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Certification in Artificial Intelligence and Machine Learning in Collaboration with E & ICT Academy, IIT Guhawati, and Imarticus Learning

Posted on by Imarticus Learning

Machine learning is powered by AI. With ML, we can power programs that are easily updated and modified to adapt to new environments and tasks- resulting in quick progress on difficult projects. ML and AI are almost synonymously used in tandem when it comes to the latest tech trends. And, AI has disrupted many industries forever – like SaaS, Manufacturing, Defense, Analytics, public sector, and so on.

If you learn machine learning through the best Machine Learning & AI course, you are likely on a fast and high-growth career trajectory.

Here is why!

#1. ML & AI Are Skills of the Future

Skills in ML can affect your long-term employment prospects because the field is projected to experience rapid growth.

With the emergence of advanced technologies like the Internet of Things (IoT), Machine Learning is experiencing a certain surge in demand and popularity.

If you learn machine learning by taking up data science certifications, there is an increase in the probability that you will have better job prospects as compared to someone without ML skills.

#2. Solve Real-World Problems

There is a lot of talk about how AI will replace jobs, but the truth is it will create new job opportunities. As an ML engineer, for instance, you get to work on projects that have a big impact in the real world and lead to business solutions that are meaningful.

#3. Versatile Growth Opportunities in the Data Sciences

Machine learning and artificial intelligence (ML &AI) skills are beneficial to a data science career. Both positions give you the opportunity to let your knowledge of both fields expand. Switching back and forth between roles can quickly enable you to become an invaluable resource in today’s rapidly changing world.

You want to have an advantage as early as possible so that you can learn about solving new and undiscovered problems. When the time comes, these skills will be in great demand and allow you to secure a career path on the rise.

What does a growth path in this domain look like?

Typically, a machine learning engineering career path begins with being a Machine Learning engineer. Machine learning engineers develop applications that automate common tasks previously done by humans and take care of the repetitive ones for humans to do efficiently without error.

When you earn a promotion as an ML engineer, you are then promoted to be an ML architect! Their responsibility is developing and designing prototypes for applications that need development.

There are a few other roles in the field which one could take on as well. These include ML data scientists, ML software engineers, senior software architects, and more.

New tech arenas keep developing into this space. So, keep your interest, skills, and industry demands aligned for the best growth!

How to make a successful career in AI & ML?

Want to learn machine learning with the best Machine Learning & AI course?

certification in Artificial Intelligence and Machine Learning

Imarticus brings to you the class-leading AI & ML certification in collaboration with E&ICT Academy of IIT-Guwahati.

Our highly-rated program has fostered hundreds of successful professionals serving the industry worldwide. Your chance to be a part of this prestigious career trek begins with us.

Enroll with Imarticus now!

How The Machine Learning Works Behind Your Favorite Google Meet Backgrounds?

Posted on by Imarticus Learning

Google Meet has been a lifesaver for many professionals and students who are unable to step out of their homes for the last few months. This increasing usage of such virtual meeting platforms has improved the technology reach. This and Google AI has now increased the need for Machine Learning training and opened up a whole new world in technology.

Google’s AI of Google Meet now allows the user to change the background and reduce the noise level as well. Instead of the boring or the interiors of the home as the background, Machine Learning has helped customize the backgrounds for such meetings.

The technology behind the backgrounds

Google uses MediaPipe Objectron to get the 3D dimensions of images on mobile devices. It is also useful for background changes as well. They came up with an in-browser version of the Machine Learning model that can blur or replace the backgrounds. With these combined efforts of the ML, MediaPipe, and the OpenGL technology, its performance is better even in the devices with low power available.

Google uses WebGL for rendering, ML such as TFLite, and ZNNPack for web-based interference.

How does it work?

The MediaPipe uses the new low-level format of the binary code of WedAssembly. This can speed up the processing faster than JavaScript and can improve the speed of the tasks as well. The instructions from the WedAssembly are converted into simpler code by the browser.

  • First of all the ML segregates the user and its background.
  • Now, the user is masked by the ML interference into a low-resolution component.
  • The mask undergoes processing to refine its edges to be a smooth blend with the new background.
  • A WebGL2 is used to get the final output for the video where the mask is suitable with the replaced or the blurred background.

The technology here uses a lighter interference that uses less power and smaller storage space.

Refining the results

Although the masking effect is refined so it makes it easier to blend with the background, it could still end up having a halo effect. The light wrapping disables this possibility. The composting technique refines the edges of the mask and also allows the background light to adjust itself to blend the user with itself. The technique allows the light from the background to spill all over the edges of the mask to conceal the halo effect. This results in the fine blending of the background with the foreground image.

Performance in various devices

In the high-end devices, the image transition through the ML system continues at a higher resolution but in the low-end devices, there is a slight change. In the latter, the working mechanism automatically switches through the lighter models of ML so as to maintain the performance speed. Here, it skips the image refining process to send the final output.

The flexible configuration of the MediaPipe enables it to choose the most effective processing method.

Google AI and ML

The regular updates on Google AI and algorithms have opened new scope in the field of Machine Learning and its various prospects. While the Machine Learning Course provides basic knowledge, there is more to it when it is learned properly.

artificial intelligence coursesSince the internet-based virtual meetings are not going to disappear anytime soon, more changes in the working are expected. With each change, there is more to learn which naturally increases the importance of Machine learning and AI.

Bottom Line

Seeing all these, it would be not a bad idea to enroll in a machine learning course to start with the basics. Though this is a field with no limits, there is sure a lot to learn.

Bringing AI and Machine Learning Accessible to Enterprises Credit to Cloud!

Posted on by Imarticus Learning

Artificial Intelligence (AI) technology has been a game-changer for businesses. It has revolutionized how businesses operate and get the work done. Artificial intelligence technology imparts machines with the ability to understand and apply intelligence while processing complex data that would’ve earlier required human aid. Machine learning is a part of Artificial Intelligence technology and entails training machines to process information using large data sets.

Let’s discuss a real-life scenario to understand the functioning of machine learning technology better. Have you ever wondered why the prices keep on fluctuating when you book a Cab using Uber? Well, that’s machine learning technology into action for you.

Dynamic pricing is how the machine learning algorithms leverage buyer’s curiosity, demand, traffic congestions, etc. to regulate the cost and price the fare accordingly. Machine learning is increasingly being deployed by organizations to help with complex real-time data processing.

AI & ML Accessibility  

Accessibility has always been a challenge when it comes to adopting AI & ML technology for businesses; cloud solutions have helped paved the way for even smaller businesses to adopt AI & ML technology. Here is a list of few cloud services that is changing the way businesses adapt to AI & ML solutions.

  1. Amazon Web Services (AWS)

Amazon needs no introduction; it has always been about boosting customer satisfaction and improving business practices. AWS is a cloud solution offering from Amazon that provides a diverse range of machine learning solutions including Amazon SageMaker that simplifies the process of creating, training, and deploying machine learning models to work. Other machine learning-related solutions by AWS includes dynamic pricing models, search recommendations, automated customer service, etc.

  1. Google Cloud

Google’s cloud solution is second in this list of cloud services that have made machine learning more accessible for companies. After the development of an open-source platform named TensorFlow, Google has achieved new heights in the AI & ML arena. In addition to its indigenous open-source application, it is also associated with DeepMind, one of the most prominent players in the machine learning space. AlphaGo is a flagship program by DeepMind that has revolutionized the machine learning and AI space.

  1. Azure by Microsoft

Azure by Microsoft is another prominent name in the list of cloud platforms that have made machine learning more accessible for organizations of all sizes. Azure boasts of in-built machine learning services for organizations that want to leverage machine learning models into their business operations. To make it more easily and user-friendly it has both code-based and drag and drop functions. Azure aims to revolutionize the machine learning space by focusing on building a bias-free responsible machine learning solution.

Conclusion

Machine learning is an indispensable tool for businesses in the contemporary that rely on the use of sophisticated technology to operate and reach new customers. Machine learning career is in huge demand as more and more businesses are leveraging this remarkable technology to grow their business and optimize their operations.

One can opt for a machine learning course from reputed institutions like Imarticus Learning to obtain comprehensive knowledge about this technology and obtain a job with some of the most reputed organizations.

What is The Best Coding Bootcamp For Machine Learning and Deep Learning?

Posted on by Imarticus Learning

Machine learning is an application of AI that provides the computer systems to automatically learn and process information to identify patterns and behavior which is later used in decision-making for given circumstances using this pattern. Machine learning is therefore looked upon as a revolutionary development in technology.

With diversified use of machine learning, companies are ready to invest in hiring machine learning engineers to stay ahead of the competition in a technology-driven world.

According to a report from Glassdoor, the average salary for machine learning engineers in the US is $115000 which is one of the reasons that make this field an attractive one even for the millennials who grew up with technology.

Aspiring candidates must possess great analytical skills and knowledge in programming languages and techniques like Python, C, C++, Hadoop, Impala, and Java which are provided through a Machine Learning Course.
There are numerous schools and institutes who are equipped with specially designed boot camps and courses both online and at the campus.

So, it becomes really difficult to choose the better one. Therefore, here is a list of compiled best boot camps for Machine Learning Training and deep learning which you can investigate further before making your decision:

  1. NYC Data Science Academy: Data science with Tableau, coding boot camp and Data science with R: Machine learning are some of its valuable courses offered full-time and part-time throughout the year. They also provide quarterly career day that connects the hirers with the students to enhance job opportunities.
  2. Grey Atom School of Data Science: It offers immersive data science boot camp, Data science masters program, data science masters program with deep learning. They offer career guidance and intense modules using Python.
  3. Product school: A list of comprehensive courses including product leadership, UX design for product managers and more cover subjects including Data analytics for managers(SQL & machine learning), blockchain and cryptocurrencies and project management. Students can get attend their courses in different locations or online and can access the job boards of the school online.
  4. Codesmith: Students get trained for landing in mid-level to high-level positions in their career with their intense courses like JavaScript for beginners, full stack software engineering immersive program and coding boot camps. On campus and online learning lets students build project modules and resume guidance.
  5. Lambda school: It renders a 30-week full time or 12 months part-time online boot camp which students can choose according to their convenience. Full-time or part-time Computer science and software engineering and Full-time or part-time machine learning and Artificial intelligence to name a few.
  6. Ubiqum Code Academy: Students can choose between a five-month full-time Bootcamp or one part-time data analytics course. Coding boot camps and full-stack web development with scholarships for women and veterans. To encourage promising aspirants they provide a facility to pay the fees in installments.
  7. Metis: It has different courses and boot camps both online and on the campus. Its data science Bootcamp has a duration of 12-week in person full-time course. Beginner Python for Matha and data science live course provides insights in subjects like web scrapping, Hadoop, Spark, Machine Learning, Git, GitHub and more.
  8. Data science Dojo: They have a specially designed 5-day data science bootcamp which leverages a hands-on-training for the students who spend 10 hours in this immersive course. Microsoft Azure machine learning, predictive analysis, and data engineering are some of the subjects the students can master during this course.
  9. We cloud data: They offer full-time and part-time data science bootcamps in Toronto and Canada. Their bootcamp is interesting and trains the students to be competitive enough to survive and flourish in this competitive field.
  10. Simple: They offer a wide range of online courses on machine learning, deep learning and data science for individuals who can concentrate on their career as well as upgrading their skills with their professional online courses simultaneously. MapReduce for big data problems, Introduction to solving data problems using UNIX and Hadoop are some of their skillfully designed courses.

Conclusion
Either for a person who is looking to land on a dream job or for a person who is looking to make a career shift in data science attending one of these coding bootcamps will definitely elevate the chances of being on a competitive front. Hike in salary and landing on full-time jobs are some of the perks received by the students who took up coding bootcamps.
Most of the bootcamps offer online courses which are flexible and allows you to work in a favorable environment. Luckily, we at Imarticus Learning is conducting an event called “Hackathon”, in which a number of students can participate in the coding competition.
Put your skills to the test, gain invaluable hands-on experience and develop your programming skills as you employ data science and machine learning to predict the price of used cars.
This exciting opportunity is brought to you by Imarticus Learning in collaboration with Analytics India Magazine. Winners stand a chance to win fantastic prizes and exciting learning opportunities.
Here’s What You Can Win: 

1st Prize
100% Scholarship		 2nd Prize
50% Discount		 3rd Prize
25% Discount
One of the Below Courses from Imarticus Learning
Data Science Prodegree
in collaboration with Genpact		 Machine Learning Prodegree in Collaboration with IBM

You Will Also Win
Exclusive tickets to Cypher 2019, India’s largest Analytics and AI summit by Analytics India Magazine. Grab your chance right away by joining one of the best bootcamps to take your career one notch higher.

HOW AI HELPS VIDEOBOT ANSWERS COVID-19 QUERIES WITH MULTILINGUAL VOICE AND TEXT?

Posted on by Imarticus Learning

Artificial intelligence is helping us during the time of one of the biggest crises in the world. It explains why youth today want to focus on having artificial intelligence training for a better career ahead.

Currently, AI helps diagnose health risks, deliver services, discover new drugs, track coronavirus infections around us, and much more. The pandemic is becoming more significant by the day, but AI is coming to the rescue through different forms of its usage.

It is not only helping researchers, scientists, and doctors to secure people’s lives but tech firms and governments to keep everyone aware. These industries are jointly working towards making the world COVID free.

CoRover teaches us to use the artificial intelligence career at its best

Recently, a start-up driven by artificial intelligence, named CoRover, create a conversational platform. It helps businesses offer authentic information to customers instantly and automatically. The system works with the help of an AI-based doctor-video bot named AskDoc.

The bot addresses queries about coronavirus, transmission, and preventive measures. It includes multilingual voice formats and text formats. Thus, it helps Indians with diverse language options like Hindi, Marathi, Tamil, Telugu, and Kannada. It also includes German and French languages.

How does AskDoc work?

AskDoc helps users get automated replies about COVID-19 and safety protocols given by the Ministry of Health and Family Welfare. It also provides information from the World Health Organization (WHO) and the Government of India.

To ask questions, users need to log into the app. They can use voice recognition or send videos to get replies. Once the app receives a query, the chatbot backend passes it through several layers of its framework.

One can access AskDoc from their laptops other than the app. It offers a chat-based portal that replies to basic questions. Even after going through layers of understanding of the data provided, the answers are pretty quick and specific.

The app helps people interact with healthcare experts across the world. They can ask questions about coronavirus and have diverse knowledge about dealing with it.

How is CoRover making an impact with AI?

The team that made CoRover is currently working towards email integration, as it is also a major source of information. It will help several government-based platforms to get quick answers.

The company also introduced Ask Disha, a conversational AI platform with more than 20 billion interactions by more than 200 million people. With the help of machine learning and artificial intelligence, it helps connect administrative staff, travelers, verified service providers, and more. The recently growing company from Bangalore has already applied for two patents for its product.

Chatbots with AI are not new and know the right way of using empathy and emotions to connect to humans. These work as efficient virtual assistants and help medical experts, medical staff, patients, and families in several cases.

The chatbots created for health only focus on aspects of healthcare. Currently, chatbots for health are increasing due to the coronavirus pandemic. With voice recognition and text formats, these can reach out to people as other humans do.

Many businesses are incorporating chatbots to offer information about COVID-19. Moreover, the Centers for Disease Control and Prevention (CDC) and WHO have chatbots on their websites to provide quick information about the virus. Several governments are also incorporating the same to keep their people aware and safe.

What Are Some Tips And Tricks For Training Deep Neural Networks?

Posted on by Imarticus Learning

Deep Neural Networks aid AI applications such as image and voice recognition to function at unprecedented accuracy. A Deep Neural network is basically an array of several layers, where each layer sieves raw data into a structured mathematical model. 

The process of making the data flow through the various layers is called Deep Neural Network Training. In humans, we also start recognizing an object once we have seen it several times. If you saw just one “car” in your entire life, you might not be able to recognize a car again if you saw a different model this time. 

In Data Science, this is easier said than done. Therefore, we have some tips and tricks that you can use when you sit down to teach your DNN to distinguish cars from trucks.

Normalization is Effective

Normalization layers help group logical data points into a higher consolidated structure. An apparent increase in performance has been recorded when using Normalization.

You can use it three ways;

  • Instance Normalization – If you’re training the DNN with small batch sizes. 
  • Batch Normalization – If you have a large batch size, supposedly more than 10, a batch normalization layer helps. 
  • Group Normalization – Independent of batch size, it divides the computation into groups to increase accuracy. 

Zero Centering 

Zero Centering is considered as an important process for preparing your data for training. Just like normalization, it helps in providing accurate results later. 

In order to zero center your data, you should move the mean of the data to 0. You can do this by subtracting the non-zeroed mean of the data from all the data inputs. This way, the origin of the data set on a scalar plane will lie on 0, making it Zero Centered.

Choose the Training Model Wisely 

One thing that you’ll come across when you learn Deep Learning, is that the choice of model can have a significant impact on training.   

Commonly, there are pre-trained models and there are models you train from scratch. Finalizing the right one that corresponds to your needs is crucial. 

Today, most DNN developers are using pre-trained models for their projects as they are resourceful in terms of the time and effort required to train a model. It’s also called Transfer Learning. VGG net and ResNet are common examples.  

The key here is the concurrency of your project with the pre-trained model. In case you can’t get a satisfactory model design, you can train a model from scratch too. 

Deal with Overfitting
Overfitting is one of the most popular problems in DNN training. It occurs when the live run of the training model yields exceptionally good results but the same wasn’t observed during the test runs. 

The problem is basically caused when the DNN starts accepting the attenuations as the perfect fit. This can be dealt with, using the technique of Regularization, which adjusts the problem of overfitting using an objective function. 

Conclusion

Wish you’d know more? Take up a deep neural network training course on Imarticus and start your progress today. DNNs are becoming increasingly popular in data science-related careers. Just like everything else, you can use the first-mover advantage with pro-active learning. 

Machine Learning and Information Security: Impacts and Trends

Posted on by Imarticus Learning

Machine Learning and Information Security: Impacts and Trends

Gone are the days when we needed to patiently sit and teach computers how to perform complex tasks that were backed by human intelligence. Today, the machine teaches itself– far from ‘magic’, it’s a tool that has revolutionized industries across the board today.

For context, machine learning is as significant a change for the world as the introduction of the Internet was. The future of machine learning encompasses more than just tech and related industries. Cybersecurity– more specifically, information security– has been heavily impacted by the introduction of machine learning in a mostly positive manner, but some grey areas exist.

What is Information Security?

InfoSec is the network of processes and systems designed for and deployed to safeguard confidential information, largely business-related, from destruction or modification in any way. InfoSec is not the same as Cybersecurity, albeit it is the part of it that is dedicated exclusively to data protection.

The types of Information Security span cloud security, cryptography, infrastructure protection and detection and management of vulnerabilities. Most Machine Learning training courses brief students about these facets, not least because they’re universal in their use across industries.

Machine Learning in InfoSec: Impacts and Trends

Automate repetitive tasks

Setting up ML algorithms to take care of everyday threats can help ensure a regular check on the underlying security. This also allows security analysts, supported by more complex algorithms, to focus their strength on bigger tactical fights an set up bulletproof systems. This frees up a lot of time on the team’s hands and cuts costs on holding onto employees for repetitive tasks alone.

Endpoint security control in mobile devices

With mobile passwords being the quickest and easiest springboard to accessing information worth selling, cybercriminals are increasingly preferring to target mobile devices. To counter this, machine learning techniques include ‘zero trusts’ no sign-on approaches that eliminate passwords and cloud-based authentication systems.

Predict and preempt strikes on systems

Predictive analytics is fast becoming a core facet of InfoSec systems today because continuous analysis and correlation mean better chances of recognizing patterns and threats ahead of the actual strike. Using AI and machine learning techniques to capture, analyze and classify data in real-time is a benefit that no other system has offered so far, least of all human systems. By identifying potential threats, businesses can prepare in advance by strengthening security, putting extra authentication processes in place and running audits.

Cloud-based security systems

In place of saving millions of customer data on chunky servers prone to breach, businesses are increasingly moving to cloud-based security systems. These systems allow all information to be kept in one place with hefty security barriers in place, with the help of machine learning, to prevent breaches. These systems keep the reins of authority in the hands of a few, making it easier to trace leaks, if any, and allow for timely intervention.

The future role of machine learning

In setting up a top-of-its-class, dynamic security landscape, machine learning plays several roles, both routine and complex. Machine learning training is the talk of the town today because companies want their employees to be more than capable of using machine learning data for the betterment of InfoSec at any organization.

How Criminals Are Using AI and Exploiting It To Further Crime?

Posted on by Imarticus Learning

AI can use the swarm technology of clusters of malware taking down multiple devices and victims. AI applications have been used in robotic devices and drone technology too. Even Google’s reCAPTCHA according to the reports of “I am Robot” can be successfully hacked 98% of the time.

It is everyone’s fear that the AI tutorials, sources, and tools which are freely available in the public domain will be more prevalent in creating hack ware than for any gainful purpose.

Here are the broad areas where hackers operate which are briefly discussed.

1. Affecting the data sources of the AI System:

ML poisoning uses studying the ML process and exploiting the spotted vulnerabilities by poisoning the data pool used for MLS algorithmic learning by. Former Deputy CIO for the White House and Xerox’s CISO Dr. Alissa Johnson talking to SecurityWeek commented that the AI output is only as good as its data source.

Autonomous vehicles and image recognition using CNNs and the working of these require resources to train them through third-parties or on cloud platforms where cyberattacks evade validation testing and are hard to detect. Another technique called “perturbation” uses a misplaced pattern of white pixel noises that can lead the bot to identify objects wrongly.

2. Chatbot Cybercrimes:

Kaspersky reports on Twitter confirm that 65 percent of the people prefer to text rather than use the phone.  The bots used for nearly every app serve as perfect conduits for hackers and cyber attacks. Ex: The 2016 attack on Facebook tricked 10,000 users where a bot presented as a friend get them to install malware. Chatbots used commercially do not support the https protocol or TLA. Assistants from Amazon and Google are in constant listen-mode endangering private conversations. These are just the tip of the iceberg of malpractices on the IoT.

3. Ransomware:

AI-based chatbots can be used through ML tweaking to automate ransomware. They communicate with the targets for paying ransom easily and use the encrypted data to ensure the ransom amount is based on the bills generated.

4. Malware:

The very process of creating malware is simplified from manual to automatic by AI. Now the Cybercriminals can use rootkits, write Trojan codes, use password scrapers, etc with ease.

5. Identity Theft and Fraud:

The generation of synthetic text, images, audio, etc of AI can easily be exploited by the hackers. Ex: “Deepfake” pornographic videos that have surfaced online.

6. Intelligence garnering vulnerabilities:

Revealing new developments in AI causes the hackers to scale up the time and efforts involved in hacking by providing them almost simultaneously to cyber malware that can easily identify targets, vulnerability intelligence, and spear such attacks through phishing.

7. Whaling and Phishing:

ML and AI together can increase the bulk phishing attacks as also the targeted whaling attacks on individuals within a company specifically. McAfee Labs’ 2017 predictions state ML can be used to harness stolen records to create specific phishing emails. ZeroFOX in 2016 established that when compared to the manual process if one uses AI a 30 to 60 percent increase can be got in phishing tweets.

8. Repeated Attacks:

The ‘noise floor’ levels are used by malware to force the targeted ML to recalibrate due to repeated false positives. Then the malware in it attacks the system using the AI of the ML algorithm with the new calibrations.

9. The exploitation of Cyberspace:

Automated AI tools can lie incubating inside the software and weaken the immunity systems keeping the cyberspace environment ready for attacks at will.

10. Distributed Denial-of-Service (DDoS) Attacks

Successful strains of malware like the Mirai malware are copycat versions of successful software using AI that can affect the ARC-based processors used by IoT devices. Ex: The Dyn Systems DNS servers were hacked into on 21st October 2016, and the DDoS attack affected several big websites like Spotify, Reddit, Twitter, Netflix, etc.

CEO and founder of Space X and Tesla Elon Musk commented that AI was susceptible to finding complex optimal solutions like the Mirai DDoS malware. Read with the Deloitte’s warning that DDoS attacks are expected to reach one Tbit/sec and Fortinet predictions that “hivenets” capable of acting and self-learning without the botnet herder’s instructions would peak in 2018 means that AI’s capabilities have an urgent need for being restricted to gainful applications and not for attacks by cyberhackers.

Concluding notes:

AI has the potential to be used by hackers and cybercriminals using evolved AI techniques. The field of Cybersecurity is dynamic and uses the very same AI developments providing the ill-intentioned knowledge on how to hack into it. Is AI defense the best solution then for defense against the AIs growth and popularity?

To learn all about AI, ML and cybersecurity try the courses at Imarticus Learning where they enable you to be career-ready in these fields.

What Are The Machine Learning Interview Questions?

Posted on by Imarticus Learning

 

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 Criminals Are Using AI And Exploiting It To Further Crime?

Posted on by Imarticus Learning

AI can use the swarm technology of clusters of malware taking down multiple devices and victims. AI applications have been used in robotic devices and drone technology too. Even Google’s reCAPTCHA according to the reports of “I am Robot” can be successfully hacked 98% of the time.

It is everyone’s fear that the AI tutorials, sources, and tools which are freely available in the public domain will be more prevalent in creating hack ware than for any gainful purpose.

Here are the broad areas where hackers operate which are briefly discussed.

1. Affecting the data sources of the AI System:

ML poisoning uses studying the ML process and exploiting the spotted vulnerabilities by poisoning the data pool used for MLS algorithmic learning by. Former Deputy CIO for the White House and Xerox’s CISO Dr. Alissa Johnson talking to SecurityWeek commented that the AI output is only as good as its data source.

Autonomous vehicles and image recognition using CNNs and the working of these require resources to train them through third-parties or on cloud platforms where cyberattacks evade validation testing and are hard to detect. Another technique called “perturbation” uses a misplaced pattern of white pixel noises that can lead the bot to identify objects wrongly.

2. Chatbot Cybercrimes:

Kaspersky reports on Twitter confirm that 65 percent of the people prefer to text rather than use the phone.  The bots used for nearly every app serve as perfect conduits for hackers and cyber attacks.

Ex: The 2016 attack on Facebook tricked 10,000 users where a bot presented as a friend to get them to install malware.

Chatbots used commercially do not support the https protocol or TLA. Assistants from Amazon and Google are in constant listen-mode endangering private conversations. These are just the tip of the iceberg of malpractices on the IoT.

3. Ransomware:

AI-based chatbots can be used through ML tweaking to automate ransomware. They communicate with the targets for paying ransom easily and use the encrypted data to ensure the ransom amount is based on the bills generated.

4. Malware:

The very process of creating malware is simplified from manual to automatic by AI. Now the Cybercriminals can use rootkits, write Trojan codes, use password scrapers, etc with ease.

5. Identity Theft and Fraud:

The generation of synthetic text, images, audio, etc of AI can easily be exploited by the hackers. Ex: “Deepfake” pornographic videos that have surfaced online.

6. Intelligence garnering vulnerabilities:

Revealing new developments in AI causes the hackers to scale up the time and efforts involved in hacking by providing them almost simultaneously to cyber malware that can easily identify targets, vulnerability intelligence, and spear such attacks through phishing.

7. Whaling and Phishing:

ML and AI together can increase the bulk phishing attacks as also the targeted whaling attacks on individuals within a company specifically. McAfee Labs’ 2017 predictions state ML can be used to harness stolen records to create specific phishing emails. ZeroFOX in 2016 established that when compared to the manual process if one uses AI a 30 to 60 percent increase can be got in phishing tweets.

8. Repeated Attacks:

The ‘noise floor’ levels are used by malware to force the targeted ML to recalibrate due to repeated false positives. Then the malware in it attacks the system using the AI of the ML algorithm with the new calibrations.

9. The exploitation of Cyberspace:

Automated AI tools can lie incubating inside the software and weaken the immunity systems keeping the cyberspace environment ready for attacks at will.

10. Distributed Denial-of-Service (DDoS) Attacks

Successful strains of malware like the Mirai malware are copycat versions of successful software using AI that can affect the ARC-based processors used by IoT devices. Ex: The Dyn Systems DNS servers were hacked into on 21st October 2016, and the DDoS attack affected several big websites like Spotify, Reddit, Twitter, Netflix, etc.

CEO and founder of Space X and Tesla Elon Musk commented that AI was susceptible to finding complex optimal solutions like the Mirai DDoS malware. Read with the Deloitte’s warning that DDoS attacks are expected to reach one Tbit/sec and Fortinet predictions that “hivenets” capable of acting and self-learning without the botnet herder’s instructions would peak in 2018 means that AI’s capabilities have an urgent need for being restricted to gainful applications and not for attacks by cyberhackers.

Concluding notes:

AI has the potential to be used by hackers and cybercriminals using evolved AI techniques. The field of Cybersecurity is dynamic and uses the very same AI developments providing the ill-intentioned knowledge on how to hack into it. Is AI defense the best solution then for defense against the AIs growth and popularity?

To learn all about AI, ML and cybersecurity try the courses at Imarticus Learning where they enable you to be career-ready in these fields.