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Tag: artificial intelligence course

Want To Learn Artificial Intelligence And Machine Learning? Where Can You Start?

Posted on by Imarticus Learning

These are exciting times to be a part of the technology industry, what with newer and fascinating fields being discovered every day. With the tech companies leading the way, the newer fields like Artificial Intelligence and Machine Learning are finding takers in multiple domains. Probably, this is the reason the demand for machine learning courses is at an all-time high.
Every second person, in the technology domain, you interact with would wax eloquent about how they are going to learn machine learning or how they have been taking the latest and most difficult artificial intelligence courses. Hearing this can be quite an unnerving experience, especially if you are a newbie in the field and looking to find out some machine learning courses that can help you unravel this mysterious world.
But, fear no more, as in this article, we try to help you find your initial foothold in this domain and slowly but surely come up to speed in your quest to learn machine learning. The first thing that confuses newcomers and throws them off-track when they begin to learn machine learning is the different terms and their interrelationships. What do the terms – Artificial Intelligence, Deep Learning, Neural Network Programming, Machine Learning – mean and how are they related.
If you somehow can navigate through the maze of big data and machine learning courses and get a hang of these terms, another big question comes up. Do I need to learn programming, statistics as well as calculus? Is this the right direction for my career, even though I do not understand either of this? Is there a way to learn machine learning without being proficient in all these?

There are no straight forward answers to these questions. But below are a few pointers that may help you to take your first step as well as identifying the correct artificial intelligence courses for yourself.
First and foremost, we need to understand the interrelationship between artificial intelligence and machine learning. Artificial intelligence is mostly trying to mimic human intelligence and behaviour by the machines including creativity, learning and reactions to any situation. As you start to learn machine learning with the help of machine learning courses, you realize that machine learning is nothing but a subset of artificial intelligence, dealing with pattern recognition and self-learning.
Now, the next fundamental question – do you need to learn programming language or statistics to complete artificial intelligence courses? You definitely need to have some basics in both as the statistics help you understand what you are doing, and the programming language shows how it is done. In case either of these is unknown to you, it is recommended you start your quest to learn machine learning with the courses for these.
You can find some excellent courses to learn programming languages like R or Python, models and algorithms basics through – imarticus.org Or also can contact on – info@imarticus.com or 1-800-267-7679 Or else you can visit our different training locations in India – Mumbai, Pune, Thane, New Delhi, Banglore, Chennai, Gurgaon, Hyderabad and Ahmedabad.
Keep Learning…!!

Exploring the Potential of AI in Healthcare!

Posted on by Imarticus Learning

To begin with, let us start with AI and its potential. What exactly is AI?
Over the last two decades, we have built huge data resources, analyzed them, developed ML both unsupervised and supervised, used SQL and Hadoop with unstructured sets of data, and finally with neural and deep learning techniques built near-human AI robots, chatbots and other modern wonders with visualized data.

We have found, optimized pattern-based techniques, exploited tools of ML, deep-learning, etc to create speech, text, image applications used pervasively today in games, self-driven cars, answering machines, MRI image processing and diagnosis of cancer, creating of facial IDs, speech cloning, facial recognition tools, and learning-assimilation products like Siri, Google Assistant, Alexa and more.

AI potential in healthcare:
The AI potential to use intelligent-data both structured and unstructured makes it a very effective diagnostic tool. Their processing speeds, ability to find anomalies and volumes of data AI can handle makes it irreplaceable, un-replicable, and the most effective tool in the healthcare sector’s diagnosis and cost-effective treatment sectors for masses.

EyePACS aided-ably by technology has been used to integrate symptoms, diagnosis, and lines of treatment in diabetic types of retinopathy. Beyond diagnosis AI in the UK helps diagnose heart diseases, cardiovascular blockages, valve defects etc. using e HeartFlow Analysis and CT scans thereby avoiding expensive angiograms.

Cancer detection at the very cellular stages is a reality with InnerEye from Microsoft which uses the patient scans to detect tumors, treating cancer of the prostate, and even find those areas predisposed to cancers. Babylon health and DeepMind are other query-answering apps that have saved multiple doctor-visits by answering queries based on patient-records, symptoms, and other data.

All that AI needs to retransform healthcare services is the appropriate and viable infrastructure that is so vital but expensive today, keeping it out of reach of the masses at large.

Cautions with use of AI:
The first issue with AI is that its ability and potential is often misused. Some doctors fear AI may one day take over their roles. The use of laser-knives, surgical implements, tasers for immobilization, and deep-neural stimulation of the brain to prevent seizures are being used to take healthcare to the underprivileged masses. Harnessing the potential of AI is an issue of ethics, the patient’s privacy, and lives. Doctors need to use these tools to aid and not replace human-intervention in healthcare.

The issue of data transfers, privacy issues, legal responsibilities, misuse, and selling of data is of high importance. Another issue that looms large is the inertia to change and sufficient testing before large-scale adoption in healthcare for the masses. An approach that is amply cautious is always the best when millions are being spent on healthcare, and the lives of masses are at stake.

The right way to implement AI into the healthcare system would then hinge on education and training, sufficient testing and trial-runs before implementation, assuring and involving all stakeholders, and rediscovering ways and means of optimizing human-intervention in diagnosis, treatment, and care of patients.

The industrial sector will see thousands of opportunities thrown up which in turn when exploited create growth and employment opportunities galore. AI in healthcare is truly a panacea-producing tool. Perhaps in the near future immortality-quest will also take its place in the fields being explored. For now, the Government, doctors, researchers, industries, and patients are all set to accept the positive impact of AI on the healthcare sector readily.

What is the Artificial Intelligence Markup Language?

Posted on by Imarticus Learning

Artificial intelligence is the technology of the future. It has exploded onto the world ever since it was first developed, and the technology has since been implemented in a lot of fields, ranging from healthcare to warfare. AI looks all set to stay and is sure to play a huge role in how the future of humanity is shaped.

However, it should be noted that AI was not always developed using popular languages today. Currently, Python and R represent the most popular languages which are used in machine learning and consequently, in AI too. However, there are a lot of other languages and methods which were used at times to various ends.

AIML was one such language which was used in the development of early chatbots. Digital assistants or chatbots truly represent the dawn of a new chapter in the scientific advancements of humankind. Chatbots are now increasingly becoming a part of most companies, and most of the internet users have already interacted with a chatbot in some form or other.

Being an AI aficionado or a prospective practitioner, you can surely try to build a chatbot from scratch in order to gain some practice in Artificial Intelligence.

What is AIML?
Artificial Intelligence Markup Language or AIML was created by Dr Richard Wallace and is currently offered as an open source framework for developing chatbots. It is offered by the ALICE AI Foundation so that users can create intelligent chatbots for their use from scratch.

AIML is an extremely simple XML, just like HyperText Markup Language or HTML. It contains a lot of standard tags and tags which are extensible, which you use in order to mark the text so that the interpreter which runs in the background understands the text you have scripted.

If you want the chatbot to be intelligent, it is important to have a content interface through which you can chat. Just like XML functions, AIML also characterizes rules for patterns, and decide how to respond to the user accordingly. AIML has several elements in them, including categories, patterns, and templates.

Categories are the fundamental units of knowledge which are used by the AIML and is further divided into the two other elements mentioned above – templates and patterns. In layman’s terms, patterns represent the questions asked by the user to the chatbot, or what the chatbot perceives as questions which need to be responded to.

The templates are the answers which it remembers based on its training, and which are subsequently modified and presented as replies to the users. Template elements basically include text formatting for the responses, conditional responses taught to it including many if/else scenarios and random responses which always come in handy while interacting with a user.

AIML is now open source, and users can start to create a chatbot by learning the fundamentals of the language. If you find yourself yearning to know more about this and AI in general, you should check out the many artificial intelligence courses on offer at Imarticus Learning.

Understand the Difference: Artificial Intelligence Vs Machine Learning

Posted on by Imarticus Learning

Artificial Intelligence and Computer Sciences, data sciences and nearly everyone today uses the terms Machine Learning/ML and AI/ Artificial Intelligence interchangeable when both are very important topics in a Data Science Course. We need to be able to differentiate the basic functions of these two terms before we do a data science tutorial where both ML and AI are used on another factor namely data itself.
AI is not a stand-alone system in the data science tutorial. It is a part of the programming that artificially induces intelligence in devices and non-humans to make them assist humans with what is now called the ‘smart’ capability. Some interesting examples of AI we see in daily life are chatbots, simple lift-arms in warehousing, smart traffic lights, voice-assistants like Google, Alexa, etc.
ML is about training the machine through algorithms and programming to enable them to use large data volumes, spot the patterns, learn from it and even write its own self-taught algorithms. This experiential learning is being used to produce some wonderful machines in detecting cancers and brain tumours non-invasively, spot trends and patterns, give recommendations, poll trends, automated driverless cars, foresight into possibilities of machine failure, tracking vehicles in real-time, etc. It is best learned at a formal Data science Course.

Difference Between Machine Learning And Artificial Intelligence

Here are the basic differences between ML and AI in very simple language.

  • ML is about how the machine uses the algorithm to learn. AI is the ability of machines to intelligently use the acquired knowledge.
  • AI’s options are geared to succeed. ML looks for the most accurate solution.
  • AI enables machines through programming to become smart devices while ML relates to data and the learning from data itself.
  • The solutions in AI are decision-based. Ml allows machines to learn.
  • ML is task and accuracy related where the machine learns from data to give the best solution to a task. AI, on the other hand, is about the machine mimicking the human brain and its behavior in resolving problems.
  • AI chooses the best solution through reasoning. ML has only one solution with which the machine creates self-learned algorithms and improves accuracy in performing the specific task.

Both AI and ML exist with the very life-breath of data. The interconnection is explained best through ‘smart’ machines to do such human-tasks through ML algorithms to scour and enable the final inferential steps of gainful data use. AI and ML are both essential to handle data which can run into a variety of complex issues in managing data. ML is the data science tutorial way you would train, imbibe and enable the computers and devices to learn from data and do all jobs using algorithms. Whereas AI itself refers to using machines to do the tasks which are in data-terms far beyond human computing capabilities. And in short, the data scientist/analyst is the one person who uses both AI and ML in his career to effectively use data and tools from both AI and ML suites.
One does not need a technical degree to choose the umbrella career of data science which teaches you both AI and ML. However, it is a must that you get the technical expertise and certification which is a validation of being job prepared from a reputed institute like Imarticus by doing their Data science Course. You will need an eclectic mix of personal traits, technologically sound knowledge of AI, ML, programming languages and a data science tutorial to set you on the right track. Hurry!
Conclusion:
The modern day trend of using data which is now an asset to most organizations and daily life can be put to various applications that can make figuring out complex data and life simpler by using AI achieved through ML programming.
The Data science Course at Imarticus Learning turns out sought-after trained experts who are paid very handsomely and never suffer from want of job-demand. Data grows and does so every moment. Do the data science tutorial to emerge career-ready in data analytics with a base that makes you a bit of a computer and databases scientist, math expert and trend spotter with the technical expertise to handle large volumes of data from different sources, clean it, and draw complex inferences from it.

7 Horrible Mistakes you’re making with Artificial Intelligence

Posted on by Imarticus Learning

We could notice that, numerous marketers commit mistakes with regards to AI. That is a common thing. We’ve done it, as well. It requires a lot of investment to get settled with AI.
In any case, a few mistakes are more inflated than others. Furthermore, these mistakes will bring your association down the wrong track with regards to AI.
No one wants to get in that bad situation. So to prevent this issue and to get benefit from AI later, marketers should think to avoid below 7 horrible mistakes that they are making while implementing a machine learning or an artificial intelligence course.
1. Thinking AI usage is simple.
Several marketers think if they have the accurate information, implementation is easy. a few of the AI tools are very simple to utilize and you can begin quickly. But transforming your association into an AI-driven organization is another responsibility completely. Receiving AI association wide requires some serious energy. It needs cash. What’s more, it takes experimentation.
You need to commit for the long period. In reality, the correct data and methodology are fundamental. Implementation is secondarily come!
2. Marking down artificial intelligence altogether.
The opposite side of the coin is advertisers who trust AI are all publicity. We get it. There is a huge amount of promotion out there and a ton of extremely strong claims. Normally, you may trust AI is simply one more popular buzzword.
Nothing could be further from reality. Over the most recent couple of years, critical advances in AI and machine learning have happened. This is an undeniable, exceptionally impact arrangement of technologies that will influence your profession.
3. Focusing on complete automation
Businesses aiming for entire automation process might merely save the salaries of the populace being supposedly substituted by AI. As per Jeremy, businesses that target to make a return on the employees by enhancing and rising workforce competence using AI would attain noteworthy ROI.
4. Fixating on where AI is going.
We get it. We cherish guessing about where AI is going. We even have a deadline for when our machine overlords will make their play for global control. But an excess of hypothesis on the most distant eventual fate of AI is diverting.
There are numerous miracles ahead as we enter the period of AI. Give yourself a little AI wandering off in fantasy land time, beyond any doubt. But, at that point discover a couple genuine implementation cases you can begin applying AI to begin at this point.
5. Thinking beginning with AI is too hard or excessively specialized.
It certainly requires some investment to get settled with ideas in Artificial Intelligence. What’s more, profoundly understanding the tech probably won’t be simple for the non-engineers among us.
This is not a regulation only for the technicians. As an advertiser, you have a gigantic chance to attach the specialized to the commonsense and discover genuine implementation cases for AI.
6. An inadequate foundation for machine learning
For most associations, dealing with the different parts of the foundation encompassing machine learning exercises can turn into a test all by itself. Trusted and dependable social database service frameworks can bomb totally under the load an assortment of data that organizations seek out to collect and investigate today.
7. Assuming AI can’t execute whatever marketers perform.
Indeed, even with a sound thankfulness for AI’s potential, it’s anything but difficult to laugh at it. How might it displace you or your partners? We can’t wait how ground-breaking AI will be, so we’re not saying it’ll replace anybody. Yet, it will change the idea of your work.
AI can do the plethora of things that marketers do today, quicker, less expensive and at scale. Inside this reality lies either guarantee or risk, reliant upon how you see it.
Marketers need to turn an attentive eye to how they create importance for associations and highlight the high-esteem innovative work

Artificial Intelligence Provides Operational Solutions for the Food Industry!

Posted on by Imarticus Learning

Though Artificial Intelligence (AI) technologies have supported industries in multiple ways, the key is to identify areas specific to each industry where AI solutions are the most relevant. In the case of the food industry, solving operational efficiencies seems to be the area where AI-based solutions can make the maximum impact. And no wonder, with the relatively short timelines that food can be stored before consumption, make this an understandable challenge.

AI or machine learning relies a lot on historic data and uses this information to make predictive solutions or suggestions that can help in foreseeing certain outcomes. The more data at hand, the more closer to accuracy the solution/suggestion is.

Considering this, here are the potential avenues for the use of AI in the food business in ways that could transform conventional modes of operation by increasing efficiencies and production, predicting, assessing, and accurately solving more market demands and more.

Forecasting
Companies have used AI to determine and analyze demand variations, shopping trends during marketing campaigns, and sales drops. Stored data for these variables help machines identify problem areas and solve for them specifically.

It answers questions like – what is the optimal shelf space for this product to ensure increased sales? Which categories perform best for a specific type of promotion? How much should a certain product be stocked during peak/low sales periods? This helps optimize processes and reduce wastes through AI’s intelligent data-back prediction systems.

Boosting Productivity
Cloud computing technology, Big Data analytics, and data-driven machine learning has equipped a lot of industries to streamline their operational efficiencies. In the case of food industries, the manufacturing arm, in particular, AI assists in aiding the production processed by making certain decisions easier through its predictive features. These real-time solutions can potentially save a lot in time and moolah. These cost benefits will in turn be reflected in market satisfaction through pricing.

Automation
Technology’s increased agility in handling fragile produce helps in automating manufacturing tasks in the food industry’s operational chain. This means, tools have become advanced enough to handle delicate food items and process them without damaging them, such as eggs or tomatoes.

Not only this, but automation helps in reducing manual effort in repetitive tasks, therefore, adding time efficiency. This is especially useful for tasks where is lower decision-making potential.

Consumer Preferences
Through AI’s capability to handle large amounts of data with multiple variables and therefore make accurate predictions, consumer preferences can be assessed through their older buying / consuming patterns. Not only does this help in the development of newer products and services but also capitalizes on the key sale-drivers with eagle-eyed consistent focus.

Applications
Some ways to apply AI or machine learning in these industries would be through smartphone apps that fit into the consumer’s lifestyle, such as fitness apps, food suggestion apps based on certain body types, etc. Chatbots for online food partners could be another potential application. Quick food manufacturing machines independent of human assistance is another use case.

Conclusion
What this means for the food industry is that there is a constant need to keep an eye on AI trends and the way it is affecting businesses. Choosing the right AI tool for a certain business is a sure-shot way of intelligently increasing efficiencies and reducing costs. There are many more upcoming AI solutions in the market – keep your eyes on the radar to assess the best solution for your business!

The Promise of AI: The Value of Artificial Intelligence and its Applications

Posted on by Imarticus Learning

Read the previous part here.
When it comes to predicting the value of AI, things can get a little vague and confusing. This is mainly because of the wide applications as well as the rapid evolvement of Artificial Intelligence. It is estimated by the Data Corporation, that the market for technologies of or related to Artificial Intelligence, is bound to increase up to $40 billion by the year 2020. While this market would deal with all those technologies that help in analysing unstructured data, it is believed that it will be generating productive improvements, which promise to be well over $60 billion worth of the United States market per year.
Consequently, a majority of investors have begun to discover and realize the true value of AI and as a result of this, more and more investments have begun to take place in the venture capitalist markets. Let’s talk about figures, the year 2013 saw about $757 million, being invested in AI start-ups, 2014 saw about $2.8 billion, 2015 saw around $2.3 billion and if these figures are anything but telling, it is guaranteed that the investments will only be growing in proportion for the coming years. The Mckinsey Global Institute has estimated that all the automating knowledge work, with Artificial Intelligence, is bound to generate anything around $5.2 trillion to close to $7 trillion. It is believed that the advancement in advanced robotics by primarily relying on Artificial Intelligence will generate anything up to $2 trillion. All of these estimates are made for up to the year 2025, which is barely a decade away.
Thus, we can conclude that the value of Artificial Intelligence is not only humongous, but it bound to increase manifold soon enough. It is believed by many experts that the AI has so much power, that it can go ahead to solve global issues like climate change and food insecurity. Artificial Intelligence already has and is bound to have a varied number of applications. Mapping poverty with satellite data, measuring literacy rates, cracking down on human trafficking rackets, preventing abusive internet tools from taking up actions are some of the social applications of AI that will definitely benefit the society in the near future.
When it comes to public safety, artificial intelligence could very well be applied for pinpointing the various happenings during crimes, for prediction of crime hotspots, for disposing of car bombs autonomously, for predicting the fire or earthquake risks of building accurately, for ensuring that every type of security screening is less invasive and so on. Industries would benefit majorly from AI as it would ensure the prevention of breakdowns or power cuts before they even take place, everything from intelligent manufacturing to automated factories could be very possible, improvement in terms of the dairy supply chains with the help of market forecasting would be a real thing and much more.
Thus AI shows a lot of promise in terms of changing the world for the better tomorrow and as a result of this, it would make a great career. There are many institutes in India like Imarticus Learning, which offer a number of comprehensive courses in Data Science, that would help an individual further their career in AI.

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The Promises of Artificial Intelligence: Introduction

Posted on by Imarticus Learning

The field of Artificial Intelligence seems to working on a winning streak. In the year 2005, the U. S Defence Advance Research Project Agency, held the DARPA Grand Challenge, which was supposedly held to spur development of autonomous vehicles, basically in order to make self-driven, smart cars. This challenge was taken up and successfully completed by 5 teams. In the year 2011, in a great competition of Jeopardy, the IBM Watson system, was successfully able to beat two long time, human champions of the same legendary game. Another great win of technology over the human race would be in the year 2016, when Google DeepMind’s AlphaGo system was able to successfully defeat the world champion of Go Player, who was reportedly the world champion for 18 consecutive times.
While these feats of technology over the human brain are extremely commendable, today the long surviving dream of humans, which basically revolved around developing technology to control their surroundings, has finally come to fruition. This has resulted in the form of Google’s Google Assistant, Microsoft’s Cortana, Apple’s Siri and Amazon’s Alexa. As a result of all of these AI (Artificial Intelligence) powered virtual assistants, people are able to make greater use of technology in order to live better lives.
Artificial Intelligence is considered to be a field of computer science, which is entirely devoted to the creation of computing machines and systems, all of which are able to perform operations that are similar to human learning and decision making. According to the Association for the Advancement of Artificial Intelligence, AI is, “the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines.” While these intelligence levels can never be compared to those of the humans, but they can certainly vary in terms of various technologies.
Artificial Intelligence includes a number of functions, which include learning, which primarily includes a number of approaches such as deep learning, transfer learning, human learning and especially decision making. All of these functionalities can later help in the execution of various fields such as cardiology, accounting, law, deductive reasoning, quantitative reasoning, and mainly interactions with people, in order to not only perform tasks, but also to learn from the environment.
While the recent changes may be extremely mind blowing, the promise of AI has always been existing since era of electromechanical computing, this began in the time period, after the World War 2. The first conference of Artificial Intelligence was held at the college of Dartmouth in the year 1956 and at that time, it was said that AI could be achieved within the time period of summer. Later on, in the 1960’s there were scientists, who claimed that in the next decade, it would be possible to see various machines controlling human lives. But it was in the year 1965, when the Nobel Laureate, Herbert Simon, who is supposed to have predicted the words, which would have some substance and which were, “In the next 20 years, it would be possible that machines would be able to do any work of labour that man can”.
With Artificial Intelligence, going in full fervour, the field which it has affected most in the field of Data Science. And as there are many who believe that there is a great to achieve in this field, have begun to get trained in the same by approaching professional training institute – Imarticus Learning.