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Tag: machine learning training

What Are The Application of Machine Learning in Medicine?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Medicine and healthcare are leading industries with altruistic goals. Smart applications of the Machine Learning are today playing a role in a variety of areas like billing, insurance claims, record-keeping, patient-care, staffing solutions and many more. Just over the last decade technology has changed much. So have the volumes of data and its complexities, the various smart devices being used, and the algorithms specific to medical care.

ML is the system of algorithms developed for specific tasks that use deep-learning and artificial intelligence to simulate the way in which the human brain works to execute and aid us in complex tasks. Hence Machine Learning Course is an evolving and very important field. The ability of the ML algorithms to accurately predict, self-learn and assist us with forecasts is truly amazing and way beyond what the human brain is capable of. The field of ML is lucrative too!

Let us look at some of the best applications of ML in the medical and allied fields.
1. Diagnosis on a global scale: ML algorithms and applications score in disease diagnosis, providing simple diagnosis and online treatment even in rural areas, identifying patterns in the disease progression that is hard to recognize, and creating a global repository of research to help better the healthcare segment. Some of these are cancer detection, treating underlying genetic conditions hard-to-diagnose like Parkinson’s, diabetes, etc, providing therapeutic aid, and predicting conditions like stress, depression, etc.

2. CAT scans and MRI aided Diagnosis: Deep Learning and ML have been responsible for the development of advanced imaging devices like the Computer-Aided-Tomography scans, Magnetic-Resonance-Imaging, and the non-invasive Doppler or ultrasound scans. These developments embody machine learning training, handling of large data volumes and the ability of AI and can easily be used to forecast accurately the development of tumours and aneurysms.

3. Manufacturing and discovery of new drugs: Research, discovery, and use of the latest drugs are very important today as we discover more diseases and strains that are traditional-drug-resistant. ML allows the learning in an unsupervised fashion and helps develop new drugs with optimum dosages which can even be personalized for cancer patients.

4. Modification behavior-therapy: Prevention being better than cure several startup firms have come out with gesture-control based therapy apps, symptomatic analysis, early cancer warning, and detection apps, sleep trackers and such behavior-sensing apps that can help even with predicting the susceptibility to genetic diseases. For ex: Angelina Jolie underwent mastectomy based on such a forecast of cancer.

5. App-based medical personalization: Currently medical predictions use biosensors, historical data, genetic information and symptoms to diagnose ailments which tend to have rather fixed treatment methods. The time is not far where ML can offer holistic personalized treatment options which can help faster recoveries by identifying the underlying cause, diets, optimum medicine dosages, and recovery paths in a moment. Even the very process of seeking doctor’s advice appears to be going online to reach out further into rural areas and the personalization of healthcare regimens.

6. Outbreak Prediction: ML, deep-learning and AI have succeeded in not only assisting in healthcare but can also accurately and through unsupervised learning predict and forecast epidemic outbreaks which can then be nipped in the bud.

7. Smart Record Keeping and insurance claims:
The better the maintenance of records the easier cashless hospitalization and treatment becomes. ML is taking very large strides with smart apps to enable smart health-records in real-time, better document classification and intelligent paperless claim settlements.

8. R and D and medical trials: ML has and bears high potential in these areas of medical care where innovation and data play a huge role.

9. Data Crowdsourcing: The smart apps powered through intelligent ML allows doctors globally to crowdsource their data resources making it easy to give a real-time diagnosis, treatment, and better healthcare facilities. Even operations and complex deliveries can now be conducted easily and safely.
10. Radiotherapy and medical imaging:
Radiology, laser treatments, pathology, gastroenterology, cosmetology and several disciplines of medicine are being innovatively modernized with smart algorithmic-based ML to make model-making of individual cases much easier to treat successfully.

In conclusion, ML is growing by the moment and now is the right time to get on board this transformation. If you want to learn Machine Learning and reap career benefits in the medical field then you need to have Machine Learning Training.

At Imarticus Learning, the mentored specialization in skill-oriented courses like ML is par excellence and comes with certification, skill-based training, personality development, and assured placements. Hurry!

Comparing Different Programming Languages For Machine Learning!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Machine learning is a fascinating field. However, if you are just starting out in your machine learning career, then deciding where to begin is the toughest part. There are plenty of programming languages available that support machine learning. And each of these has its own uses and values.

Machine Learning CourseIn this article, we will dive deep into the different types of programming languages available along with their details to help you select the right one for you.

Let’s get started.

  • Python
  • Java
  • C++
  • MATLAB
  • R
  • Julia

Python

Python is one of the most famous programming languages and is used worldwide. It is used by over 8.2 million developers across the world. It has a vast library that is easy to access, handle, process, and transform data. Python offers better readability, more independence, and less complexity. Making it the No. 1 choice for machine learning engineers. However, the major shortcoming of Python is that it is relatively slower than C++ and its multithreading support is not good.

Java

Java is popular among those machine learning engineers who have a background in Java development. These engineers do not need to learn a completely new language for their machine learning implementation. Java offers easy dubbing, enhanced user-interaction, ease of use, and graphical representation. It also makes the scaling task easier for engineers. But Java is a slow and memory-consuming platform that requires significant memory space for running.

C++

C++ is a great programming language for building libraries. When it comes to game development, C++ is the most used language. Some C++ packages also support Tensorflow and Microsoft Cognitive Toolkit (CNTK) for deep learning. They come with OpenCV for computer vision. Shogun and MLPack are used for machine learning. DyNet, OpenNMS, and ANN support neural networks. However, C++ is mostly syntax-oriented, which is quite complex for beginners.

MATLAB

MATLAB – Matrix Laboratory is an innovative programming language. It is easy to use and understand language for machine learning aspirants. It supports GUI and is not constrained with complex syntax. It can easily perform code generation functions. It comes with predefined functions and helps optimize the coded models. Though MATLAB is a good language, it has its own shortcomings. MATLAB is a bit costly and is not easily available for free use. One can get a 30 days trial version with MATLAB, but for further use, it has to be bought.

R

R is a good alternative programming language for Python. With a userbase of over 2 million, R is used for various machine learning applications. It has significant benefits for statistical purposes. It is an open-source programming language with a good library. It supports matrix implementation, vectors, and other data structures. From data visualization to model evaluation, data sampling, data analysis, and more, R offers diverse packages. However, R lacks basic security and has lesser speed.

Julia

Julia is a dynamic programming language that can be used for a variety of general-purpose tasks. It works exceptionally well for computational and numerical analysis. It supports nearly all types of hardware and is used by major corporations like NASA, Apple, Oracle, etc. The codes of Julia are universally functional. They employ larger scalability. But it takes too much time to plot the data. Its packages are also not much mature.

Every programming language has its own pros and cons. Depending on your use, you should choose the one that best serves your purpose. A machine learning course can provide you with a deeper understanding of these languages along with their efficient use.

How The Machine Learning Will Improve Education In The Future?

Posted on by Imarticus Learning
Reading Time: 3 minutes

 

Education has definitely moved away from the teacher facing a classroom of students all using the same textbook. Today the learning experience is internet and ML dependent for data, technology, and digital resources, No wonder the education system is deeply invested in machine learning.

Let us explore how a machine learning course of 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 with ML algorithms that allow each student to follow his own pace and learning curve.

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 machine learning course and AI style of learning deal with the most effective style to help the student learn.

Adaptive 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, ML and AI can together test how well the students adapt their learning to applications and thus promote the progress of students based on individual interests.

What is machine learning? 

The definition of ML- machine learning is that it gives the AI the ability to self-learn from data, mimicking the human brain and is based on statistical techniques. The algorithm used need not be supervised or explicitly programmed. Almost all ML applications in education work very closely with concepts that are interconnected with artificial learning, deep learning of data, neural networks based on complex self-learning algorithms and the very basic concepts of a horde of machine learning course based applications helping machines do repetitive and intuitive tasks most times more accurately and better than humans themselves.

The benefits of machine learning in education:

Here are some ways in which ML makes a difference in the educational experience of educators and students.

Aid the educators: Data mining is the basis of ML and how well it performs. Forming a single repository of the students in one database, ML can effectively study each student’s behavior versus his peers. Thus ML can help cluster similar students and pace them better throughout the learning experience with the right resources and learning materials.

Gives insight to a student’s performance: One of the huge pluses of ML is the ability to give insights and make predictions based on data of a student’s performance. The ML technology can identify gaps and weaknesses to help students stay ahead of the curve.

Capacity to test students: ML can offer both offline and online tests and guidance that helps students to revise, relearn and evaluate performances. Both educators and students can benefit from their foresight and insights. The AI and ML-based tests and multiple choice answers also test the practical application of knowledge and not just rote learning.

Fair gradation of students: ML removes any bias in grading and scoring. The objective style tests and assignment answers can now be automatically assessed with tools like Grammarly or Turn It In. Both online and offline resources, MOOCs and such can be integrated into the learning process.

Experiential and customized learning: Personalizing the experience and offering near-instantaneous feedback is a huge advantage of ML. Both students and teachers can now benefit from knowing how to fill the knowledge gaps.

Content and feedback are instantaneous: ML is excellent at organizing content, task lists, learning resources, colleges, schools information and much more, to personalize the studying for each student. This helps students grade themselves and progress up the ladder with the suggested courses.

Through identifying weaknesses, machine learning can organize content more effectively. For example, as students learn one skill, they move on to the next skill continually building upon knowledge.

Drop-out rate reduction and retention: Corrective action can be applied rapidly if knowledge gaps persist and are identified by ML. This prevents higher drop-out rates while improving retention levels.

Availability based tutoring: This means ML will facilitate the student’s needs with an available expert tutor for effective learning and tutoring.

Conclusions:

Yes, technology and ML especially will transform the educational experience with more and more algorithms being developed by the minute. If you want to learn all about how to make a career in this field then do a machine learning course at the reputed Imarticus Learning Institute. Now is the right time to jump onto the bandwagon. Why wait?

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

Posted on by Imarticus Learning
Reading Time: 3 minutes

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.

What Is The Role of Machine Learning In Financial Fraud Prevention?

Posted on by Imarticus Learning
Reading Time: 3 minutes

What is the role of machine learning in financial fraud prevention?

The instances of fraud rose between 2015 and 2018, there was also an increase in the total value and volume of fraud, according to KPMG. Needless to say, financial fraud is alive and well, despite increasingly stringent measures to reduce numbers and tighten security.

Out of all fraud instances, banks reported that cyber-attacks were the most imminent risks, no doubt due to the burgeoning use of technology within the sector without adequate security. The solution is a tad ironic- to fight technology with more technology, like fighting fire with fire. Financial fraud prevention is best countered by machine learning, a subset of Artificial Intelligence.

Machine learning may be an alien concept to many, but it plays an intrinsic role in our daily functioning– even more so as times become for futuristic. Detection of email spam, product recommendations on your favorite video network or even image recognition algorithms on cellphones are examples of machine learning in everyday use.

For the banking industry, machine learning comes off much like a savior, especially in light of the many disadvantages of traditional fraud detection.

Traditional methods were heavily based on rules, which meant they could be inefficient, erroneous or hard to scale. This formed a weak buttress against sophisticated hackers who are well-versed in enhanced fraudulent methods and was as good as having no firewall after a point.

Against all of this, machine learning has an important role to play, as any Machine Learning course would tell you. Here are the benefits of using machine learning for financial fraud prevention:

Scalable, cost-effective algorithms

The benefit of machine learning algorithms is that more data means more precision. This is a definite step-up from traditional methods where bigger datasets could lead to incorrect data or even crashes. In machine learning, the algorithm learns with more data as it picks out outliers, nuances and different patterns to provide more accurate results in a more efficient manner.

It’s cost-effective, too– rule-based methods required a lot more in terms of costs to scale, whereas machine learning setups need a few tweaks to get back up and running after an update in datasets. An additional benefit is that machine learning algorithms can repeat menial, frustrating tasks 24/7 without a hitch– a feat highly impossible if a human employee were involved.

Real-time processing

In traditional methods of fraud detection and prevention, the focus was more on long-term processing, which often led to delays in resolving fraud reports and transactions. That naturally led to a lot of unhappy customers, but Machine Learning can successfully put an end to that saga.

The results from machine learning algorithms are real-time, which means instant updates and immediate results. Not only does this lead to quicker fraud resolutions, but it also helps banks identify loopholes in their system and fix them immediately.

Reduce time and increase accuracy

The main draws of machine learning are its ability to reduce the time and effort taken to detect fraud and prevent it. There are claims that a fully-ready machine learning algorithm can detect up to a whopping 95% of fraud, that too accurately. According to another Capgemini report, fraud detection software that uses machine learning can reduce the time taken to investigate by 75%, all the while improving the accuracy of fraud detection by 90%. Needless to say, machine learning purports a lot of benefits when used in the banking sector for fraud detection.

Applicable across industries

The banking sector liaises with multiple other industries, as do their customers. However, the fraud cases they deal with are often dissimilar in intensity, method, uses, and reasoning. It only makes sense, then, that a fraud detection setup is capable of handling fraud detection and resolution across all industries.

With machine learning, algorithms can be scaled or adapted for different industries including e-commerce, medicine, hospitality, retail, and insurance. By using unique datasets for each sector, similar machine learning algorithms can be used to cater to each, instead of limiting all claims to one or a few traditional fraud prevention methods.

The financial and banking sector has a lot to benefit from the use of machine learning in fraud detection– today’s machine learning courses today cover these uses in-depth, making aspirants ready for changing technology in a traditional industry.

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

Posted on by Imarticus Learning
Reading Time: 4 minutes

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.
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100% Scholarship		 2nd Prize
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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.

What is Differential Deep Learning?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Introduction

The world today has changed drastically. Therefore, it is important to change according to the environment in which we function. One such major change has been brought about by the advent caused by Artificial Intelligence.

Deep learning is a concept which has come around from artificial intelligence. It has been adopted by a lot of companies who want to automate their processes and make things easy. Also, people have started transitioning to deep learning to cut their losses and maximise profits. Therefore, deep learning is creating a lot of jobs in the market.

So, you can take up a Deep Learning course to increase your employability.

Understanding Differential Deep Learning

Deep learning is a way of implementing mathematical methods in the day to day working of a company. Numbers change every day. In most cases, we have to do these changes manually. With deep learning, you can keep a tap on the numbers without any additional effort.

With Deep Learning, you can formulate different algorithms to make your job easy. Deep Learning provides you with the ease of making predictions about future rates and prices based on the past and current trends and also on the market situations at different points of time. It provides ease of calculating and predicting values even if the market is volatile. Deep learning increases the analytic speed and efficiency of any company.

With proper Machine Learning training, you can design different algorithms for a different set of problems with which the company deals. Application of Deep Learning brings in automation which drastically improves the performance of any system is it calculating the sales value, keeping a tap on the generated leads, making a financial analysis, etc.

Differential Learning principals can be applied to different machine learning algorithms. Also, once you are well versed with the working of differential learning, you can try your hands on any machine learning language. It is going to be easy for you to understand.

You can apply Differential learning in models like regression, Principal component analysis, etc to get the best results. Also, differential deep learning is effectively used in the field of Finance. This can be used to develop a mechanism on pricing.

You can also use this to manage and assess various risks. Differential deep learning is effectively used to produce different models for closed-form solutions. Various Risk management metrics like hedge strategies, etc can be easily generated with the help of deep learning.

Uses of Differential Deep Learning

  1. It is used for developing different machine learning models where these algorithms are built to give results on different sets of inputs.
  2. Differential deep learning is being widely used in the field of risk management. With the use of different models, you can assess the situation at different values,
  3. It makes use of Automatic Adjoint Differentiation which can calculate changes and derivative sensitives quite easily.
  4. With Differential deep learning various fast pricing analytics can be formed which can be used to compute the metrics of risk management at different sets of information
  5. It is used to extract important estimations and values even when small data sets are available. Using differential deep learning can bring you to a conclusion very easily.
  6. It replaces the frequent interference of humans which was a prerequisite earlier. Differential Deep learning creates a system which is automated and reduces the cost of labour to a great extent. This system reduces the chances of errors drastically.

Conclusion

Differential Deep learning has changed the way we use our data. It has made the application of Machine learning possible. Also, with the help of this, companies are moving towards a fully automated world.

How the Indian Government’s AI Website Will Help the Country’s Artificial Intelligence Industry?

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Collaboratively developed by the National Association of Software and Service Companies (Nasscom) and the National e-Governance Division of the Ministry of Electronics and Information Technology (MeitY), the portal is intended to be a one-stop-shop for all AI-related developments to happen in India.

The portal will also distribute documents, case studies, research reports and more relating to AI, and will also become a repository for AI-related learning job roles.

Noteworthy Resources On the Website

  • AI Courses

In order to help professionals and fresh graduates upskill, a variety of AI-related and machine learning courses are being offered through this government portal. The course providers range from IIT Bombay to companies such as IBM.

  • Editorial content

In order to collect all related information in one place, the website publishes editorial content and news as well as case studies and resources for budding AI entrepreneurs.

  • Highlighting successes

The portal also aims to highlight the country’s successes in AI in the recent past, spanning industries and use cases.

How the Website Will Help India’s AI Industry

The website is a distinctive step forward in recognizing the potential and scope of AI in India. Here are some ways in which the website can benefit the AI landscape at national and grassroots levels:

  • Democratising AI

Sanjay Dhotre, the MoS for E&IT, Communications, and HRD, said that digital innovation, including AI, has been a massive equaliser among the Indian population despite the diversity of the country. AI has already penetrated several homes; in the future, this website hopes to bring AI to virtually every part of the country and expound on its benefits in a growing economy.

  • Cementing India’s Place in the World

This public declaration of support towards Artificial Intelligence developments is a solid way of confirming to the world that India is investing in the technologies of the future. India has already supplied global leaders in technology to different parts of the world. By funding this AI website and encouraging its use, the country is making its way up the list of countries with one eye on the foreseeable future.

  • Encouraging Innovation

By making case studies, courses and resources accessible to the masses, the AI website is, in effect, encouraging each and every citizen to experiment in the field. There is no dearth of innovation and ideation in the country; this website becomes a place for resources to support the realization of AI-related dreams and the furthering of the country’s technological goals.

  • Building Networks

The website is touted as a convergence point for students, fresh graduates, AI experts, experimental businesses and new-age entrepreneurs to share and transact over revolutionary AI-driven ideas and proposals. The website also becomes a launching pad for innovation and a networking platform for those with ideas and those with the power to fund them. The departments are also working on creating investor pitch events to link ideators and funders alike.

  • Preparing the Future Generation

Alongside the website, the departments also announced the Responsible AI for Youth Program which is targeted at imparting AI education in government schools. The different phases of the programme aim to train teachers and gather groundbreaking ideas from the students themselves. Keeping in mind that India is one of the most populous countries in the world, instilling an understanding of AI and a passion for innovation at a young age is critical to the growth of the country internationally.

Conclusion

The AI website can be looked at as a show of support towards futuristic technologies that emerge from the grassroots level. By attempting to democratize AI, this government website is also creating a conducive atmosphere for technological growth on par with the rest of the world.

RPA Lifecycle – Everything About Different Stages of Bot Development!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Robotic Process Automation (RPA) in Machine Learning is a hot topic right now. Aspiring learners are looking to amass knowledge in it while IT professionals are looking to gain hands-on experience. In this quest, you are bound to come across the RFA lifecycle which is how bots are built from scratch.

Let’s take a look at the critical stages of bot development.

RPA Lifecycle

robotics programming automation course

Before we get into the stages of bot development, let’s first understand what Robotic Process Automation in Machine Learning really is. In laymen terms, RPA allows a process to be automated using software, robots, or any type of bots so that human intervention can be minimized.

Ever contacted the customer support of Amazon.in and a chatbot suddenly appeared on your screen? That is RPA in motion. It involves mimicking a human activity so that basic actions (like responding to a customer’s query about a recent purchase) can be automated. This helps reduce costs and human resources.

Let’s move on to the different stages of bot development.

Discovery

In this phase, the brief provided by the client is analyzed.

Can the process be automated? Can a bot for the said process be built? What are the challenges and advantages of building it?

Answers to these questions will give the entire RPA development team a clear understanding of the project. Thorough coordination with the client is essential in this phase.

Solution Design

In this phase of RPA in Machine Learning, a Process Definition Document (PPD) is created which provides detailed information about the process. A flowchart is then created that will act as a blueprint to the bot creation. What actions will trigger the bot? How many elements should it include? Such questions are asked.

data analytics course

This is perhaps the most important phase of Robotic Process Automation in Machine Learning as the information gathered here will heavily influence the final product: the bot.

Development

Once all processes are established, the next step is development. This is the longest phase in RPA in Machine Learning and can extend depending upon the scope and volume of the project.

Different RPA tools are used to develop bot. High levels of coding are executed in addition to automated tools and templates. This is where skilled and experienced individuals in the ML field is required.

The key element of the Development phase is to test how extensive the build of the bot is. Have all scenarios and outcomes been tested and there is no gap in the development? Will there be a situation when the bot goes live and it fails to act? Proper coordination with the research done in the initial phases is extremely important. This will ensure the bot is a success.

Testing

As the name suggests, this stage involves all types of testing. From the user interface to the backend, all elements are rigorously tested to ensure there are no loopholes.

In a way, Robotic Process Automation in Machine Learning is a complex procedure. Testing, therefore, is the second-most important phase because the findings in this stage can make or break the project.

Deployment

Last on the RPA lifecycle is the deployment stage. Here the bot goes live on the frontend and is ready for user interaction. Maintenance and bot execution are generally part of this stage.

That is the RPA lifecycle in a nutshell for you. Of course, when you go deep into each of the stages discussed above, you will understand the complexity of RPA in Machine Learning. It’s an interesting concept but one where the lifecycle has to be religiously followed to avoid mistakes.

Top 3 Apache Spark Tutorials For Machine Learning Beginners!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Apache Spark is a well-known name in the machine learning and developer worlds. For those who are unfamiliar, it is a data processing platform with the capacity to process massive datasets. It can do so on one computer or across a network of systems and computing tools. Apache Spark also offers an intuitive API that reduces the amount of repetitive computing and processing work that developers would otherwise have to do manually.

Today, Apache Spark is one of the key data processing and computing software in the market. It’s user-friendly and it can also be used through whatever programming language you’re most comfortable with including Python, Java and R. Spark is open-source and truly intuitive in that is can be deployed for SQL, data streaming, machine learning and processing graphs. Displaying core knowledge of Apache Spark will earn you brownie points at any job interview.

To gain a headstart even before you begin full-fledged work in Apache Spark, here are some tutorials for beginners to sign up for.

  1. Taming Big Data with Apache Spark and Python (Udemy)

This best-selling course on Udemy has fast become a go-to for those looking to dive into Apache Spark. More than 47,000 students have enrolled to learn how to:

  • Understand Spark Streaming
  • Use RDD (Resilient Distributed Datasets) to process massive datasets across computers
  • Apply Spark SQL on structured data
  • Understand the GraphX library

Big data science and analysis is a hot skill these days and will continue to be in the coming future. The course gives you access to 15 practical examples of how Apache Spark was used by industry titans to solve organisation-level problems. It uses the Python programming language. However, those who wish to learn with Scala instead can choose a similar course from the same provider.

  1. Machine Learning with Apache Spark (Learn Apache Spark)

This multi-module course is tailored towards those with budget constraints or those who are unwilling to invest too much time, preferring instead to experiment. The modules are bite-sized and priced individually to benefit those just dipping their toes. The platform’s module on “Intro to Apache Spark” is currently free for those who want to get started. Students can then progress to any other module which catches their fancy or do it all in the order prescribed. Some topics you can expect to explore are:

  • Feature sets
  • Classification
  • Caching
  • Dataframes
  • Cluster architecture
  • Computing frameworks
  1. Spark Fundamentals (cognitiveclass.ai)

This Apache Spark tutorial is led by data scientists from IBM, is four hours long and is free to register for. The advantage of this course is that it has a distinctly IBM-oriented perspective which is great for those wishing to build a career in that company. You will also be exposed to IBM’s own services, including Watson Studio, such that you’re able to use both Spark and IBM’s platform with confidence. The self-paced course can be taken at any time and can also be audited multiple times. Some prerequisites to be able to take this course are an understanding of Big Data and Apache Hadoop as well as core knowledge of Linux operating systems.

The five modules that constitute the course cover, among other topics, the following:

  • The fundamentals of Apache Spark
  • Developing application architecture
  • RDD
  • Watson Studio
  • Initializing Spark through various programming languages
  • Using Spark libraries
  • Monitoring Spark with metrics

Conclusion

Apache Spark is leveraged by multi-national million-dollar corporations as well as small businesses and fresh startups. This is a testament to how user-friendly and flexible the framework is.

If you wish to enrol in a Machine Learning Course instead of short and snappy tutorials, many of them also offer an introduction to Apache Spark. Either way, adding Apache Spark to your resume is a definite step up!