supervised approach that already knows the predicted outcome, unsupervised learning uses the basis of similarity in properties to classify the unseen data points in the given n-dimensional space.

The main idea is to take a data point that is new to the given space, extract the behaviors of the data point, compare it with the already existing properties of the other objects and accordingly classify or categorize them into the appropriate group. The common examples of unsupervised learning are clustering, Apriori and K-means algorithm.

Reinforcement learning

Reinforcement learning is very similar to the animal kingdom where the animals do not train their offspring to perform a particular task but they leave them out in the ecosystem to learn from the experiences that it gains while struggling to accomplish a particular task.

The basic idea of performing reinforcement learning is to let the model learn on its own. It uses a trial and error strategy to gain knowledge from the available environment. According to the experiences gained from the conditions, it is exposed to, appropriate predictions and decisions are made. Markov Decision Process is an example of reinforcement learning.

Conclusion

Because of the wide variety of applications offered by machine learning, there are several Machine learning courses dedicated to offering the training in machine learning algorithms so that an individual can recognize the problem efficiently and work towards building an appropriate solution. Learning and understanding of machine learning algorithms are very easy. It just needs a proper classification of the interest in performing the desired operation.

Artificial Intelligence skilling has to start from a young age! How? Explore…

Posted on by Imarticus Learning

The chasm between machines and living things is shrinking. Artificial intelligence (AI) is deeply rooted in all aspects of technology, from robots to social networks. India has the potential to skyrocket in the domain of Artificial Intelligence and surpass USA and China, largely owing to:

  • It’s deep-rooted IT &ITeS infrastructure
  • Innovation ( India ranked among the top 50 countries in the Global Innovations Index 2020)
  • Accessibility to large datasets

These have pioneered more than a handful of start-ups and private investments in this sector. For AI to flourish further, there needs to be a nationwide upskilling of the younger generation in Artificial Intelligence Training. The GenZ needs to be acquainted with the theoretical and practical aspects of AI application to increase its scope of innovation and entrepreneurship.

Artificial Intelligence CareerIn the future, the interaction between humans and AI will define in a lot of ways the structure and functioning of a modern-tech society.

Thus it becomes imperative to lay down the basis of friendship for the years to come by exposing the young ones to AI.

While a lot of minds will wander to an Artificial Intelligence Career it is also important that others are no less familiar with the upsides and downsides of such a powerful technology.

Here is how we can ensure the frontiers of the same:

  • Introduce young people to the concepts of AI and machine learning through education curriculum. In India, the Central Board of Secondary Education (CBSE) announced the integration of AI in partnership with IBM for the academic year 2020-21
  • Encourage learning through hands-on projects so that student can make better, informed and critical use of these technologies
  • Enrolling young minds on various Edu-tech platforms specializing in the field of Machine Learning and AI which help them gauge interest and real-life applications of such technologies using intuitive software

Some of these websites include- Scratch, App Inventor, Cognimates etc

  • Experiments with Google is an easy-access, affordable, and user-friendly tool to explore artificial intelligence training at a young age with exciting experiments on AI, VR, AR, Chrome, Voice, Android etc to apply creativity and technological dexterity at the same place. One of these fun-filled learnings includes MixLab that uses voice commands to create music
  • Engage in the practice of cultural inquiry – like what is the goal of You tube’s recommendations or how do my Amazon purchases reflect on my Instagram feed
  • Lastly, before introducing your children to the world of AI and machine learnings, self-education of the same is very crucial

Apart from exploring the possibilities of AI, these junior minds also need to know the limitations of AI to have a balanced approached. That is to say, AI is not the ultimate machine as it is created by humans and will improve along the way by errors made and rectified by humans.

Artificial Intelligence CareerIn recent studies, a scientist is experimenting to teach AI to learn like a kid. They want to inoculate the eager learning attitude and swift skills of young people into the algorithms of machines.

And, AI does not create everything. It is the innovation and vision of responsible human beings that will introduce, implement, and maintain the technological structure in human society.

How Machine Learning Systems Can Streamline Healthcare Disbursement Setups?

Posted on by Imarticus Learning

The ripple effects of the COVID19 pandemic have been felt across industries at several levels. The healthcare industry wasn’t spared either, with essential healthcare workers moving to the frontlines to deal with the emergency. As a result, many organizations saw their back-end operations, such as appointment bookings and disbursement trackers, floundering.

However, there is a silver lining in this situation– it’s that technology has speedily been integrated into systems. Telehealth software has seen a surge in demand so as to prevent risks of exposure; healthcare disbursements are next on the list to be made easier.

Healthcare disbursements are traditionally tricky and convoluted processes; the pandemic has put further amounts of strain on the system and caused frustration, delays, and errors. However, machine learning in healthcare is a step forward in fixing disbursement delays.

Here’s how:

  • Moving from Checks to Digital Disbursements

A majority of disbursement systems around the world rely heavily on cheques and other outdated methods. However, this has become a point of friction at this time considering courier services have shut down and deliveries are very delayed. In such a scenario, the use of digital reimbursement options, bolstered by machine learning, is tempting.

Providers can facilitate faster payouts through DTC (direct-to-consumer) payments. By shifting the process online, providers will also be able to keep track of all patient and consumer data on one server. Machine learning can be used to pull up the relevant information, create automated disbursement setups, and ensure the consumer receives their disbursement digitally. The reduced reliance on paper payment processes will lessen the load on healthcare finance systems as well as get disbursements out to the right people in a flash.

  • Addressing Glitches in Systems

Several reports talk of misplaced cheques, incorrect deposit information, and several such kinks in telehealth and digital healthcare solutions being used today. Machine learning can be leveraged to iron out these kinks because, especially during a healthcare crisis, such errors can have a snowball effect on consumers and providers alike.

Providers who use machine learning systems to manage delays will be able to maintain strict records of past and future payouts. The system can be trained to collect the right deposit information as well as cross-verify with other records if required. The reliance on an automated system, in this case, equals to a lesser reliance on outdated methods of payout tracking.

  • Simplify User Experience

Claiming payouts and processing them can become a nightmarish experience for both patients and healthcare providers alike. Machine learning systems effectively reduce quite a number of manual steps which, in turn, saves time, money, and efforts. Machine learning can be leveraged to extract critical information from healthcare contracts, estimate how much is owed, and prepare the right documentation in time for a payout.

For patients, too, the process of claiming payouts become simpler. They will no longer have to fill out a myriad of forms and move from office to counter over days. Instead, by automating certain processes from the providers’ ends, patients can be called in only to verify details if necessary and to provide any other physical documentation the healthcare provider may need.

Conclusion

The healthcare industry will most likely see a surge in the adoption of machine learning and artificial intelligence. This will be across the board– from handling disbursements to automating admissions and discharges. Therefore, students who are interested in pursuing an artificial intelligence career would do well to explore this niche and develop the right skillset for it.

You can do this by enrolling in a machine learning course that focuses on the healthcare system, or take on related projects that could leverage your portfolio when it comes to it. The current strains on healthcare providers worldwide have exposed significant cracks in the system that machine learning could most likely fix.

How To Build A Credit Scoring Model With Machine Learning?

Posted on by Imarticus Learning

Credit bureaus and lending institutions have embraced big data and machine learning to develop credit score models on the basis of which the creditworthiness of a borrower is judged. This has many benefits for the business as they can better assess the risks of offering loans, gauge the repayments and plan accordingly. Businesses today take advantage of the huge volumes of data proliferating nearly every sector to create their own scoring models based on Big data and a long delicate and expert process of executing a machine learning course of algorithms to build their own models.

The trends:

The era of basing decisions solely on credit scores from bureaus are over. Today custom models work better and more accurately since they use data from a number of sources both internal and external to assess creditworthiness. Such data could include supplier information, account data, customer relationship or other market data. More the data the more accurate and efficient the scoring model becomes.

How to create the scoring model:

1. Goal setting:

Clear cut goal setting is important to achieve accurate results in scoring models. The goal needs to be in mine with the needs of business and its scoring model. For example, the goal could be the probability of late repayments of existing loans and dealing with the repercussions. Or, it could be using the data to decide on scoring the financial repayment plans of borrowers and their creditworthiness.

2. Data gathering:

This is a crucial requirement as all assessment is done on the basis of data. With enough data volumes and reliable data, a scoring model is made for the specific goals set. The test model so built can be used to supervise the model which will help in training the model under supervision from domain experts. Beyond this point, you will need to test the model with credible credit score website databases like the Boostcredit101.

3. Building the model:

With both internal data and comparative data in place, the experts can now build your scoring model. The Machine Learning Course procedure is complex and involves a large number of algorithms trained to interpret the data before the final test model is ready for deployment. It goes without saying that the goals of the business owners and the aim of the model builders need to be the same and both would need to contribute to the end goals and success of the scoring model being developed.

4. Validation:

The next phase is to validate the process and ensure the scoring model provides accurate results. Most applications lean on how to predict the late payments of the debtors. The scoring model will use the new data while scoring it against the test results to produce a score between 1 and 100. Higher scores mean fewer defaults and vice versa. These scores are also repeatedly done as changes in financial status, incomes and economic growth can all affect the score.

5. The implementation:

This final phase is where the permanency of the scoring model is tested by the actual implementation. A successful model will remain while the inefficient models get wiped out. Challenger models play the role of checking to see if the scoring model is functioning well or is the challenger is the better model.
The Big Data connection:
A shift from total reliance on credit-bureau data has seen lending banks, institutions, and companies that use credible data buy such data. Data is digital gold and large volumes of big data are needed to train AI on a machine learning course.The cleaning, parsing and making sense of such large volumes of multivariate data is a job for expert data scientists. This data is then used to create the scoring model be it a new or challenger model.
According to data scientists, it is these scoring models with the best ML algorithms that ML can accurately tap all unrelated factors and relationships in the data to provide a better scoring model. Though it is not without problems it is heartening that ML can help the machines self-learn with data and the more data one inputs the better are the results of the scoring model.

Conclusions:

The insights, big data, and ML have helped create scoring models for businesses, lenders, and organizations. While traditional credit bureau reports are also crucial, ML can go further with scoring models helping them add insights and provide newer business points of view. If you are interested in learning more about ML and credit risk scoring you could do a machine learning course at the Imarticus Learning Institute where futuristic technologies are taught and skilled on. Don’t wait too long. Start today!
For more details in brief and further career counseling, you can contact us through the 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.

What’s The Quickest Way To Learn Math For Machine Learning And Deep Learning?

Posted on by Imarticus Learning

In modern times we have everything from developments like smartphones, robots, driver-less cars, medical instruments like CAT scans and MRI machines, smart traffic lights, and a host of animated games. Even payments have gone digital and cashless! And all this has emerged over the last decade due to AI, ML, and data analytics.

The future holds great promise for development in these fields and to make a high-paid scope-filled career in any of these fields, mathematics is the key ingredient that you must learn if you want to learn machine learning. ML runs on algorithms and the algorithm is dependent on knowledge of mathematics and coding.

Why mathematics is so important in ML:

Some of the many reasons are :

  1. Selecting the apt algorithm with a mix of parameters including accuracy, model complexity, training time, number of features, number of parameters, and such.
  2. Selecting the validation of strategies and parameter-settings.
  3. Using the tradeoff of Bias-Variance in identifying under or overfitting.
  4. Estimating uncertainty and confidence intervals.

The math components required for ML:

ML algorithms require proficiency in the three topics of Linear Algebra, Probability Theory, and Multivariate Calculus.

Let us discuss the topics you need to learn machine learning under each of these heads.

A. Linear Algebra:

The use of Linear algebra notation in ML helps describe the structure of the ML algorithm and the parameters it depends on. Thus linear algebra is important in the interconnection of neural networks and their operations.

The topics that are important are : 

  • Vectors, Tensors, Scalars, Matrices,
  • Special Vectors and Matrices
  • Norms of Matrices
  • Eigenvalues and vectors

B. Multivariate Calculus:

ML learns from its experience with the data set and to supplement this we need calculus to power learning from examples, improving performance, and updating parameters of the different models.

The important topics here are : 

  • Integrals
  • Derivatives
  • Differential Operators
  • Gradients
  • Convex-Optimization

Probability Theory:

The assumptions about data use this theory to design the AI and its deep learning capabilities. The key probability distributions are crucial to algorithms.

Study these topics well.

  • Random Variables
  • Elements of Probability
  • Distributions
  • Special Random Variables
  • Variance and Expectation

Can you learn Math for ML quickly?

To learn machine learning it is not required to be an expert. Rather understand the concepts and applications of the math to ML. Doing things like math is time-consuming and laborious.

While there may be any number of resources online, Mathematics is best learned by solving problems and doing! You must undertake homework, assignments, and regular tests of your knowledge. One way of getting there quickly and easily is to do a learn machine learning course with a bootcamp for mathematics at Imarticus Learning

This will ensure the smooth transition of math and ML applications in a reputed institute for ML where they do conduct bootcamps. At the end of this course, you can build your algorithms and experiment with them in your projects. But, the main question that remains is why do a learn Machine Learning Course at Imarticus in the first place?

The Imarticus Learning course scores because: 

  • They have sufficient assignments, tests, hands-on practice, and bootcamps to help you revise and learn machine learning.
  • They use certified instructors and mentors drawn from the industry.
  • They integrate resume writing, personality development, mock interviews, and soft-skill development modules in the course.
  • They have convenient modes and timings to learn at your own pace for professionals and classroom mode for freshers and career aspirants.

Conclusion:

Mathematics is all about practice and more practice. However, it is crucial in today’s modern world where AI, ML, VR, AR, and CS rule. These sectors are where most career aspirants are seeking to make their careers, because of the ever-increasing demand for professionals and the fact that with an increase in data and the development of these core sectors, there are plentiful opportunities to land the well-paid jobs.

At the Imarticus, you can consider the Machine Learning course, you will find a variety of courses on offer for both the newbie and tech-geek wanting to go ahead in his/her career. Start today if you want to do a course in AI, ML, or Data Analytics. For more details in brief and further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi, and Gurgaon.

10 Essential Leadership Qualities For The Age Of Artificial Intelligence

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Artificial intelligence (AI) is slowly being a revolution that can completely change the workforce. At the same time, it is still not able to replace human intelligence and reliability.

This is the main reason why leadership qualities are highly significant under the circumstances.
When AI is starting to show its power, it takes a highly capable leader to show the team that there is still a lot the humans can do.

In order to show them the same, a ladder needs to have certain attributes at this age of AI. These are qualities that are not taught during an Artificial intelligence course but are the ones that you need to develop yourself.
The essential leadership qualities

  1. Agility: In this fast world, a leader needs to have a quicker mind and make strategies on the go. This is one area where there are no compromises. If you have to survive in this era you have to be an agile leader.
  2. Adaptability: Sharpen adaptability skills because the requirements and circumstances could change anytime, A leader must be willing to make changes swiftly but effectively to adapt to the situations. Better the adaptability, finer would be the outcome.
  3. Accountability: Be accountable for all or any actions and decisions made as a team. Since leading from the front requires trust, this attribute helps develop confidence within the team. So be accountable and transparent.
  4. Commitment: Artificial intelligence may be able to show the way but the decision-making power is still with the humans- leaders. A leader must be committed to the decisions made and for any changes thereafter.
  5. Better communication: A leader needs better communications skills, period. Developing this attribute is more important than enrolling in any Artificial intelligence course. Look for courses that help develop this personal quality.
  6. High work ethics: Learn to value others in the team and give as much importance to every part of the work system. One who can inspire others and aspire to be a better person is better valued by the companies.
  7. Foresight: AI may be able to foresee future possible changes but it is the leader who needs to have the foresight to see and decide for the possible changes that could be down the lane. It also calls for some amount of creativity to use such changes for the betterment of the company.
  8. Flexibility with demands: When Artificial intelligence is predicting changes even a small change of course can have major impacts. A true leader must be flexible with such changes according to the demands. A leader must be able to alter his or her working style to suit the new scenario and should also be able to make it productive.
  9. Be able to influence: The flexibility in work and coming up as the winner at the end of such a trial should be enough to influence others to follow. This is one leadership quality that is highly dependent on the other attributes. One must be reliable, adaptable, and trustworthy enough to influence others. When you influence others to be positive, you are giving more value to yourself and to the company.
  10. Stay Humane: AI might be taking over too much of human efforts but the one thing that it cannot take away is the humane nature. A ladder who stays humane under all circumstances is sure to be born as a commander. This is another attribute that no Artificial intelligence course could teach you. You stay grounded even when you are flying high; it’ll make you the person that defines leadership qualities in this very age of robotics and manmade intelligence.

Also Read: 10 Interesting Facts About Artificial Intelligence

Keen to Know What Transitions a Fintech Aspirant to a Fintech Expert? Check This Out!

Posted on by Imarticus Learning

Financial services have experienced a major paradigm shift due to the introduction of fintech. Digital banks are replacing traditional ways of accessing financial services. The current fintech market in India is more than 1,900 billion and will grow with an impressive CAGR in the coming years.

There are a lot of job opportunities in the fintech sector and you can build a successful career in fintech by choosing the right career path. Read on to know more about the transitions required to become a fintech expert.

 Get the Right Education

A bachelor’s degree in mathematics or computer is the best to get into the fintech industry. Many fintech aspirants also have degrees in business, accounting, economics, etc. Getting a degree will not teach you about the working of the fintech industry but it will help you in developing an analytical & statistical mind.

Many fintech aspirants also prefer to get a master’s degree for opting for senior job roles in the fintech industry. One should also try to be updated with the modern-day technologies used in the fintech industry. AI (Artificial Intelligence), ML (Machine Learning), deep learning, etc. are used widely to improve fintech solutions.

A technical degree with Fintech Course as a specialization will also help you in getting into the fintech industry. Along with getting a degree, you can also opt for internships, sponsored/individual projects, workshops, etc. in fintech for boosting your knowledge.

Fintech TrainingYou can target any particular job role in the fintech industry based on your skillset. There are many types of job roles in the fintech industry like a compliance expert, cybersecurity expert, data scientist, financial analyst, etc.

Acquire Necessary Skills

You will require several technical & non-technical skills to become a fintech expert which is as follows:

  • You should have good problem-solving skills to create better ways of providing financial services to people with the aid of technology.
  • You should have good analytical skills to draw conclusions and to analyze various solutions.
  • Good programming skills are required to become a fintech expert. Programming languages like C#, C++, Java, Python, SQL, etc. are widely used in the fintech industry. You should also be aware of the databases used in the fintech industry.
  • You also should have good financial skills to become an expert. You should be able to read & analyze financial statements & reports for creating better financial services.
  • You should know about the applications/tools used in the fintech industry know about the practices involved in the fintech industry. You should also be familiar with the latest technologies like AI, blockchain, etc. used in the fintech industry.
  • You will also have to possess some soft skills like collaborative skills, communication skills, adaptability, etc. to thrive in the fintech sector.

 Get the Right Certification Course

Besides getting a degree in the related field, you will need to get a certification in fintech from a reliable source to know about the working methodology of the fintech sector. Imarticus Learning is a reliable source that provides you an online Professional Certification in FinTech course. This course by Imarticus Learning is associated with the SP Jain School of Global Management. You will get to learn via an industry-first approach and will get to study real-life case studies.

This course touches on many aspects/processes involved in the fintech industry like payments & lending, API, RPA (Robotic Process Automation), cryptocurrency management, blockchain, etc. You can choose from the Core Modules (for broad coverage) & PRO Modules (for in-depth coverage) of the aforementioned course.

Imarticus also provides several other courses like Pro Degree in Financial Analysis & PG Program in Finance and Accounts to know more about the financial services/industry. The Project: Paradigm Shift provided by the fintech course will help you in creating/transforming business ideas.

Conclusion

Personal capabilities are the main factor for upskilling in any industry. You will only end up working smartly if you follow the right career path. You will get to work on various projects by opting for the fintech course provided by Imarticus Learning.

It provides an excellent practical environment to implement the things learned in the course. Expert faculties which are associated with reputed firms/institutions will be teaching you if you opt for Imarticus courses. Start your fintech course now!

 

Predictions For The Future Of Big Data

Posted on by Imarticus Learning

While a lot of experts believe that there’s some great stuff in store for the future of big data, it is also true that technology will be greatly advancing throughout 2017. This is why there are a number of complex facets of big data, which are increasing by the day. Various attributes of big data, such as artificial intelligence and cloud computing, are believed to have a huge impact on big data analytics. There are a number of factors that exhibit the potential to change or more likely determine the direction at which big data is moving. For instance, there will soon be a number of customers who would replace the businesses, in demanding various amounts of data, to look for the cheapest hotels and understanding climate issues and similar concepts. There is a very acceptable idea today, of a reality where it would be the customers, the common man, if you may, who would be demanding personalized, tailored artificial intelligence technology, to suit their particular needs and demands.
While these seem like mere examples, with a tinge of realism, there are absolute chances of these becoming a reality very soon.
Ten years ago, all the data that was ever generated and accumulated, made up the highest denominations of storage space, which was namely Gigabytes or Terra-bytes, but the recent few years have made an explosion of data, into what is known as exabytes; this term roughly refers to billions of Gigabytes of data. This is where we derive the term ‘big data’ from, it is to denote the humongous amounts of data that has been generated, all over the world, in such a short amount of time. Regardless of whatever happens in other aspects of this field, one thing we can be absolutely certain of. That is, that data will be continuously growing, which means that soon there will come a time, when we will be talking about Zettabytes, which roughly amounts for a trillion Gigabytes.
Artificial Intelligence began its advent, as just a buzzword which was continuously used by sci-fi movie enthusiast and was mainly used to refer to technology only seen in sci-fi movies and the likes. Today, this term is no longer reserved for those, who are obsessed with technological gizmos, or those involved in science. It has very well become a part of our everyday lives, through various examples, like Google’s Allo, Microsoft’s Cortana and Apple’s Siri. There are absolute indicators that AI has full potential of transforming, from something nice to have to very essential technology to have. There are so many changes and futuristic developments that big data can make today, as well as in the future.
One of the biggest prediction is the fact that big data can result in various advanced applications for fields of national security, customer behavior tracking, weather forecasting, HR, sports, health and so on.

One prediction is definitely going to happen, which is that big data will have a better, smarter and a huge impacting role to play in the future.

Loved this blog? Try these blogs as well –
What’s Machine Learning All About?
Is Big Data Really Changing The World?

What is the best way to learn Artificial Intelligence for a beginner?

Posted on by Imarticus Learning

What is the best way to learn Artificial Intelligence for a beginner?

Over the past few years, the field of Artificial intelligence has displayed tremendous amounts of growth. AI is now driving businesses of billions of dollars across various industries and enabling enormous career opportunities.

If you have plans to learn artificial intelligence, it is the perfect time to start acting on it. This article discusses the best way to master AI for beginners.

1. Begin with the Basics

The first thing you have to do is unlearn everything about the AI. Clear all the preconceived notions and make your mind open and fresh for learning. Now you can actually start learning.

Start with the basics. Learn about the various technologies involved and their objectives. It will help you get oriented at the beginner level. You can refer books or blogs to get through this step.

2. The podcasts and Videos
The next step is listening to podcasts and videos. It will give you more comprehension about the industry, application of different technologies, the effect of them in our real life, various techniques in them and many more.

Often these videos and podcasts come with jargons and concepts involved. So, it is important to have a fair amount of familiarity with the basics.

3. Guided Courses
A dedicated artificial intelligence course is one of the most important practical ways of mastering AI. A guided course will take you fully into the world of Artificial Intelligence. You will get global exposure to the skills required. Usually, such a course will brush up on the basics you have already taken care of and then help you develop the right technical skills required to work with AI.

If you are planning to join the industry, such a course is inevitable. A guided course will also put you in touch with experts of this technology and excellent study materials. So, it is important to attend a guided course for a complete learning experience. Along with that, you will get a certification proving your excellence in AI at the end of these courses. It will help you during the search for a job.

4. Projects
The best way to learn anything is to practice it properly. So, it is essential to indulge in lots of projects and gain practical exposure. You will be doing capstone projects during your course. From those projects to the projects you are personally interested in, you have to constantly work and build your portfolio. By doing this you will be able to master this skill in a very short time.

For a beginner with very low prior experience with AI, these are the little steps that make sense. Also, through this, you will be able to find some time to process the transition between each step and prepare for the next one. Within just a year, this road map will equip you with AI capabilities that are good enough to be a part of the industry. So, start your process as soon as possible and take part in the AI revolution going all around the world.