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Tag: machine learning certification

5 ways AI is Utilized in Advancing Cancer Research

Posted on by Imarticus Learning
Reading Time: 2 minutes

When it comes to the health of a person, life and death become a matter of problem. Health care centers and medical professionals all over the world are now leveraging the power of AI, to research a plethora of ailments. One such case is cancer research. Cancer is a disease which results in the uncontrollable division of cells and hence the destruction of body tissues. This problem can be solved with the help of artificial intelligence as it is nowadays providing favorable outcomes in every field. It can help in early detection of cancer and the treatment can prove to be very successful.
 Here’s a list of 5 ways Ai is being utilized in advancing cancer research: 

  1. Machines fed with adequate data and programmed with advanced algorithms can make use of past medical records during surgery of a patient. This is possible only with the help of artificial training. Researchers have found that there are approximately 5 times fewer complications in a robotic procedure of surgery in comparison to surgeons operating alone.
  1. Artificial intelligence can be used to interact with patients by directing them the most effective care, answering the questions, monitoring them and providing quick solutions to their problems. Most applications of virtual nursing include fewer visits to hospitals and 24 hours of care to the patients.
  1. Healthcare providers also make use of artificial intelligence to diagnose patients. Early diagnosis of cancer has now become a necessity, as any delay can cause a difference between life and death.

According to a recent study, artificial learning methods can help to classify the patients into high or low-risk groups. The study further added that AI has a great impact in the area of cancer imaging as artificial intelligence can analyze more than 10000 skin images with higher sensitivity.

  1. Complicated tests and analysis, such as CT scan and internal imaging have turned out to be hassle-free with the help of AI-enabled systems. It reduces the chances of any manual error and helps the doctors to diagnose the condition before it becomes critical.

According to a study AI has proved to be 99% accurate and more than 25 times faster in detecting breast cancer. Artificial intelligence can also be used to find out vertebral fractures if any.

  1. AI has the potential of developing lifesaving drugs and saving billions. Engineers have developed algorithms that can analyze the potency and effectiveness of the medicines developed for treatment. It also helps them to make better decisions related to healthcare.

Most of the people even use wearable technology based on artificial intelligence to check out their sleep patterns and heart rate. Applying artificial intelligence to detect cancer can inform healthcare providers about specific chronic conditions and manage the disease in a better way.
So there are various cases where artificial intelligence can find its application. Artificial intelligence training can help the individual to enhance their skills and knowledge in the field of artificial intelligence.
Imarticus Learning is one of the leading institutes that provide numerous courses in data science, machine learning, blockchain, etc. The institute takes pride in helping students make a career in artificial intelligence. AI has improved its application in the past few years and is expected to revolutionize the world in many ways in the coming years. Thus, having good artificial intelligence training will prove to be useful in all fields. You can have such good knowledge with the help of experts and qualified staff at the institute which can help you to shape your career in a better way.

Which Are The Powerful Applications of Machine Learning in Retail?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Introduction

Machine Learning is one of the top technological trends in the retail world. It is having a great impact on the retail industries, especially in e-commerce companies like Flipkart, Amazon, eBay, Alibaba, etc. These companies completely rely on online sales, where it is common to use Machine Learning or Artificial Intelligence nowadays.

Companies like Flipkart, eBay, Amazon or Alibaba are the successful companies who have integrated AI technologies across the entire sales cycle. Not limited to these big companies, there are also numerous small companies, some of which are already using this technology and inclined towards using this technology for the growth and development of their businesses.

How can Machine Learning Change Retail

We can think of three key scenarios when Machine Learning comes into play:

  • Finding the Right Product; Enabling your users or your customers to find the right product at the right time. We can move people away from the regular textual searches and help them find the products more visually.
  • Recommending the next product; The other aspect is how do we help them recommend the right product at the right time. One of the things that we increasingly deal with  is a choice. We can help customers by giving them the right product at the right time on the basis of their prior user behaviour.
  • Understanding the feedback; Once the product is released into the wild, we are interested in knowing how the product fares, people’s opinion about it, their suggestions in relation to the particular product. We can get a better feeling of sentiment, understanding what they do with the product, and get a better answer that can drive your life cycle from a product’s development perspective, marketing perspective and multiple downstream activities.

Applications of Machine Learning in Retail

As discussed above, those three are the key scenarios when Machine Learning is used in Retail. Some other applications of Machine Learning in retail are discussed below:

  • Market Basket Analysis: This is the traditional tools of data analysis in retail. The retailers have been making huge profits out of it for years. This is totally dependent on the amount of organization’s data that is collected by customer’s transactions. This analysis is done using Association Rule Mining algorithm.
  • Price Optimization: The formation of price not only depends on the cost to produce an item but also on the different types of customers and their budgets as well as other competitor’s offers. The data received from various sources define the flexibility of prices at different locations, different customers’ buying attitude, seasoning, and the competitor’s pricing. The retailers attract customers, retain the attention and realize personal pricing schemes with the help of real-time optimization.
  • Inventory Management: Inventory is nothing but stocking goods for their future use. This means retailers stock goods in order to use them in times of crisis. Their aim is to provide a proper product at the right time at the right place and in proper condition. A powerful machine learning algorithms and data analysis platforms help in finding not just the patterns and correlation but also the optimal stock and inventory strategies.
  • Customer Sentiment analysis: Sentiment analysis is performed on the basis of Natural Language Processing, and text analysis to extract positive, neutral or negative sentiments. The algorithms run through all the meaningful layers of speech. Here, the output is the sentiment ratings.
  • Fraud Detection: Fraud detection is one of the challenging activities for retailers. The reason for fraud detection is an immense financial loss. The algorithms developed for fraud detection not only recognize the fraud and flag it to be banned, but it also predict future fraudulent activities.

Conclusion

In the article, we briefly discussed the applications of Machine Learning in retail.

Ways To Use Artificial Intelligence In Education

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Ways To Use Artificial Intelligence In Education

Do you know that AI is very present in all our lives and has pervaded almost every space? Not just the imaginary humans with chips portrayed by fiction writers and science fiction movie makers but just look around.

Google searches, automatic sensors for reversing your car, automatic lens adjustments and light settings for those perfectly timed selfies, Google maps to take you straight to your destination, MRIs to detect those illnesses you never thought you had, multiple-choice answer sheets scored automatically on online learning sites, paying bills online, that favorite app you just downloaded and everything I between. They all run on the artificial intelligence courses of the self-learning algorithms of machine learning help make machines truly aid to human thinking through deep learning and neural networks.

Though AI has actually taken over most of the human tasks, they are still a long way off from replacing human beings and the one area where they have tremendous application potential is in education. Let’s reiterate that the basic aim of artificial intelligence courses and neural thinking is not to replace humans but to help them with repetitive tasks and data sifting far beyond the limits of the best human brains. So, in the future, AI and humanoid robots will not replace teachers. But they will transform how we learn, and what to learn and go a step further by helping us learn. That includes the teachers too who are constantly learning too!

Why AI matters in education:

Let us explore how AI is going to bring its benefits to the education experience of the future. The class sizes keep increasing with compulsory education and teachers are often facing many challenges in giving attention and help to the large numbers of students. A big challenge like this has been simplified by incorporating computer programs that allow each student to follow his own pace and learning curve. Individualized learning modules can help find knowledge gaps and personalize the learning materials to fill in the gaps.

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

The newer methods of experiential learning at educational institutions use advanced techniques of AI, machine learning and deep learning in instructing and teaching like chatbots and learning bots. A differentiated AI style of learning deals with the most effective style to help the student learn. Adaptive artificial intelligence courses based learning curates the learning exercises matching them to the student’s needs and knowledge gaps. Competency-based AI tests aid the students to gauge their learning levels and progress from thereon. Using all these three types of learning AI can test how well the students can adapt their learning to applications of it and thus promote the progress of students based on individual interests.

The benefits:

Some of the benefits of artificial intelligence courses that can be harnessed are: 

1. Grading, scoring, and such repetitive tasks can easily be handled by AI.

2. Personalization of educational software can be need-based and adapted to individual learning curves.

3. Lacunae and learning gaps can be predicted and rectified with suggestions for learning materials and courses needed to improve.

4. Tutoring through subject-specific learning bots, online self-paced courses etc can support students.

5. The feedback route is almost instantaneous and can be gainfully harnessed by both educators and learners.

6. AI has changed the way we search for and interact with data. Just Google for information on anything and everything is what 95% of the people do to find information.

7. AI will make teachers more effective and ever-learning educators.

8. AI will develop human skills and make trial-testing-and-error learning the norm.

9. Data harnessing and empowerment will change the learning experience using AI to find, support and teach students.

10. AI can offer both offline and online resources which will alter where we learn, how and who teaches them and help apply to learn to basic implicational skills.

Conclusions: 

What do you think would be the results of AI in education and the learning process? Yes, the education field is going to be very different from what we now see it as. Skills in learning applications will count for more. Jobs will be linked to skills and not degrees. Certification will emerge as a measurable tool of skills. And, if you want to explore more, why not do artificial intelligence courses at the reputed Imarticus Learning institute?

What Machine Learning Has To Do With Your Personal Finances?

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Reading Time: 3 minutes

What Machine Learning Has To Do With Your Personal Finances?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Is A Machine Learning The Next Step Of Smart Learning?

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Is A Machine Learning The Next Step Of Smart Learning?

2015 was rife with stories of ML and self-driving cars. Again a decade ago people were abuzz with robots doing the repetitive tasks, and human intervention being all about seasoned judgment for complex tasks. And then, cars were self-driven and computers really proved that they were fast approaching the machine learning course of human intelligence with self-learning algorithms and AI taking over a data-driven world.

Both AI and ML started with helping the human coded programs helping computers spot data patterns and inform algorithms which made foresight, insights, decisions and data-driven predictions. And then, the volumes of data became huge and machines needed a machine learning course to be able to help humans with cleaning and sorting the data.

 

So self-learning and deep learning algorithms soon taught the AI-driven computers to get smart and self-learn. As data became larger, their ability to accurately parse and clean data and provide insights increased. And so, we developed new self-learning ML algorithms which are the latest weapons against terrorism, cybersecurity, climate change, and cancer applications.

The Master Algorithm authored by Pedro Domingos, claims machine learning is the new basis and infrastructure for all applicational algorithms. He described it as the Higher Education switchboard. When data privacy became an issue in the UK in 2014 the US K-12 dialogue led to concerns in over-testing and by the end of the year, more than a hundred vendors of EdTech voluntarily signed the pledge for data privacy.

The series on SmartParents led with the argument that personalized learning is data-based and that parents with access to such data should decide on the privacy of the data. The policymakers were forced to embrace both data privacy and personalization.

Today ML and Big Data techniques are part of our lives and play a big role in informal and formal education. Let us see how we can make learning smart with machine learning. Below we list the areas and resources available to parents and teachers alike for

1. Learning data analytics and applications in a machine learning course that can help track student knowledge while recommending further learning steps. Here are some resources for learning systems that are adaptive.

Ex: ALEKS, DreamBox, Knewton and Reasoning Mind. Here’s a treasure for game-based learning:  Mangahigh and ST Math.

2. Beginning learning on content analytics that is used to optimize and organize content-modules.

Ex: IBM Watson Content Analytics and Gooru.

3. Scheduling for math learning that teams students needing help with teacher resources in real-time and dynamically.

Ex: NewClassrooms which uses ML and data analytics to schedule mathematics learning sessions.

4. Scoring and grading systems to score and asses on a large scale the student answers to the computer and other assignments and assessments: The grading could be peer grading or automatic ML grading.

Ex:  Lightside by Turnitin and WriteToLearn by Pearson help detect plagiarism and score essays.

5. To identify new opportunities through tools for process intelligence which analyze both big unstructured and structured data while enabling the visualization of work-flow.

Ex: Clarity from BrightBytes provides a strength gap analysis by reviewing best practices and research to build evidence-based frameworks.

IBM SPSS  and Jenzabar are systems for ERP-Enterprise Resource Planning which help the formal higher education schools and institutions in enhancing campus security, improving financial aid, predicting enrollment, and boosting student retention.

6. In helping match schools and teachers like TeacherMatch and MyEdMatch which harmonize online the recruitments of teachers.

7. For learning data mining and predictive analytics from experts try Map patterns of expert teachers and Improve learning, retention, and application articles.

8. For a host of applications in the back office are school bus scheduling EDULOG, Evolution, and DietMaster.

Parting Notes:

There is no doubt that the earlier you learn the more skilled you become. To ensure smart learning in ML every student and faculty member must learn at the earliest the impact of ML, AI, and Big Data. No matter what the pathway your learning curve should be enhanced with skills at the earliest.

Do you want to join a machine learning course at the Imarticus Learning Academy where data scientists hone their skills in AI, Big data and ML? Their courses also include personality development modules and train you in the soft skills required to emerge job-ready and with the right skill sets.

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.

How To Pay Taxes Without Leaking Your Personal Data Using Machine Learning?

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How To Pay Taxes Without Leaking Your Personal Data Using Machine Learning?

The cutoff date to file taxes in the US (April 15th) teaches all taxpayers many a lesson. Did you know that 1 in 3 Americans wait until April each year and get stressed when filing taxes? The major fallout of this behavior is that besides the stress and worry, one is tempted to cut corners and hurry through tax filing thereby exposing themselves to potential personal data loss or stealing.
Think about this fact. Tax scams saw a 60 percent increase in 2018 in comparison to the previous three years where the rates had actually shown a decline. That is why we are going to explore what we need to do when we file taxes to ensure safety, privacy, and efficiency of the machine learning course working behind the filing of our taxes.

Password strength:

Strong passwords can protect your online tax account. Use the IRS recommendation of a long password with at least one uppercase letter, one symbol, one lowercase letter, and one number. Avoid using your name, child’s name, pets name, DOB, school name and such easily discoverable data. Once they guess your password your account can be comprised at their will and social media sites like Facebook, Instagram, Twitter and such are always used by these scam generators to study your profile.

Watch for phishing:

Online phishing by overseas cyber criminals use websites which look very like the original IRS site to scam you. Once you enter your details on the link sent to you your data has been compromised and security breached. Here’s what to look for. An email that claims you will face legal action and prompts you to use the link to provide your personal details. Don’t panic.
Also look for giveaways like letters starting without your name or the ‘Dear Customer’ from the so-called tax department officials, poor sentence construction, grammatical errors, or threats of penal action. Periodically check your account for activity and never click on the links!

Do not use your phone for transfers:

If ever you receive phone calls from the IRS claiming you are a defaulter or have to pay a court-fine and need you to pay through phone transfers then call the IRS directly at 800-829-1040 and speak to a legitimate IRS staff member. Go ahead and block such calls as the banks, IRS or any other financial institutions have long since stopped using phone calls to call you. Even when you panic, remember smart frauds can phish over the phone with misusing the Machine Learning Course and reflecting the 911 number on your display.

VPN authentication:

Always use a VPN. The service uses remote server encryption and can go a long way when you use WiFi networks both untrusted and public networks to send your tax returns to the IRS. Hackers will need to work very hard to get past the encryption and this ensures the security of your personal data.

Stay clear of great offers:

In the USA 2 in 3 taxpayers find tax-filing complicated and use a tax returns professional to file their returns. Especially so, when in a hurry! The major issue here is to distinguish between the ethical and legal professional and the scam-master posing as one just to steal and hijack your personal data. That’s why it’s always better to plan in advance and know your tax- professional’s credentials before you employ them.
The American saying that there is no free lunch anywhere is true here too. Beware of tax professionals offering to get you huge refunds especially when you know that your financial position is not so good. Does that offer sound like a bonus from heaven? Then stop! It is a scam. If your tax professional is refusing to issue a receipt for payments, then beware. No references, only cash payments or lacking testimonials are also good cause for concern.
To watch your step, ask the tax professional for his Preparer Tax Identification Number or PTIN which needs to be mandatorily mentioned with their signature when they file returns on behalf of you. Stay abreast of the latest scams and fraud methods where the machine learning course behind your personal data could be exploited for unlawful gain.
Conclusion:
Tax season or otherwise the above-mentioned measures are effective and not time-consuming to enforce all year round while preventing data loss and personal identity thefts. To learn more about cybersecurity and the machine learning course behind the tax scams do a course at the reputed Imarticus Learning Academy. All the best with your tax returns!
For more details regarding this 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, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

What Are The Machine Learning Use Case in IT Operations?

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Reading Time: 3 minutes

 

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 Machine Learning Interview Questions?

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It is not surprising that machines are an integral part of our eco-system driven by technology. Reaching a point in technical pinnacle was made easier from the time machine started learning and reasoning even without the intervention of a human being. The world is changing from the models developed by machine learning, artificial intelligence and deep learning which adapt themselves independently to a given scenario. Data being the lifeline of businesses obtaining machine learning training helps in better decision-making for the company to stay ahead of the competition.

Machine learning interview questions may pop up from any part of the subject like it may be about algorithms and the theory that works behind it, your programming skills and the ability to work over those algorithms and theory or about your general insights about machine learning and its applicability.

Here is a collection of a comprehensive set of interview questions about machine learning and guidelines for the answers:

1. What are the different types of machine learning?

Machines learn in the following ways:

Supervised learning: A supervised learning essentially needs a labeled data which are pre-defined data set using which machines provide a result when new data is introduced.

Unsupervised learning: Here machines learn through observation and defines structures through data as these models do not require labeled data.

Reinforcement learning: Here there is an agent and reward which can meet by trial and error method. Machine tries to figure out ways to maximize rewards by taking favorable action.

2. How does machine learning differ from deep learning?

Machine learning essentially uses algorithms to parse data, learn from them and makes informed decisions based on the learnings. Whereas, deep learning structures different algorithms and gimmicks an artificial neural system to make intelligent decisions by learning on its own.

3. Having too many False positives or False negatives which one is better? Explain

It completely depends on the question and domain for which we are figuring out a solution. For a medical domain showing false negatives may prove risky as it may show up no health problems when the patients are actually sick. If spam detection is the domain then false positives may categorize an important email as spam.

4. What is your idea about Google training data for self-driving cars?

Google uses Recaptcha to sense labeled data from storefronts and traffic signals from its eight sensors interpreted by Google’s software. Creator of Google’s self-driving car Sebastian Thrun’s insights is used to build a training data.

5. Your thoughts on data visualization tools and which data visualization libraries do you use?

You may explain your insights data visualization and your preferred tools. Some of the popular tools include R’sggplot, Python’s seaborn, Matplotlib, Plot.ly, and tableau.

6. Explain about a hash table?

In computing, a hash table is a data structure which that implements an associative array. It uses a hash function using which a key is mapped to certain values.

7. Explain the confusion matrix?

Confusion matrix or error matrix essentially visualizes the performance of algorithms in machine learning. In the below table TN= True negative, FN=False negative, TP=True Positive and FP=False positive.

8. Write pseudo-code for a parallel implementation by choosing an algorithm

Enlighten your knowledge about pseudo-code frameworks such as Peril-L and some visualization tools like Web sequence diagram to aid you in showcasing your talent to write a code that reflects parallelism well.

9. How do you handle missing or corrupted data in a dataset efficiently?

You could identify missing or corrupted data in a dataset and ideally drop them or replace them with another value. In pandas isnull() and dropna() are two useful methods which can be used to identify columns of missing or corrupted data and drop them or replace an invalid value with a placeholder value like fillna().

10. Difference between a linked list and an array?

An array consists of an ordered collection of objects wherein it assumes that every object has the same size. A linked list, on the other hand, is a series of objects with directions as to sequentially process them which helps a linked list to grow organically than an array.

Conclusion

For becoming a successful machine learning engineer, you could join Machine learning certification training to make yourself proficient in various topics of machine learning and its algorithms. From this curated list of interview questions, you would have understood that machine learning is an internal part of data science. Use these sample questions to broaden your knowledge about the questions that may pop up in your interview and be ready to spellbind the interviewer with your swift answers.

For more details, in brief, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or 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, Delhi, Gurgaon, and Ahmedabad.

How important is the R programming language nowadays?

Posted on by Imarticus Learning
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R is a popular programming language used for statistical computing and graphics by developers. This open sourced tool is not only just a programming language but also an excellent IDE. One important field of its applications is data analysis. Statisticians and data miners largely prefer R to develop their statistical software. However, R is not as popular as programming languages such as Java or Python. This article discusses the importance of R in the current era where data is everything.
How important is R?
We know that programming software like python offers an easy to understand syntax and higher versatility. Yet, R is preferred among data analysts. The reason for is that R was designed for statisticians. Hence R comes with field-specific advantages such as great data visualization features. A large number of major organizations are found using R in their operations. Google not only uses R but developed the standards for the language which got wide acceptance.
Revolution Analytics, kind of a commercial version of R was purchased by Microsoft and they provided servers and services on top of it. So, in general, despite the steep learning curve and uneasy syntax, R has its own advantages and the industry has recognized it very well.
In the opinion of experts, R is expected to remain as an indispensable resource for the data scientists for a very long time. The wide range of pre-defined packages and libraries for the statistical analyses will keep R in the top. The introduction of platforms such as Shiny has already resulted in increased popularity of R, even among the non-specialists.
So, Should You Continue Taking that Course Teaches Machine Learning via R?
It is known that every professional with a machine learning certification has huge career opportunities waiting ahead. But it is important to possess the exact skills the employers are looking for. So, is R such a skill wanted by employers? Well, it is observed that organizations are moving towards Python at a slow pace. In academic settings and data analysis R is still most popular, but when it comes to professional use, Python is leading. Python has achieved this by providing substantial packages similar to R. Even though most machine learning tasks are doable by both languages, Python performs better when it comes to repetitive tasks and data manipulation. A better possibility of integration is another advantage of Python. Also, your project may consist of more than just statistics.
It is recommended to start learning Python if you haven’t spent much time with your Machine Learning course that teaches through R. After learning python, you can use RPy2 to access the functionalities offered by R. In effect, you will have the power of two different languages in one. Since most of the companies have production systems ready for this language, Python is always production-ready. Even if you feel like learning R after learning RPy2, it is pretty easy to do. But moving to Python after R is relatively much difficult. If you are already too deep in R, ignore everything and focus on it.