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Tag: machine learning course

Use of Machine Learning in Social Cause!

Posted on by Imarticus Learning

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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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!

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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.

How AI and ML Affects Cybersecurity?

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The digital world has been shaken up many a time by cyber-attacks which only continue to get sophisticated and more complex. True to the fact that this era is being referred to as the ‘digital dark age’, data fraud and cyber attacks are two of the top 5 global risks in the world today, not far behind natural disasters and abject weather situations

However, AI and ML are being leveraged to take the battle against cyber attacks up a notch. The future of artificial intelligence will see cybersecurity being taken off the hands of human resources and automated to achieve more efficient results in real-time.

How AI and Machine Learning Affects Cybersecurity

Detection of Anomalies

Before prevention comes detection– and that was one of the many failings of a human-based security force that couldn’t keep up with increasingly complex digital threats. Deep learning and access to databases spanning decades have made AI and ML capable of detecting anomalies in existing systems and tracing sources, whether internal or external.

Pre-emption of Strikes

AI and ML are crucial in the continuous battle against cyberattacks. That said, they’re also instruments used by hackers to conduct strikes. In a case of fighting fire with fire, AI and ML can be leveraged to pre-empt such strikes to identify vulnerabilities and identify threats in services from as basic as emails to as confidential as financial transactions.

Prediction of Threats

As any Machine Learning course would teach, pattern prediction is a perk of AI and ML that can be used in achieving cybersecurity targets and maintaining defenses against breaches. Emerging technologies such as these can successfully predict the likelihood and type of future threats as well as identifying the source to take preventative measures. The same logic can be implemented internally, to analyze internal systems and close up loopholes and weak links.

Improvement of Biometric Authentication

Gone are the days when passwords and swipe patterns were the most innovative authentication technology could get. Biometric authentication is the new norm– think face ID, fingerprint technology– but inaccuracies and failings were always a concern. Today, developers are leveraging AI and machine learning to rid biometric authentication of its imperfections to make it more stable, reliable and more difficult to hack. This is crucial because biometric authentication affects so much more than cellphones and email addresses– it’s used for ID verification, financial authentication and more. Therefore, the stakes are much higher.

Management of Vulnerabilities

In the days when security was largely relegated to antivirus software and human resources, vulnerabilities would be manipulated and turned into a threat or an outright attack before measures were taken. In contrast, AI and ML allow firms to identify and manage their vulnerabilities well in advance so that the approach is preventative rather than scrambling for a cure. AI and ML use a plethora of combinations and tactics to identify these vulnerabilities, such as:

  • Dark web leads
  • Hacker discussions or threats
  • Threat patterns
  • Frequently targeted systems or divisions
  • Risks and losses at hand

By effectively leveraging emerging technologies, firms can meet cyber-threats head-on, even strike pre-emptively, instead of dealing with thousands of dollars’ worth of losses and bills in the cleanup.

How Machine Learning is a Boon For License Plate Recognition?

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Machine Learning has weathered some tough days, pulling through to become a powerful technological force capable of leading and creating real-world change.

A prime example of this is the use of Machine Learning in automated surveillance systems on roads in busy metropolises. License plate recognition, for example, has transformed from a pipe dream to current reality, thanks to image processing and recognition capabilities of AI and ML systems.

Out of all the solutions posited towards vehicle movement and management, machine learning solutions are the most accurate because:

  • They derive crucial information from vehicles on the move
  • They are real-time and efficient
  • They are self-taught and don’t need human resource support

What is license plate recognition?

It is the process of detecting and identifying license plates, through Optical Character Recognition, to be run against an existing database. The most basic recognition system consists of three steps:

  • Detecting the actual license plate
  • Segmenting characters into individual images
  • ML algorithms recognize each character

License plate recognition is a boon for many government and private entities, especially when used in tandem with an existing robust database.

Where is license plate recognition thought to be useful?

As the number of vehicles increases across metros and crime also shoots up, many law enforcement bodies find it increasingly difficult to track offensive vehicles and levy fines, book drivers or conduct searches in good time.

Identification of traffic defaulters: ML-based license plate recognition systems allow bodies like the police and traffic control to identify vehicles that break rules like driving over the speed limit, not wearing seatbelts or having broken headlights. Depending on the business of the junction or road, the technology can be used in tandem with human traffic police to ensure accuracy and efficiency.

Recognition of abandoned wanted or stolen vehicles: By integrating ML-based systems on mobile devices, police or other law enforcement bodies can recognize and identify vehicles whether wanted, stolen or abandoned. This reduces a lot of time wasted on identifying vehicles or organizing paperwork. It also allows the enforcers to contact the person in question or make arrests were necessary.

Automating of toll collection: By adding LP systems to toll booths, law enforcement authorities can conveniently collect tolls without the need for actual service personnel manning the station.

A good LPR system is set apart from the rest by the following salient features–

Functions in all environments: Using humans to identify license plates in extreme weather leaves plenty of room for errors. Good LPR systems work regardless of the environment– stormy, cloudy, foggy or dusty– thereby heightening the accuracy and increasing real-time problem solving or escalation. They also function on low-resolution images, when the image is at an angle or where the image is blurry and of a fast-moving vehicle.

Identifies plates on all vehicles: Depending on the vehicle, indeed even the country it’s in, the license plate can differ in color, font, and sequence. It could also be placed on different parts of the vehicle body, which means a good LPR tool needs to be able to identify the right markings in the right spot and identify the sequence correctly.

The final word

Detecting and recognizing license plates is a task that grows increasingly cumbersome as the number of vehicles and registration requirements increase. To get a head start on this emerging field, students, freshers, and industry professionals must engage in a Machine Learning Course and open up new avenues for themselves. Using a license plate recognition system that is dynamic, ML-based and scalable is a positive step towards managing the chaos and getting real-time, positive results.

What Is The Quickest Way to Learn Math For Machine Learning and Deep Learning?

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Synopsis

Math is integral to machine learning and deep learning. It is the foundation on which algorithms are built for artificial intelligence to learn, analyze and thrive. So how do you learn math quickly for AI? 


Machines today have the ability to learn, analyze and understand their environment and solve problems on the basis of the data given to them. This intelligence of the machines is known as artificial intelligence and the ability to learn and thrive is known as machine learning. Algorithms form the crux of everything you do in technology and a Machine Learning Course provides you with an understanding of the same. 

 

Today, individuals who are proficient after completely a Machine Learning Certification is highly sought after and employed. Companies invest a large sum of money to have professionals trained in AI as the applications of AI are vast and cost-effective.  It is a lucrative career to pursue one that involves complex and challenging problems that need to be solved in creative ways. 

 

Mathematics forms the foundation of building algorithms as all programming languages use the basics. Binary code is the heart of machines and the language used to teach them things is the programming language. So do you pursue Machine Learning Training, and also learn math quickly at the same time? 

 

Here are a few ways to understand how math is applicable in AI 


Learn the Basics 

Important sections such as  Statistics, Linear Algebra, Statistics, Probability and Differential Calculus are the basics of math that one needs to know in order to pursue learning a programming language. While this may sound complicated, they form the basis for machine learning, so investing in courses that teach the above-mentioned functions will go a long way in programming.  There are plenty of online resources that are useful repositories when it comes to learning math for deep learning. 

 

Invest Sufficient Time

Learning math depends on the ability to absorb and apply the math learned in machine learning. Applications of statistics, linear algebra is important in machine learning and hence investing 2-3 months to brush up on the basics go a long way. Constant applications of the lessons learned also helps when it comes to math for AI. Since the principles are the same but the various derivatives and applications can change with the algorithm constant practice and brushing up will help while learning the code. 


Dismiss The Fear

One of the biggest ways to learn math quickly for machine learning is by dismissing the fear associated with numbers. By starting small and investing efforts, one can move forward in the code. Since there is no shortage of resources when it comes to learning math, taking the initial step and letting go of any fear towards the subject will greatly help. 


Conclusion

Learning a programming language whose principles are based on mathematics can sound daunting and tedious but it is fairly simple once you understand the basics of it. This can be applied while programming for machine learning and artificial intelligence. 

How Artificial Intelligence Has Changed The Way We Secure The Data?

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How Artificial Intelligence Has Changed The Way We Secure The Data?

Though the concepts have been around for ages the past two decades have seen a phenomenal in ML/AI applications. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence without the use of any human intervention or explicit programming. 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.

The availability of very large databases of Big Data itself and the proliferation of cloud technology and cloud computing have directly contributed hugely to allowing ML/AI to sift through these very large volumes of very big data and mimic the human brain’s logic in inferential and logical predictions, gaining foresight or producing predictive insights into such data.

The figures and data volumes are mind-boggling and cannot be humanly attainable without ML/AI applications. It is estimated that by 2030 almost all businesses will use ML/AI techniques and the market value of training data sets using a Machine Learning course will see a market of 13trn USD!

AI in cybersecurity:

Cybersecurity is the most demanding and promising area for ML/AI. In theory, if the machine is given complete data both good and bad then it should throw up any pattern that is related to unusual behaviour or malware in the database. This implies that

  • Your model needs to effectively harness a huge volume of available data including malware, and good and benign data.
  • The data pipeline needs the data scientists and engineers to build and maintain a continuous process for the sampling of data sets and training effective data-based models.
  • The goal of providing insights needs to be categorized by specialists in the domain to sift the bad from the good and the process and results need to be justifiable, logical and explained.

Sad but true, is the fact that many  ML/AI security solutions lack in meeting these criteria. 

The process used:

A basic tenet of cybersecurity is a multi-security-layered defence in depth rather than just the use of ML/AI technologies while scanning the system periodically for user-accessed content. The area of file downloading should stop SSL-encrypted communications between the user-client and destination servers and allow the scanning of every file involved in order to ensure scanner perceptibility. This is time-consuming and affects UX. However, such scanning is a compromise of providing a secure user experience with the white-listed files while effectively blocking threats and malware.

Once threat intelligence has been deployed there is still the zero-day or unknown threats which loom large. Such threats are sandboxed in a virtual environment mimicking the user environment and studied before labeling them as bad or good. ML/AI techniques with the deployment of artificial intelligence course trained algos can effectively do this process almost instantly and avoid the user having to wait for long periods of time.

Hackers use exploitative kits which borrow delivery techniques and exfiltration of previous known threats and attacks which are easily identified by ML/AI models trained to identify variants that are polymorphic. Importantly, answering queries on why particular data sets are classified ‘bad’ should use the expertise of domain specialists who are capable of explaining the triggers and test results in order to ensure better and more accurate predictive models.

Training the models:

There are two kinds of learning and making prediction models. One is unsupervised learning, which is based on data structure and free from any human bias in the selection of data sets or malware features. Supervised learning, on the other hand, uses human intervention in sampling and labelling the database while using labelled data for the extracted prediction model. Which method is better depends on the suitable parameters prior to training the artificial intelligence course of algorithms that result in the predictions?

The best security areas where AI/ML can help:

The cybersecurity challenges are confrontable by smart ML/AI 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. They can also detect unusual behaviour once they are trained in recognizing normal patterns on your profile or account. A red flag is immediately raised and you are asked to verify the transaction. This makes the hacker’s job harder and your account safer and more secure.

In conclusion:

An artificial intelligence course that can train the AI/ML model under expert guidance from cybersecurity and data science experts is a valuable tool in mitigating the effects of cyberfrauds. Do your course on AI and ML at the Imarticus Learning Academy to emerge career –ready in these fields.

For more details in brief and for 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, Bangalore, Hyderabad, Delhi and Gurgaon.

What Are The Machine Learning Use Case in IT Operations?

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Machine learning and AI can together take your enterprise to better productivity and efficiency which translates to better profitability.ML is used to make the algorithmic programs executable and uses many ML tools. The oldest tool for ML is Shogun. Without ML none of the tasks in an AI device can run.

ML’s iterative capacity is crucial as the ML trained models independently adapt to new data. Their self-learning ability akin to the human brain learns from previous experiences and computations to give repeatable, accurate and reliable decisions and results. With the advent of data analytics, the increased dependence on AI devices and data being generated by the nanosecond Machine Learning Training has gained popularity and impetus in the last decade.

Why training is crucial:

Nearly all employees are short on workplace skills and need training in these weak areas whether it be learning a new language, an advanced technique in your expertise area, or plain communication and change management. A Machine Learning Course program helps assimilate best practices in the skills required at your job. It also progresses your career to the next level with certification. Departmental training programs improve your morale and bring all employees with similar knowledge and skills on a single platform.

ML use cases for IT operations:

For sheer want of space and time, we shall discuss such cases superficially with the intent of defining the areas where ML is beneficial for the IT operations. Some of the complex areas where ML is used are:

RPA-Robotic Process Automation:

One can undertake digitization of processes in a few months instead of many years with legacy systems. The legacy system need not be replaced since ML ensures the bots can operate on such a system based on your ML algorithm. This includes all processes from existing process analysis, RPA bots programming, and humans using the bots. The implementation time is greatly cut down and the cost of replacing legacy systems avoided. Take a look at such times in the below chart.

That having been said, it is also necessary to be aware that process complexity determines analysis and programming times, and the automation levels and time-taken for human interaction with the bots can vary. Other factors that affect the time are the training through a Machine Learning Course for the bot-interacting employee and error testing.

Predictive maintenance:

Predictive maintenance to minimize operational disruptions is another crucial area where ML helps.  The uptime and maintenance costs are the factors directly impacted by the regular maintenance of robots, bots, and connected machinery. In business parlance, this translates into cost savings of millions of dollars. A Nielsen study shows that some industries suffer downtime costs of 22,000 USD per minute.

Manufacturing/Industrial analytics:

Many industrial assets like Chillers, Boilers, Batteries, Turbines, Transformers, Valves, Circuit Breakers, Generators, Meters, and Sensors are all connected to the ML through IoT platforms. Popularly referred to as industrial-analytics ML helps reduce maintenance costs, manufacturing effectiveness and reduces downtime throughout the system from production to logistics.

Supply chain and inventory optimization:

ML leverages the optimization of such processes to the next level greatly reducing supply chain costs while increasing the organization’s efficiency and productivity.

Robotics:

ML helps automate physical logistics and manufacturing process by introducing automated advanced robotics. The resultant effects are improved effectiveness and time-saving.

Collaborative Robot:

Cobots use ML to achieve automation with a flexible process. The Cobot’s ML process, engineers the automated response of flexible robots to learn from past experience and mimicking.

Qualitative benefits:

Some of the benefits of using ML-enabled use cases are: 

  1. Better performance.
  2. Improved production continuity and rhythm.
  3. Increased worker productivity.
  4. Increased availability of time for repair and maintenance work.
  5. Better team preparation and interventions.
  6. Effective management of inventories and spare parts.
  7. Reduced costs on energy.

A study conducted by McKinsey reports that ML has the potential to use cases and provide the following benefits.

  1. Downtimes reduced by 50 percent.
  2. MTBF increased by 30 percent.
  3. The useful life of machines upped to 3-5 percent.
  4. Inventories in spares cut to 30 per cent.
  5. Maintenance costs declined by 10-40 percent.
  6. Injuries to the workforce declined by 10-25per cent.
  7. Waste reduces by 10-20 percent.
  8. Advanced analytics reduces environmental impact, improves employee morale and customer satisfaction.
  9. Betters product quality and improves performance.

In parting, if you want to do a Machine Learning Course to effectively learn ML applications try Imarticus Learning. Their machine learning training fast-tracks your career with widely-accepted ML certification. 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, Gurgaon, and Ahmedabad.

What Are The Best Courses For Cyber Security Using Machine Learning?

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What Are The Best Courses For Cyber Security Using Machine Learning?

Today everything is online and from such activity the sheer volumes of data generated, its management and security from misuse is a matter of concern that cybersecurity professionals are tackling on a war footing.

ML and AI have seen huge developments in the last decade in conjunction with the rapid growth of data and data analytics. Most organizations value their data and ML as organizational assets. So any threat to them or the devices connected to the algorithms is considered a serious cybersecurity threat. And cybersecurity depends more and more on ML since it holds potential for analyzing large volumes of data, structure and process data in real-time, and present instantly any threat to its intelligence as it occurs.

To learn machine learning is the mainstay of threat intelligence which alerts you so you can deal with mitigating the threats. Gone are the days of incoming alerts and handling attacks. Today we have advanced ML where threat intelligence is the buzz word.

Why? Because, ML has huge applications in helping organizations defend against malware, apply TI (threat intelligence), make unknown connections, identify key parameters, the transformation of unstructured text, threat actors, and such relevant risks.

Cybersecurity ML algorithms and software help: 

  • Understand why and how ML will affect the future of cybersecurity
  • AI techniques add value to ML to make the analysts more effective.
  • Obtain insights on ML processes for threat intelligence.
  • Help to detect future threats through predictive analytics.

How to become a cybersecurity professional:

Choosing a career in cybersecurity or opting to change careers to it is a great career move at the moment. You will have to learn machine learning with a reputed training institute like Imarticus Learning who are renowned for fast-tracking career options and enhancing technical skills required for careers in the latest emerging fields. Such learning courses are available online with a host of reading and comprehension on cybersecurity risks and its mitigation. However, classroom sessions and supervised learning will also be needed to gain practical and implementation skills.

You could start with an entry-level position gaining experience in security, risk management or IT and move your way up to a mid-level role as an analyst, security administrator, risk auditor or cybersecurity engineer. To sharpen and hone your cybersecurity skills advanced training and certifications will be required before you can actually practice as a security consultant.

Cybersecurity education:

A formal after-school college experience for an associate’s degree will take four full-time semesters or two years to start as an Intern. A bachelor’s degree could last 8-semesters or four years and the master’s degree will last two years or another 4-semester duration should help you learn all about the theory behind cybersecurity.

The actual practice of writing algorithms can be honed by online challenges participation, certifications and hackathons on Kaggle. The necessary attributes for cybersecurity would be proficiency in English, mathematics, and statistics. Combined with a certification you are set to start your career according to the BLS.

The top-10 roles in Cyber Security:

The field of risk evaluation, mitigation and prediction are growing with data analytics and data taking center stage in modern times. Take your pick of career paths from a few of the roles enumerated here to always be in demand.

  • Ethical Hackers.
  • Security Systems Administrator.
  • Security Consultant.
  • Computer Forensics Analysts.
  • Information Security Analyst.
  • Chief Information Security Officer.
  • IT Security Consultant.
  • Penetration Tester.

The top cybersecurity certifications:

Certifications are essential to your resume and offer employers a real-time measurable scale of your skills in cybersecurity and validate that you can implement and use the learn machine learning applications for risks and security of cyber systems effectively and practically.

The top certifications are: 

  • The ISC (2) Certification
  • CISM- Information Systems Manager
  • CISA- Information Security Auditor
  • Information Systems and Risk Control Certified
  • CEH- Ethical Hacker
  • Tester GIAC/ GPEN Penetration
  • Cyber Security Courses     State Approved

Payouts:

The Cybersecurity professionals have a median salary of 116,000 USD. At an hourly rate of 55.77 USD/ hour, it is almost thrice the national average income offered to full-time workers. The BLS reports make the high salaries a very attractive feature to make cybersecurity your dream career.

Conclusions:

The cybersecurity professional is highly paid and has immense job scope in a variety of roles. Formal education, practical skills, certification and performance at the end-of-the-day will set you apart and help your career progression. There definitely is a huge demand for the cybersecurity professions which will continue into the next decade according to most reports on Glassdoor, Payscale, and BLS.

Resources are available aplenty to make cybersecurity your career no matter where you live. The Imarticus Learning courses, unlike many online programs, have limited class sizes meant to enhance learning, certification attainment and networking.

So hurry and learn machine learning! Also, for more details 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, Gurgaon, and Ahmedabad.

How Important Is An Application Domain In Regards To Post-Graduate In Machine Learning?

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How Important Is An Application Domain In Regards To Post-Graduate In Machine Learning?

If you wish to do ML research, either academic or in the industry, then you need to be a great coder and get to working with the elite in the ML domain. But, for the following reasons, you would still have the advantage.

  1. ML research is the right path since there is an acute shortage of qualified practical doctorates in ML. Spending a few years under the best in the domain of ML can actually help improve your knowledge and practical skills through effective mentorship. There are ML mentors like Geoffrey Hinton, Nando Freitas, Yann LeCun, Andrew Zisserman, Andrew Ng, etc who are well known for their work and contribution to research.
  2. Attaining proficiency in Machine Learning Training needs proficiency in data, mathematics, statistics, linear algebra, calculus, differentiation, integration, and a host of other subjects to do research in the ML domain. If you have these it still takes 3-5 years before you get to writing effective algorithms.

Most software engineering jobs in industries do not provide you time for reading or research. Further, you will lose out on practicing your development skills. Since the ML programs on the market today are more or less ready to use, it makes perfect sense to learn Machine Learning Course.

To answer which way you should proceed read on. One can opt for any of the two ways of applying ML. To research and applications. Let us explore these choices.

ML research:

Learning about the science of machine learning is actual research. An ML researcher is constantly exploring ways to push the scientific boundaries of the science of ML and its applications to the Artificial Intelligence field. Such aspirants do have a post-graduation or even a Ph.D. in CS with frequent and periodical publications of their research presented at the top ML conferences and seminars. They are visible and popular in these research circles. The ML researcher is looking for something to improve upon and thanks to their efforts technology are always cutting edge and progressing in pace with developments.

When you need to tweak your applications and seem to go nowhere with it, it is these ML researchers who can get you up from 95 to 98 percent accuracies or more by offering you a personalized and customized solution. The ML researcher really knows his wares well. The only drawback is that he may never get the opportunity to actually deploy his solutions in applications. He knows the theory and is devoid of practice in SaaS delivery, deploying to production or translating the research finding into a practical app.

Machine Learning application:

In comparison to the researcher, the ML application is about the engineering of ML. An ML engineer will take off from where the researcher left. He is adept at using the research and turning it into a valuable practical application or service. They are adept at services of cloud computing and services like the GCP of Google or AWS from Amazon. They are fluent in Agile practices and can diagnose and troubleshoot anywhere in the SDLC of the product.

These ML engineers are often not as recognized as the ML researcher for want of a decorated Ph.D. and referral citations. But they are the people you must go to if you want your customers to be happy with ML-driven products. These application engineers have years of experience and deployments of thousands of products to their credit.

Consult an ML application engineer before you deploy products or services in the market. Your decisions should be based on your business domain, the product or services on offer and the methods of delivering it to the targeted market.

Expected payouts:

The Gartner report states that by 2020 the domains of ML and AI will generate 2.3 million jobs. Digital Vidya claims the ML career is great since the inexperienced freshmen land jobs that pay 699,807- 891,326 Rs. If your domain expertise is in data analysis and algorithms your salary could be 9 lakh to Rs 1.8 crore Rs pa.

Conclusion:

For most teams/businesses and teams, ML has many apps that are applicable to its specific needs. You do not need to reinvent it but must know how to use it better. Its an awesome tool for the enterprise and customer’s too! Learn ML at Imarticus Learning. Besides learning how to tweak the ML algorithm through hands-on assignments, project work, and workshops you get assured placements, soft-skill, and personality development modules with a resume writing exercise. Hurry and start today!