Top 10 Hot Artificial Intelligence Technologies

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Introduction to Artificial Intelligence

Talking of advancements, from Abacus to Super-Computers, the world has come a long way. The world, say a hundred years ago was highly dependent on manual execution. Simple tasks such as arithmetic operations too were long and tedious. Having realized this difficulty, various technologies capable of executing complex calculations were introduced.

These technologies grew rapidly and soon the world saw it’s potential. Calculations now were quicker and accurate. Such technologies saw a great implementation in various fields including research and development, defence, healthcare, business, etc.

But however efficient these machines were, there was always a lack of “Intelligence”. Computers may be reliable, accurate and a gazillion times faster than a human but they were all but “dumb machines”.

Artificial Intelligence is a concept far superior to any other concept and aims to make machines able to learn and respond on their own.

Though the term Artificial Intelligence has been around for more than 5 decades, it was not until 2 decades before that the world started realizing its huge potential. Artificial Intelligence has a plethora of applications in areas such as Natural Language Processing, Simulations, Robotics and Speech Recognition to name a few.

While the potential of Artificial Intelligence and its applications has been realized, but due to the complexities involved, the advancements in this field, as of now, is only restricted to the development of Weak Artificial Intelligence Systems also known as Narrow Artificial Intelligence Systems.

There has been a steady development in the field of Artificial Intelligence and the growth is exponential. Today, Artificial Intelligence is everywhere. From Google to Facebook and Shopping to Learning, Artificial Intelligence is at the forefront.

There are many technologies in existence today that have a direct or indirect application of Artificial Intelligence. Let’s have a look at some of these latest Artificial Intelligence technologies to understand better:

Natural Language Generation

Popularly known as “Language Production” among Psycholinguists, Natural Language Generation is a procedure that aims to transform any structured data into a natural language. In layman terms, natural language generation can be thought of as a process that converts thoughts to words.

For example, when a child looks at a butterfly flying in a garden, he may think of it in various ways. Those thoughts may be called ideas. But when the child describes his thought process in his natural language (mother tongue), this process may be termed as Natural Language Generation.

Natural Language Understanding

Natural Language Understanding is the opposite of Natural Language Generation. This procedure is more inclined towards the interpretation of Natural Language.

In the example above, if the child is told about the butterfly rather than shown, he may interpret the data given to him in a variety of ways. Based on that interpretation, the boy will make a picture of a butterfly flying in a garden. If the interpretation was correct, then one may infer that the procedure (Natural Language Understanding) was successful.

Speech Recognition

As the name suggests, Speech Recognition is a technology that uses Artificial Intelligence to convert human speech into a computer-accessible format. The process is very helpful and acts as a bridge in human-computer interaction.

Using Speech Recognition technology, the computer can understand human speech in several natural languages. This further enables the computer to have a faster and smoother interaction with humans.

For example, let’s say that the child in the first example was asked, “How are you?” during a normal human to human interaction. When the child listens to the human speech sample, he processes the sample according to the data (knowledge) already present in his brain.

The child draws necessary inferences and finally comes up with an idea about what the sample is about. This way, the child can understand the meaning of the speech sample and respond accordingly.

Machine Learning

Machine Learning is yet another useful technology in the Artificial Intelligence domain. This technology is focussed on training a machine (computer) to learn and think on its own. Machine Learning typically uses many complex algorithms for training the machine.

During the process, the machine is given a set of categorized or uncategorized training data pertaining to a specific or a general domain. The machine then analyses the data, draws inferences and stores them for future use.

When the machine encounters any other sample data of the domain it has already learned, it uses the stored inferences to draw necessary conclusions and give an appropriate response.

For example, let’s say that the child in the first example was shown a collection of toys. The child interacts (using his senses like touch, see, etc.) with the training data (toys) and learns about the toys’ properties. These properties can be anything from size, colour, shape, etc. of the toys.

Based on his observations the child stores the inferences and uses them to distinguish between any other toys that he may have any future encounters with. Thus, it can be concluded that the child has learned.

Virtual Agents

Virtual Agents are a manifestation of a technology which aims to create an effective but digital impersonation of humans. Quite popular in the customer care domain, Virtual Agents use the combination of Artificial Intelligence programming, Machine Learning, Natural Language Processing, etc. to understand the customer and his grievances.

A clear understanding by the Virtual Agents is subject to the complexity and technologies used in the creation of the agent. These systems are nowadays highly used through a variety of applications such as chatbots, affiliate systems, etc. These systems are capable of interacting with humans in a humane way.

In the above-mentioned examples, if the child is considered a Virtual Agent and is made to interact with unknown participants, the child will use a combination of his already learned knowledge, language processing and other necessary “tools” to understand the participant.

Once the interaction is complete, the child will derive inferences based on the interaction and be able to address the queries posed by the participant effectively.

Expert Systems

In the context of Artificial Intelligence, Expert Systems are computer systems that utilize a pre-stored knowledge base and mimic the decision-making ability of humans. These complex systems utilize reasoning ability and the predefined ‘if-then’ rules.

Contrary to conventional procedural code based machines, Expert Systems are highly efficient in solving complex problems. Extending the above examples a bit further, the child, based on his pre-existing knowledge base and inference deriving capability is capable of analyzing problems and suggest methods to solve them.

Decision Management

Modern Decision Management Systems highly rely on Artificial Intelligence abilities in interpreting and converting data into predictive models. These models, in the long run, help an organization to take necessary and effective decisions.

These systems are widely used in a vast number of enterprise-level applications. Such applications provide automated decision-making capabilities to any person or organization using it.

If the child in the above example is considered as a Decision Management System, based on the knowledge set and reasoning abilities, he shall be able to manage his decisions effectively. If the child is given access to a certain behavioural data of say 10 people, then the child will be able to make near-accurate predictions. Such predictions will govern the decisions the child will make to address the problem at hand.

Deep Learning

Deep Learning is a special subset of Machine Learning based on Artificial Neural Networks. During the process, learning is carried out at different levels where each level is capable of transforming the input data set into composite and abstract representations.

The term “deep” in this context refers to the number of levels of data transformation carried out by the computer system. The technology finds its applications in a variety of domains such as Computer Vision, News Aggregation (sentiment-based), development of efficient chatbots, automated translations, rich customer experience, etc.

For the sake of a simpler example, if the child in the above examples carries out learning restricted to only a single level, then the output (response) may not be specific to the problem but general. Learning at a deeper level helps the child in understanding the problem better. Hence it can be inferred that deeper the learning is, more accurate is the response.

Robotic Process Automation

Artificial Intelligence is also heavily used at industrial levels to automate various processes. While manual robotics is capable of completing the job, it lacks the necessary automation required to complete the task without human intervention.

Such automated systems help in larger domains where it is not feasible to employ humans. If the child, in the above examples, is considered a Robot without intelligence, he shall be dependent on others to carry out his chores.

While he may still be able to complete his work, he would not be able to do it all by himself. Intelligence enables him to work independently without having to rely on any external interventions.

Text Analytics

Text Analytics can be defined as an analysis of text structure. Artificially Intelligent Systems use text analytics to interpret and learn the structure, meaning, and intentions of text they may come across.

Such systems find their applications in security and fraud detection systems. An Artificial Intelligence enabled system can distinguish between any two types of text samples without any human intervention. This independence makes such a system effective, efficient and faster than its human counterparts.

The child’s intelligence, in the above examples, will also be able to make him capable of distinguishing between the handwritings written by his family members.

To summarize, Artificial Intelligence finds a variety of applications in various fields. In all the examples mentioned above, the child was able to tackle all the problems independently because he was intelligent and was not dependent on external instructions but relied on his own inferences.

Artificial Intelligence Technologies


Being highly advanced and capable of solving very complex problems, Artificial Intelligence is the key to the future. Various industries and organizations today, are making extensive use of Artificial Intelligence to fulfil the requirements that were once considered very difficult to meet.

Modern research has suggested growth in Artificial Intelligence domain at the rate of 36.6 % and shall be worth $190.60 billion by the year 2025.

While all the artificial intelligence technologies are expecting a massive growth, Deep Learning is expected to grow the highest in terms of the Compound Annual Growth Rate (CAGR).

In terms of market share, Artificial Intelligence based software has been forecasted to hold the largest market share. While in terms of geographical area, Asia Pacific is the top contender in terms of the highest Compound Annual Growth Rate (CAGR) and North America is to hold the largest market share.

In a span of just around two decades, Artificial Intelligence has made an exemplary mark on today’s Information Technology industry. It has further provided an impressive set of tools and applications having a wider range in various domains.

Artificial Intelligence has changed the understanding of the world regarding the power of reasoning and methods of problem-solving. Additionally, it has also enlightened us about the complexity of human intelligence.

While some people may perceive Artificial Intelligence as a threat to human existence, responsible and limited use will help humans and technology to co-exist together. Such a co-existence, together, will help in reshaping the very reality we live in and change the face of this world entirely.

For those who are interested in pursuing a career in Artificial Intelligence, check out Great Learning’s PG program in Artificial Intelligence and Machine Learning.

Your essential weekly guide to Artificial Intelligence – October 2019 Part I

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In this week’s Artificial Intelligence digest, we have included some of the latest developments in the field of Artificial Intelligence. These developments suggest a bright future for AI applications across industries and the increasing scope of these technologies in creating more jobs globally in the coming years. 

Unveiling Three Ways Artificial Intelligence is Disrupting the Media and Entertainment Sector

Media and entertainment companies face several challenges that can be attributed to factors such as live streaming of content, unpredictable traffic, personalization and publishing permission-based content. Companies in the media and entertainment sector are investing a significant portion of their budget to improve bandwidth for streaming content seamlessly.

Alibaba Unveils Chip Developed for Artificial Intelligence

The Chinese e-commerce group unveiled the Hanguang 800 on Wednesday, which is designed to carry out the type of calculations used by AI applications much faster than conventional chips.

Software Robots Are Guiding us Into Intelligent Automation, With Less Stress

The big-bang AI and machine learning projects may be stuck in pilots and proofs of concepts, but a less glamorous form of intelligent automation may be percolating its way through processes and channels across enterprises — software bots associated with robotic process automation (RPA). These bots usually take on single-purpose tasks, such as pulling data for a purchase order or delivering an email confirming with a transaction. A majority of enterprises surveyed by Deloitte last year, 53 per cent, report they have put RPA in place, with 72 per cent expecting to do so within the next two years.

Artificial Intelligence Has Become a Tool For Classifying And Ranking People

AI’s ability to classify and rank people, to separate them according to whether they’re “good” or “bad” in relation to certain purposes. At the moment, Western civilization hasn’t reached the point where AI-based systems are used en masse to categorize us according to whether we’re likely to be “good” employees, “good” customers, “good” dates and “good” citizens. Nonetheless, all available indicators suggest that we’re moving in this direction.

Meanwhile, if you are interested to know more about the field of artificial intelligence, we have created a complete tutorial for you to know all about the different concepts and subjects under AI, along with its applications and career scope across industries. You can read the full article here: Artificial Intelligence Tutorial: Everything you Need to Know


If you found these articles to be interesting then head over to our Artificial Intelligence blog to get more updates.

What is Artificial Intelligence- A Complete Beginners’ Guide to AI- Great Learning

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  1. What is Artificial Intelligence?
  2. How do we measure if the AI is acting like a human?
  3. How Artificial Intelligence works?
  4. What are the three types of Artificial Intelligence?
  5. What is the purpose of AI?
  6. Where is AI used?
  7. What are the disadvantages of AI?
  8. Applications of Artificial Intelligence in business?
  9. What is ML?
  10. What are the different kinds of Machine Learning?
  11. What is Deep Learning?
  12. What is NLP?
  13. What is Python?
  14. What is Computer Vision?
  15. What are neural networks?
  16. Conclusion

What is Artificial Intelligence?

The short answer to What is Artificial Intelligence is that it depends on who you ask. A layman with a fleeting understanding of technology would link it to robots. They’d say AI is a terminator like-figure that can act and think on its own. An AI researcher would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So to summarise, AI is:

– An intelligent entity created by humans.

– Capable of performing tasks intelligently without being explicitly instructed.

– Capable of thinking and acting rationally and humanely.

How do we measure if the AI is acting like a human?

Even if we reach that state where an AI can behave as a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:

– Turing Test

– The Cognitive Modelling Approach

– The Law of Thought Approach

– The Rational Agent Approach

What is Artificial Intelligence

Let’s take a detailed look at how these approaches perform:

What is the Turing Test?

The basis of the Turing Test is that the AI entity should be able to hold a conversation with a human agent. The human agent ideally should not able to discern that they are talking to an AI. To achieve these ends, the AI needs to possess these qualities:

Natural Language Processing to communicate successfully.

– Knowledge Representation to act as its memory.

– Automated Reasoning to use the stored information to answer questions and draw new conclusions.

– Machine Learning to detect patterns and adapt to new circumstances.

Cognitive Modelling Approach

As the name suggests, this approach tries to build an AI model-based on Human Cognition. To distil the essence of the human mind, there are 3 approaches:

– Introspection: observing our thoughts, and building a model based on that

– Psychological Experiments: conducting experiments on humans and  observing their behaviour

– Brain Imaging – Using MRI to observe how the brain functions in different scenarios and replicating that through code.

The Laws of Thought Approach

The Laws of Thought are a large list of logical statements that govern the operation of our mind. The same laws can be codified and applied to artificial intelligence algorithms. The issues with this approach, because solving a problem in principle (strictly according to the laws of thought) and solving them in practice can be quite different, requiring contextual nuances to apply. Also, there are some actions that we take without being 100% certain of an outcome that an algorithm might not be able to replicate if there are too many parameters.

The Rational Agent Approach 

A rational agent acts to achieve the best possible outcome in its present circumstances.

According to the Laws of Thought approach, an entity must behave according to the logical statements. But there are some instances, where there is no logical right thing to do, with multiple outcomes involving different outcomes and corresponding compromises. The rational agent approach tries to make the best possible choice in the current circumstances. It means that it’s a much more dynamic and adaptable agent.

Now that we understand how AI can be designed to act like a human, let’s take a look at how these systems are built.

How Artificial Intelligence works?

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using it’s computational prowess to surpass what we are capable of.  AI can be built over a diverse set of components and will function as an amalgamation of:

– Philosophy

– Mathematics

– Economics

– Neuroscience

– Psychology

– Computer Engineering

– Control Theory and Cybernetics

– Linguistics

Let’s take a detailed look at each of these components.

What is Artificial Intelligence


The purpose of philosophy for humans is to help us understand our actions, their consequences, and how we can make better decisions. Modern intelligent systems can be built by following the different approaches of philosophy that will enable these systems to make the right decisions, mirroring the way that an ideal human being would think and behave. Philosophy would help these machines think and understand about the nature of knowledge itself. It would also help them make the connection between knowledge and action through goal-based analysis to achieve desirable outcomes.

Also Read: Artificial Intelligence and The Human Mind: When will they meet?


Mathematics is the language of the universe and system built to solve universal problems would need to be proficient in it. For machines to understand logic, computation, and probability are necessary.

The earliest algorithms were just mathematical pathways to make calculations easy, soon to be followed by theorems, hypotheses and more, which all followed a pre-defined logic to arrive at a computational output. The third mathematical application, probability, makes for accurate predictions of future outcomes on which AI algorithms would base their decision-making.


Economics is the study of how people make choices according to their preferred outcomes. It’s not just about money, although money the medium of people’s preferences being manifested into the real world. There are many important concepts in economics, such as Design Theory, operations research and Markov decision processes. They all have contributed to our understanding of ‘rational agents’ and laws of thought, by using mathematics to show how these decisions are being made at large scales along with their collective outcomes are. These types of decision-theoretic techniques help build these intelligent systems.


Since neuroscience studies how the brain functions and AI is trying to replicate the same, there’s an obvious overlap here. The biggest difference between human brains and machines is that computers are millions of times faster than the human brain, but the human brain still has the advantage in terms of storage capacity and interconnections. This advantage is slowly being closed with advances in computer hardware and more sophisticated software, but there’s still a big challenge to overcome as are still not aware of how to use computer resources to achieve the brain’s level of intelligence.


Psychology can be viewed as the middle point between neuroscience and philosophy. It tries to understand how our specially-configured and developed brain reacts to stimuli and responds to its environment, both of which are important to building an intelligent system. Cognitive psychology views the brain as an information processing device, operating based on beliefs and goals and beliefs, similar to how we would build an intelligence machine of our own.

Many cognitive theories have already been codified to build algorithms that power the chatbots of today.

Computer Engineering

The most obvious application here, but we’ve put this the end to help you understand what all this computer engineering is going to be based on. Computer engineering will translate all our theories and concepts into a machine-readable language so that it can make its computations to produce an output that we can understand. Each advance in computer engineering has opened up more possibilities to build even more powerful AI systems, that are based on advanced operating systems, programming languages, information management systems, tools, and state-of-the-art hardware.

Control Theory and Cybernetics

To be truly intelligent, a system needs to be able to control and modify its actions to produce the desired output. The desired output in question is defined as an objective function, towards which the system will try to move towards, by continually modifying its actions based on the changes in its environment using mathematical computations and logic to measure and optimise its behaviours.


All thought is based on some language and is the most understandable representation of thoughts. Linguistics has led to the formation of natural language processing, that help machines understand our syntactic language, and also to produce output in a manner that is understandable to almost anyone. Understanding a language is more than just learning how sentences are structured, it also requires a knowledge of the subject matter and context, which has given rise to the knowledge representation branch of linguistics.

Read Also: Top 10 Artificial Intelligence Technologies in 2019

What are the 3 types of Artificial Intelligence?

Not all types of AI uses all the above fields simultaneously. Different AI entities are built for different purposes, and that’s how they vary. The three broad types of AI are:

– Artificial Narrow Intelligence (ANI)

– Artificial General Intelligence (AGI)

– Artificial Super Intelligence (ASI)

What is Artificial Intelligence

Let’s take a detailed look.

What is Artificial Narrow Intelligence (ANI)?

This is the most common form of AI that you’d find in the market now. These AI systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of AI that exists today. They’re able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters.

What is Artificial General Intelligence (AGI)?

AGI is still a theoretical concept. It’s defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

We’re still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. Even with the most advanced computing systems and infrastructures, such as Fujitsu’s K or IBM’s Watson, it has taken them 40 minutes to simulate a single second of neuronal activity. This speaks to both the immense complexity and interconnectedness of the human brain, and to the magnitude of the challenge of building an AGI with our current resources.

What is Artificial Super Intelligence (ASI)?

We’re almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI. An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships.

Once we achieve Artificial General Intelligence, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of. While the gap between AGI and ASI would be relatively narrow (some say as little as a nanosecond, because that’s how fast AI would learn) the long journey ahead of us towards AGI itself makes this seem like a concept that lays far into the future.

What is the Purpose of AI?

The purpose of AI is to aid human capabilities and help us make advanced decisions with far-reaching consequences. That’s the answer from a technical standpoint. From a philosophical perspective, AI has the potential to help humans live more meaningful lives devoid of hard labour, and help manage the complex web of interconnected individuals, companies, states and nations to function in a manner that’s beneficial to all of humanity.

Currently, the purpose of AI is shared by all the different tools and techniques that we’ve invented over the past thousand years – to simplify human effort, and to help us make better decisions. AI has also been touted as our Final Invention, a creation that would invent ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.

That’s all in the far future though – we’re still a long way from those kinds of outcomes. Currently, AI is being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and to make business predictions based on hard data rather than gut feelings. As all technology that has come before this, the research and development costs need to be subsidised by corporations and government agencies before it becomes accessible to everyday laymen.

Where is AI used?

AI is used in different domains to give insights into user behaviour and give recommendations based on the data. For example, Google’s predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increase watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on their facial features in their images. AI is used everywhere by large organisations to make an end user’s life simpler. The uses of AI would broadly fall under the data processing category, which would include the following:

– Searching within data, and optimising the search to give the most relevant results

– Logic-chains for if-then reasoning, that can be applied to execute a string of commands based on parameters

– Pattern-detection to identify significant patterns in large data set for unique insights

– Applied probabilistic models for predicting future outcomes

What are the disadvantages of AI?

As is the case with any new and emerging technology, AI has its fair share of drawbacks too such as:

– Cost overruns

– Dearth of talent

– Lack of practical products

– Lack of standards in software development

– Potential for misuse

Let’s take a closer look.

Cost overruns

What separates AI from normal software development is the scale at which they operate. As a result of this scale, the computing resources required would exponentially increase, pushing up the cost of the operation, which brings us to the next point.

Dearth of talent 

Since it’s still a fairly nascent field, there’s a lack of experienced professionals, and the best ones are quickly snapped up by corporations and research institutes. This increases the talent cost, which further drives up AI implementation prices.

Lack of practical products

For all the hype that’s been surrounding AI, it doesn’t seem to have a lot to show for it. Granted that applications such as chatbots and recommendation engines do exist, but the applications don’t seem to extend beyond that. This makes it difficult to make a case for pouring in more money to improve AI capabilities.

Lack of standards in software development

The true value of AI lays in collaboration when different AI systems come together to form a bigger, more valuable application. But a lack of standards in AI software development means that it’s difficult for different systems to ‘talk’ to each other. AI software development itself is slow and expensive because of this, which further acts as an impediment to AI development.

Potential for Misuse

The power of AI is massive, and it has the potential to achieve great things. Unfortunately, it also has the potential to be misused. AI by itself is a neutral tool that can be used for anything, but if it falls into the wrong hands, it would have serious repercussions. In this nascent stage where the ramifications of AI developments are still not completely understood, the potential for misuse might be even higher.

Applications of Artificial Intelligence in business?

AI truly has the potential to transform many industries, with a wide range of possible use cases. What all these different industries and use cases have in common, is that they are all data-driven. Since AI is an efficient data processing system at its core, there’s a lot of potential for optimisation everywhere.

What is Artificial Intelligence

Let’s take a look at the industries where AI is currently shining.


– Administration: AI systems are helping with the routine, day-to-day administrative tasks to minimise human errors and maximise efficiency. Transcriptions of medical notes through NLP and helps structure patient information to make it easier for doctors to read it.

– Telemedicine: For non-emergency situations, patients can reach out to a hospital’s AI system to analyse their symptoms, input their vital signs and assess if there’s a need for medical attention. This reduces the workload of medical professionals by bringing only crucial cases to them.

– Assisted Diagnosis: Through computer vision and convolutional neural networks, AI is now capable of reading MRI scans to check for tumours and other malignant growths, at an exponentially faster pace than radiologists can, with a considerably lower margin of error.

– Robot-assisted surgery: Robotic surgeries have a very minuscule margin-of-error and can consistently perform surgeries round-the-clock without getting exhausted. Since they operate with such a high degree of accuracy, they are less invasive than traditional methods, which potentially reduces the time patients spend in the hospital recovering.

– Vital Stats Monitoring:  A person’s state of health is an ongoing process, depending on the varying levels of their respective vitals stats. With wearable devices achieving mass-market popularity now, this data is not available on tap, just waiting to be analysed to deliver actionable insights. Since vital signs have the potential to predict health fluctuations even before the patient is aware, there are a lot of live-saving applications here.


– Better recommendations: This is usually the first example that people give when asked about business applications of AI, and that’s because it’s an area where AI has delivered great results already. Most large e-commerce players have incorporated AI to make product recommendations that users might be interested in, which has led to considerable increases in their bottom-lines.

– Chatbots: Another famous example, based on the proliferation of AI chatbots across industries, and every other website we seem to visit. These chatbots are now serving customers in odd-hours and peak hours as well, removing the bottleneck of limited human resources.

– Filtering spam and fake reviews: Due to the high volume of reviews that sites like Amazon receive, it would be impossible for human eyes to scan through them to filter out malicious content. Through the power of NLP, AI can scan these reviews for suspicious activities and filter them out, making for a better buyer experience.

– Optimising search: All of the e-commerce depends upon users searching for what they want, and being able to find it. AI has been optimising search results based on thousands of parameters to ensure that users find the exact product that they are looking for.

– Supply-chain: AI is being used to predict demand for different products in different timeframes so that they can manage their stocks to meet the demand.

Human Resources 

– Building work culture: AI is being used to analyse employee data and place them in the right teams, assign projects based on their competencies, collect feedback about the workplace, and even try to predict if they’re on the verge of quitting their company.  

– Hiring: With NLP, AI can go through thousands of CV in a matter of seconds, and ascertain if there’s a good fit. This is beneficial because it would be devoid of any human errors or biases, and would considerably reduce the length of hiring cycles.

What is Artificial Intelligence

What is ML?

Machine learning is a subset of artificial intelligence (AI) which defines one of the core tenets of AI – the ability to learn from experience, rather than just instructions.

Machine Learning algorithms automatically learn and improve by learning from their output. They do not need explicit instructions to produce the desired output.  They learn by observing their accessible data sets and compares it with examples of the final output. The examine the final output for any recognisable patterns and would try to reverse-engineer the facets to produce an output.

What are the different kinds of Machine Learning?

The types of Machine learning are:

– Supervised Learning

– Unsupervised Learning

– Semi-supervised learning

– Reinforcement Learning

Read Also: Advantages of pursuing a career in Machine Learning

What is Supervised Learning?

Supervised Machine Learning applies what it has learnt based on past data, and applies it to produce the desired output. They are usually trained with a specific dataset based on which the algorithm would produce an inferred function. It uses this inferred function to predict the final output and delivers an approximation of it.

This is called supervised learning because the algorithm needs to be taught with a specific dataset to help it form the inferred function. The data set is clearly labelled to help the algorithm ‘understand’ the data better. The algorithm can compare its output with the labelled output to modify its model to be more accurate.

What is Unsupervised Learning?

With unsupervised learning, the training data is still provided but it would not be labelled. In this model, the algorithm uses the training data to make inferences based on the attributes of the training data by exploring the data to find any patterns or inferences. It forms its logic for describing these patterns and bases its output on this.

What is Semi-supervised Learning?

This is similar to the above two, with the only difference being that it uses a combination of both labelled and unlabelled data. This solves the problem of having to label large data sets – the programmer can just label and a small subset of the data and let the machine figure the rest out based on this. This method is usually used when labelling the data sets is not feasible, either due to large volumes of a lack of skilled resources to label it.

Read Also: Top 9 AI Startups in India

What is Reinforcement Learning?

Reinforcement learning is dependent on the algorithms environment. The algorithm learns by interacting with it the data sets it has access to, and through a trial and error process tries to discover ‘rewards’ and ‘penalties’ that are set by the programmer. The algorithm tends to move towards maximising these rewards, which in turn provide the desired output. It’s called reinforcement learning because the algorithm receives reinforcement that it is on the right path based on the rewards that it encounters. The reward feedback helps the system model its future behaviour.

What is Artificial Intelligence

What is Deep Learning?

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning concepts are used to teach machines what comes naturally to us humans. Using Deep Learning, a computer model can be taught to run classification acts taking image, text, or sound as an input.

Deep Learning is becoming popular as the models are capable of achieving state of the art accuracy. Large labelled data sets are used to train these models along with the neural network architectures.

Simply put, Deep Learning is using brain simulations hoping to make learning algorithms efficient and simpler to use. Let us now see what is the difference between Deep Learning and Machine Learning.

Deep Learning vs. Machine Learning

What is Artificial Intelligence

How is Deep Learning Used- Applications

Deep Learning applications have started to surface but have a much greater scope for the future. Listed here are some of the deep learning applications that will rule the future.

– Adding image and video elements – Deep learning algorithms are being developed to add colour to the black and white images. Also, automatically adding sounds to movies and video clips.

– Automatic Machine Translations – Automatically translating text into other languages or translating images to text. Though automatic machine translations have been around for some time, deep learning is achieving top results.

– Object Classification and Detection – This technology helps in applications like face detection for attendance systems in schools, or spotting criminals through surveillance cameras. Object classification and detection are achieved by using very large convolutional neural networks and have use-cases in many industries.

– Automatic Text Generation – A large corpus of text is learnt by the machine learning algorithm and this text is used to write new text. The model is highly productive in generating meaningful text and can even map the tonality of the corpus in the output text.

– Self-Driving cars – A lot has been said and heard about self-driving cars and is probably the most popular application of deep learning. Here the model needs to learn from a large set of data to understand all key parts of driving, hence deep learning algorithms are used to improve performance as more and more input data is fed.

– Applications in Healthcare – Deep Learning shows promising results in detecting chronic illnesses such as breast cancer and skin cancer. It also has a great scope in mobile and monitoring apps, and prediction and personalised medicine.

Why is Deep Learning important?

Today we can teach machines how to read, write, see, and hear by pushing enough data into learning models and make these machines respond the way humans do, or even better. Access to unlimited computational power backed by the availability of a large amount of data generated through smartphones and the internet has made it possible to employ deep learning applications into real-life problems.

This is the time for deep learning explosion and tech leaders like Google are already applying it anywhere and everywhere possible.

The performance of a deep learning model improves with an increase in the amount of input data as compared to Machine Learning models, where performance tends to decline with an increase in the amount of input data.

What is Artificial Intelligence

What is NLP?

A component of Artificial Intelligence, Natural Language Processing is the ability of a machine to understand the human language as it is spoken. The objective of NLP is to understand and decipher the human language to ultimately present with a result. Most of the NLP techniques use machine learning to draw insights from human language.

Read Also: Most Promising Roles for Artificial Intelligence in India

What are the different steps involved in NLP?

The steps involved in implementing NLP are:

– The computer program collects all the data required. This includes database files, spreadsheets, email communication chains, recorded phone conversations, notes, and all other relevant data.

– An algorithm is employed to remove all the stop words from this data and normalizes certain words which have the same meaning.

– The remaining text is divided into groups known as tokens.

– The NLP program analyzes results to spot deduce patterns, their frequency and other statistics to understand the usage of tokens and their applicability.

Where is NLP used?

Some of the common applications that are being driven by Natural Language Processing are:

– Language translation application

– Word processors to check grammatical accuracy of the text

– Call centres use Interactive Voice Response to respond to user requests; IVR is an application NLP

– Personal virtual assistants such as Siri and Cortana are a classic example of NLP

What is Python?

Python is a popular object-oriented programming language that was created by Guido Van Rossum and released in the year 1991. It is one of the most widely-used programming languages for web development, software development, system scripting, and many other applications.

Why is Python so popular?

What is Artificial Intelligence

There are many reasons behind the popularity of Python as the much-preferred programming language, i.e.,

– The easy to learn syntax helps with improved readability and hence the reduced cost of program maintenance

– It supports modules and packages to encourage code re-use

– It enables increased productivity as there is no compilation step making the edit-test-debug cycle incredibly faster

– Debugging in Python is much easier as compared to other programming languages

Read Also: Top Interview Questions for Python

Where is Python used?

Python is used in many real-world applications such as:

– Web and Internet Development

– Applications in Desktop GUI

– Science and Numeric Applications

– Software Development Applications

– Applications in Business

– Applications in Education

– Database Access

– Games and 3D Graphics

– Network Programming

How can I learn Python?

There is a lot of content online in the form of videos, blogs, and e-books to learn Python. You can extract as much information as you can through the online material as you can and want. But, if you want more practical learning in a guided format, you can sign up for Python courses provided by many ed-tech companies and learn Python along with hands-on learning through projects from an expert which would be your mentor. There are many offline classroom courses available too. Great Learning’s Artificial Intelligence and Machine Learning course have an elaborate module on Python which is delivered along with projects and lab sessions.

What is Computer Vision?

Computer Vision is a field of study where techniques are developed enabling computers to ‘see’ and understand the digital images and videos. The goal of computer vision is to draw inferences from visual sources and apply it towards solving a real-world problem.

What is Computer Vision used for?

There are many applications of Computer Vision today, and the future holds an immense scope.

– Facial Recognition for surveillance and security systems

– Retail stores also use computer vision for tracking inventory and customers

– Autonomous Vehicles

– Computer Vision in medicine is used for diagnosing diseases

– Financial Institutions use computer vision to prevent fraud, allow mobile deposits, and display information visually

What is Deep Learning Computer Vision

Following are the uses of deep learning for computer vision:

What is Artificial Intelligence

Object Classification and Localisation: It involves identifying the objects of specific classes of images or videos along with their location highlighted usually with a square box around them.

Semantic Segmentation: It involves neural networks to classify and locate all the pixels in an image or video.

Colourisation: Converting greyscale images to full-colour images.

Reconstructing Images: Reconstructing corrupted and tampered images.

What are Neural Networks?

Neural Network is a series of algorithms that mimic the functioning of the human brain to determine the underlying relationships and patterns in a set of data.

Read Also: A Peek into Global Artificial Intelligence Strategies

What are Neural Networks used for?

The concept of Neural Networks has found application in developing trading systems for the finance sector. They also assist in the development of processes such as time-series forecasting, security classification, and credit risk modelling.

What are the different Neural Networks?

What is Artificial Intelligence

The different types of neural networks are:

Feedforward Neural Network: Artificial Neuron: Here data inputs travel in just one direction, going in from the input node and exiting on the output node.

Radial basis function Neural Network: For their functioning, the radial basis function neural network consider the distance between a point from the centre.

Kohonen Self Organizing Neural Network: The objective here is to input vectors of arbitrary dimension to discrete map comprised of neurons.

– Recurrent Neural Network(RNN): The Recurrent Neural Network saves the output of a layer and feeds it back to the input for helping in predicting the output of the layer.

– Convolutional Neural Network: It is similar to the feed-forward neural networks with the neurons having learn-able biases and weights. It is applied in signal and image processing.

– Modular Neural Networks: It is a collection of many different neural networks, each processing a sub-task. Each of them has a unique set of inputs as compared to other neural networks contributing towards the output.

What are the benefits of Neural Networks?

The three key benefits of neural networks are:

– The ability to learn and model non-linear and complex relationships

– ANNs can generalize models infer unseen relationships on unseen data as well

– ANN does not impose any restrictions on the input variables


Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.

What is TensorFlow? The Machine Learning Library Explained

Reading Time: 6 minutes

The deep learning artificial intelligence research team at Google, Google Brain, in the year 2015 developed a software library which they named as TensorFlow for Google’s internal use. This Open-Source Software library is used by the research team to perform several important tasks.

TensorFlow is at present the most popular software library. There are several real-world applications of deep learning that makes TensorFlow popular. Being an Open-Source library for deep learning and machine learning, TensorFlow finds a role to play in text-based applications, image recognition, voice search, and many more. DeepFace, Facebook’s image recognition system, uses TensorFlow for image recognition. It is used by Apple’s Siri for voice recognition. Every Google app that you use has made good use of TensorFlow to make your experience better.

What are the Applications of TensorFlow?

  • Google use Machine Learning in almost all of its products: Google has the most exhaustive database in the world. And they obviously would be more than happy if they could make the best use of this by exploiting it to the fullest. Also, if all the different kinds of teams — researchers, programmers, and data scientists — working on artificial intelligence could work using the same set of tools and thereby collaborating with each other, all their work could be made much simpler and more efficient. As technology developed and our needs widened, such a toolset became a necessity. Motivated by this necessity, Google created TensorFlow  — a solution that they have been long waiting for.
  • TensorFlow bundles together the study of Machine Learning and algorithms and will use it to enhance the efficiency of its products — by improving their search engine, by giving us recommendations, by translating to any of the 100+ languages, and more.

What is Machine Learning?

A computer can perform various functions and tasks relying on inference and patterns as opposed to the conventional methods like feeding explicit instructions, etc. The computer employs statistical models and algorithms to perform these functions. The study of such algorithms and models is termed as Machine Learning.

Deep learning is another term that one has to be familiar with. A subset of Machine Learning, deep learning is a class of algorithms that can extract higher-level features from the raw input. Or, in simple words, they are algorithms that teach a machine to learn from examples and previous experiences. 

Deep learning is based on the concept of Artificial Neural Networks, ANN. Developers use TensorFlow to create many multiple layered neural networks. Artificial Neural Networks, ANN, is an attempt to mimic the human nervous system to a good extent by using silicon and wires. The intention behind this system is to help develop a system that can interpret and solve real-world problems like a human brain would. 

What makes TensorFlow popular?

  • It is free and open-sourced: TensorFlow is an Open-Source Software released under the Apache License. An Open Source Software, OSS, is a kind of computer software where the source code is released under a license that enables anyone to access it. This means that the users can use this software library for any purpose — distribute, study and modify — without actually having to worry about paying royalties.
  • When compared to other such Machine Learning Software Libraries — Microsoft’s CNTK, or Theano — TensorFlow is relatively easy to use. Thus, even new developers with no significant understanding of machine learning can now access a powerful software library instead of building their models from scratch.
  • Another factor that adds to its popularity is the fact that it is based on graph computation. Graph computation allows the programmer to visualize his/her development with the neural networks. This can be achieved through the use of Tensor Board. This comes in handy while debugging the program. The Tensor Board is an important feature of TensorFlow as it helps monitor the activities of TensorFlow– both visually and graphically. Also, the programmer is given an option to save the graph for a later use.  


All the computations associated with TensorFlow involves the use of tensors. This leads to an interesting question :

What are Tensors?

It is a vector/matrix of n-dimensions representing types of data. Values in a tensor hold identical data types with a known shape. This shape is the dimensionality of the matrix. A vector is a one-dimensional tensor; matrix a two-dimensional tensor. Obviously, a scalar is a zero dimensional tensor.

In the graph, computations are made possible through interconnections of tensors. The  mathematical operations are carried by the node of the tensor whereas the input-output relationships between nodes are explained by a tensor’s edge.

Thus TensorFlow takes an input in the form of an n-dimensional array/matrix (known as tensors) which flows through a system of several operations and comes out as output. Hence the name TensorFlow. A graph can be constructed to perform necessary operations at the output.


Below are listed a few of the use cases of TensorFlow:


  • Voice and speech recognition: The real challenge put before programmers was that a mere hearing of the words will not be enough. Since, words change meaning with context, a clear understanding of what the word represents with respect to the context is necessary. This is where deep learning plays a significant role. With the help of Artificial Neural Networks or ANNs, such an act has been made possible by performing word recognition, phoneme classification, etc.

Thus with the help of TensorFlow, artificial intelligence-enabled machines can now be trained to receive human voice as input, decipher and analyze it, and perform the necessary tasks. A number of applications makes use of this feature. They need this feature for voice search, automatic dictation, and more.

Let us take the case of Google’s search engine as an example. While you are using Google’s search engine, it applies machine learning using TensorFlow to predict the next word that you are about to type. Considering the fact that how accurate they often are, one can understand the level of sophistication and complexity involved in the process.

  • Image recognition: Apps that use the image recognition technology are probably the ones that popularized deep learning among the masses. The technology was developed with the intention to train and develop computers to see, identify, and analyze the world like how a human would.  Today, a number of applications finds these useful — the artificial intelligence enabled camera on your mobile phone, the social networking sites you visit, your telecom operators, to name a few.

In image recognition, Deep Learning trains the system to identify a certain image by exposing it to a number of images that are labelled manually. It is to be noted that the system learns to identify an image by learning from examples that are previously shown to it and not with the help of instructions saved in it on how to identify that particular image.

Take the case of Facebook’s image recognition system, DeepFace. It was trained in a similar way to identify human faces. When you tag someone in a photo that you have uploaded on Facebook, this technology is what that makes it possible. 

Another commendable development is in the field of Medical Science. Deep learning has made great progress in the field of healthcare — especially in the field of Ophthalmology and Digital Pathology. By developing a state of the art computer vision system, Google was able to develop computer-aided diagnostic screening that could detect certain medical conditions that would otherwise have required a diagnosis from an expert. Even with significant expertise in the area, considering the amount of tedious work one has to go through, chances are that the diagnosis vary from person to person. Also, in some cases, the condition might be too dormant to be detected by a medical practitioner. Such an occasion won’t arise here because the computer is designed to detect complex patterns that may not be visible to a human observer.    

TensorFlow is required for deep learning to efficiently use image recognition. The main advantage of using TensorFlow is that it helps to identify and categorize arbitrary objects within a larger image. This is also used for the purpose of identifying shapes for modelling purposes. 

  • Time series: The most common application of Time Series is in Recommendations. If you are someone using Facebook, YouTube, Netflix, or any other entertainment platform, then you may be familiar with this concept. For those who do not know, it is a list of videos or articles that the service provider believes suits you the best. TensorFlow Time Services algorithms are what they use to derive meaningful statistics from your history.

Another example is how PayPal uses the TensorFlow framework to detect fraud and offer secure transactions to its customers. PayPal has successfully been able to identify complex fraud patterns and have increased their fraud decline accuracy with the help of TensorFlow. The increased precision in identification has enabled the company to offer an enhanced experience to its customers. 

A Way Forward

With the help of TensorFlow, Machine Learning has already surpassed the heights that we once thought to be unattainable. There is hardly a domain in our life where a technology that is built with the help of this framework has no impact.

 From healthcare to entertainment industry, the applications of TensorFlow has widened the scope of artificial intelligence to every direction in order to enhance our experiences. Since TensorFlow is an Open-Source Software library, it is just a matter of time for new and innovative use cases to catch the headlines.

Your essential weekly guide to Artificial Intelligence – September Part II

Reading Time: 2 minutes

Artificial Intelligence is paving the way to the future, one breakthrough at a time. On the other hand, there are debates on the ‘pros and cons’ and the ‘perceived vs. actual intelligence’ of AI. Here are a few recent articles that highlight this notion. Read on to learn more.

A Breakthrough for A.I. Technology: Passing an 8th-Grade Science Test

The Allen Institute for Artificial Intelligence, a prominent lab in Seattle, unveiled a new system that passed the test with room to spare. It correctly answered more than 90 per cent of the questions on an eighth-grade science test and more than 80 per cent on a 12th-grade exam. The system, called Aristo, is an indication that in just the past several months researchers have made significant progress in developing A.I. that can understand languages and mimic the logic and decision-making of humans.

Artificial Intelligence Aid Fight Against Global Terrorism

Although terrorists have become skilled at manipulating the Internet and other new technologies, artificial intelligence or AI, is a powerful tool in the fight against them, a top UN counter-terrorism official said this week at a high-level conference on strengthening international cooperation against the scourge. Read more to know how AI technologies are being used to counter global terrorism.

AI is Not as Smart as You Think

‘Computers won’t cause the end of civilisation.’

Speaking at a recent artificial intelligence seminar, Dr Mariarosaria Taddeo, a research fellow at the Oxford Internet Institute, said AI will never think for itself. There is not a shred of proper research that supports the idea that AI can become sentient. This is a technology that behaves as if it were intelligent, but that is nothing to do with creating or deducing.

Top Highlights From The World Artificial Intelligence Conference

With the theme of “Intelligent Connectivity, Infinite Possibilities”, the World Artificial Intelligence Conference 2019 concluded recently in Shanghai. During the opening ceremony, Alibaba Group Chairman Jack Ma and Elon Musk, CEO of Tesla had a 45-minute debate on the impact of AI on human civilisation, future of work, consciousness and environment. The event saw innovative applications related to industrial ecology, AI urban application, autonomous driving and other cutting-edge technologies, and about 400 companies which participated.

How Artificial Intelligence is Creating Jobs in India, Not Just Stealing Them

There is a growing demand for data-labelling services that are “localised”- both linguistically and culturally relevant to India. From an opportunity point of view, there are about a lakh jobs posted on various portals currently. 

Happy Reading!


For more roundups on AI, watch this space!

If you are interested in upskilling with Artificial Intelligence, read more about Great Learning’s PG program in Artificial Intelligence and Machine Learning.

What is Machine Learning?

Reading Time: 7 minutes

The term Machine Learning was coined by Arthur Samuel in the year 1959. He was a pioneer in Artificial Intelligence and computer gaming, and defined Machine Learning as “Field of study that gives computers the capability to learn without being explicitly programmed”. In this article, we will explore in detail about machine learning.

Simply put, Machine Learning is the study of making machines more human-like in their behaviour and decisions by giving them the ability to learn and develop their own programs. This is done with minimum human intervention, i.e., no explicit programming. The learning process is automated and improved based on the experiences of the machines throughout the process. Good quality data is fed to the machines and different algorithms are used to build ML models to train the machines on this data. The choice of algorithm depends on the type of data at hand, and the type of activity that needs to be automated. 

Here’s a video by explaining what is Machine Learning from the ground up.

Now you may wonder, how is it different from traditional programming? Well, in traditional programming we would feed the input data and a well written and tested program into a machine to generate output. When it comes to machine learning, input data along with the output is fed into the machine during the learning phase, and it works out a program for itself. To understand this better, refer to the illustration below:

What is machine learning - difference between traditional programming and ML

Types of Machine Learning

In this section, we will learn about the different approaches towards machine learning and the type of problems they can solve. 

Supervised Learning

It is the widely used approach in a maximum of the Machine Learning applications. The supervised learning model has a set of input variables (x), and an output variable (y). An algorithm is used to identify the mapping function between the input and output variables. The relationship is y = f(x).

The learning is monitored or supervised in the sense that we already know the output and the algorithm are corrected each time to optimize its results. The algorithm is trained over the data set and corrected repeatedly until it achieves an acceptable level of performance.

Supervised learning problems can be further grouped as:

  1. Regression problems – Used to predict future values and the model is trained with the historical data. Eg: Predicting the future price of a product.
  2. Classification problems – The algorithm is trained with various labels to identify items within a specific category. Eg: Disease or no disease, Apple or an orange, Beer or wine.

Here’s a video that describes step by step guide to approaching a Machine Learning problem with a beer and wine example:


Unsupervised Learning:

This approach is the one where the output is unknown and we have only the input variable at hand. The algorithm is left to learn by itself and discover interesting structure in the data. 

The goal is to decipher the underlying distribution in the data with an aim to gain more knowledge about the data. 

Unsupervised learning problems can be further grouped as:

  1. Clustering: This is used to group the input variables with same characteristics together. Eg: grouping users based on search history
  2. Association: Here we discover the rules that govern meaningful associations among the data set. Eg: People who watch ‘X’ will also watch ‘Y’


Semi-supervised Learning:

Here the data is trained on a mix of a very small amount of labelled data and a large amount of unlabelled data. Usually, the first step is to cluster similar data with the help of an unsupervised machine learning algorithm. The next step is to label the unlabelled data using the characteristics of the limited labelled data available. Once the complete data set is labelled, the supervised learning algorithms can be used to solve the problem.


Reinforcement Learning:

In this approach, machine learning models are trained to make a series of decisions based on the rewards and feedback they receive for their actions. The machine learns to achieve a goal in complex and uncertain situations and is rewarded each time it achieves it during the learning period. 

Reinforcement learning is different from supervised learning in the sense that there is no answer available so the reinforcement agent decides the steps to perform a task. When the training data set is not present, the machine is bound to learn from its own experiences. 



Machine Learning Applications

Machine Learning algorithms help in building intelligent systems that can learn from their past experiences and historical data to give accurate results. Many industries are thus applying machine learning solutions to their business problems, or to create new and better products and services. Some of the applications of Machine Learning can be seen in healthcare, defence, financial services, marketing, and security services among others. Some of them are described below:

Facial recognition/Image recognition: The most common application of machine learning is Facial Recognition and the simplest example of this application is the iPhone X. There are a lot of use-cases of facial recognition, mostly for security purposes. It can be used to identify criminals, find missing individuals, aid forensic investigations, etc. Apart from this, it is being used in intelligent marketing, diagnose diseases, track attendance in schools, and more.

Automatic Speech Recognition: Abbreviated as ASR, automatic speech recognition is used to convert speech into digital text. Its applications lie in authenticating users based on their voice and performing tasks based on the human voice inputs. The system is trained by feeding speech patterns and vocabulary to train the model. Presently ASR systems find a wide variety of applications in the following domains:

– Medical Assistance

– Industrial Robotics

– Forensic and Law enforcement

– Defence & Aviation

– Telecommunications Industry

– Home Automation and Security Access Control

– I.T. and Consumer Electronics

Financial Services – Machine learning has many use cases in Financial Services. Machine Learning algorithms prove to be excellent at detecting frauds by monitoring activities of each user and assess that if an attempted activity is typical of that user or not.

Financial monitoring to detect money laundering activities is also an important security use case of machine learning.

Machine Learning also helps in making better trading decisions with the help of algorithms that can analyse thousands of data sources simultaneously. Other applications include, but are not limited to, underwriting and credit scoring.

The most common application that is witnessed in our day to day activities is the virtual personal assistants like Siri and Alexa.

Marketing and Sales – Machine Learning is improving lead scoring algorithms by including various parameters such as website visits, emails opened, downloads, and clicks to score each lead.

It also helps businesses to improve their dynamic pricing models by using regression techniques to make predictions. 

Sentiment Analysis is another important application to gauge consumer response to a specific product or a marketing initiative. 

Machine Learning for Computer Vision helps brands identify their products in images and videos online. This is used to measure the mentions that are posted without any relevant text. 

Chatbots are also becoming more responsive and intelligent with the help of machine learning.

Healthcare – An important application of Machine Learning is in the diagnosis of diseases and ailments which are otherwise difficult to diagnose. Radiotherapy is also becoming better with Machine Learning taking over. 

Early-stage drug discovery is another important application which involves technologies such as precision medicine and next-generation sequencing. 

Clinical trials cost a lot of time and money to complete and deliver results. Machine Learning based predictive analytics could be applied to improve on these factors and give better results. 

Machine Learning technologies are also critical to make outbreak predictions and are being used by scientists around the world to predict epidemic outbreaks. 

Recommendation Systems – Many businesses today use recommendation systems to effectively communicate with the users on their site. There is a lot of learning in the user behaviour data that can be applied to recommend relevant products, movies, web-series, songs, and much more. 

Most prominent use-cases of recommendation systems are e-commerce sites like Amazon, Flipkart, and many others along with Spotify, Netflix, and other web-streaming channels.


Machine Learning Jobs and Career prospects:

Before moving on to Machine Learning job roles and career prospects, let us have a look at the skill sets that are necessary to become a successful machine learning professional.

The prerequisites to learn Machine Learning are:

– Linear Algebra

– Statistics and Probability

– Calculus

– Graph theory

– Programming Skills – Python, R, MATLAB, C++ or Octave

Essential Machine Learning skills to become a successful ML professional are:

Machine Learning Algorithms and Libraries – There is an absolute need to be acquainted with the implementation of ML algorithms mostly available through APIs, Packages, and Libraries. It is also important to learn about the pros and cons of different applicable approaches towards ML implementation. 

Data Modelling and Evaluation – This includes the process of continuously evaluating the performance of the given model. This is achieved by selecting an appropriate accuracy measure and an effective evaluation strategy based on the problem at hand. 

Distributed Computing – Machine Learning jobs require to be working with a great set of data. This large amount of data cannot be processed using a single machine. It needs to be distributed across a cluster of machines. 

Software engineering and system design – A strong base in software engineering and system design is a requisite for a successful machine learning career. The ability to build appropriate interfaces for components is preferred by employers. These skills are valuable for improving quality, productivity, collaborations, and maintainability.     


Machine Learning Job Roles and salary trends: 

What is machine learning - machine-learning-job-roles


What is machine learning - machine-learning-salary-trends

(Source: Analytics India Magazine ‘Salary Study – 2018′)


The future scope of Machine Learning

It is estimated that Machine Learning market will grow to reach USD 8.81 billion by the year 2022. That means that there is going to be a substantial requirement of skills around Machine Learning to drive this growth. The future looks promising for those planning a career in Machine Learning!

If you want to know more about what is machine learning and are interested in pursuing a career in Machine Learning, check out Great Learning’s postgraduate program in Machine Learning. 

Auto-Keras: An Open Source Library For Automated Machine Learning!

Reading Time: 2 minutes

Overview :

Auto-Keras is an Open Source library for Automated Machine Learning.

Allows an easy access to deep learning models to the domain experts with limited machine learning background.

Multiple examples make it simple to understand the model.

Introduction :

Automated Machine Learning or AutoML is the process of automating the end-to-end process of applying machine learning to real-life problems. Auto-Keras is an open-source software library for AutoML, a new member of the ‘open-source libraries’ family. The main aim is to provide domain experts with ML knowledge and easy access to deep learning models.

Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models. What makes it different is the ease of use, simple installation and the numerous examples to get started with. As it is an open-source library, one can easily study, change and enhance the source code as well!

The first and the most important step to start-off with Auto-Keras is installation. To install the package, use the pip installation as shown on their website.

For those having second thoughts (if any), there is a gamut of evidence on how effective AutoML is. AutoML was proposed as an AI-based solution to the ever-growing challenge of applying machine learning and the results speak for themselves. Automating the end-to-end process of applying machine learning results in simpler solutions, creating those solutions faster, and developing models that often outperform models that were designed manually. Having said that the current version of Auto-Keras comes with a disclaimer which you can read here.

Follow the links to go through the code on GitHub and a few examples as well.

So Let’s Summarize :

Click on the link, install and give it a try! There is nothing to lose irrespective of whether you wish to learn or put this software to a test. This will make your deep learning models easier to build and hopefully perform better. As Automated Machine Learning is gaining attention swiftly, an open-source opportunity like this definitely gains a lot of popularity in the community.

So what are you waiting for? Try Auto-Keras today!

For more information on machine learning concepts and tools, visit the Great Learning Blog


Is Deep Learning Better Than Machine Learning?

Reading Time: 3 minutes

Overview :

What is deep learning?

When, where and why is deep learning used.

Is deep learning better than conventional machine learning?

Which came first? The chicken or the egg?

Centuries have passed and we haven’t been able to answer this question. But soon, maybe a machine will! Can it? Will it? Let’s figure out!

Introduction :

What is deep learning? How is it related to machine learning? Is it better than conventional machine learning? A lot of questions at once, isn’t it? Deep learning is a part of the machine learning family which is based on the concept of evolutionary algorithms. It basically mimics biological processes like evolution.

So what exactly is machine learning?

Machine learning is a subset of AI that uses statistical strategies to make a machine learn without being programmed explicitly using the existing set of data. It evolved from the study of pattern recognition in AI. For a detailed understanding of machine learning, you can watch this video-

Deep Learning:

Conventional machine learning methods tend to succumb to environmental changes whereas deep learning adapts to these changes by constant feedback and improve the model. Deep learning is facilitated by neural networks which mimic the neurons in the human brain and embeds multiple-layer architecture (few visible and few hidden). It is an advanced form of machine learning which collects data, learns from it, and optimises the model. Often some problems are so complex, that it is practically impossible for the human brain to comprehend it, and hence programming it is a far fetched thought. Primitive forms of Siri and Google assistant are an appropriate example of programmed machine learning as they are found effective in their programmed spectrum. Whereas, Google’s deep mind is a great example of deep learning. Essentially, deep learning means a machine which learns by itself by multiple trial and error methods. Often a few hundred million times!

A simple understanding of basic deep learning concepts can be grasped from this video on neural networks:

Now that was pretty impressive, right?

Let us think of writing a program which differentiates between an apple and an orange. Although it may sound like a simple task to accomplish, it is indeed a complex one as we cannot program a machine to know the difference merely by observing it. We as humans can, machines can’t! So if we were to program, we would mention a few specifications of the apple and the orange but it would work for simple and clear images like these.

deep learning vs machine learning

But what if we place a banana?

The machine would probably be befuddled! This is where deep learning comes into the picture. A conventional machine learning method helps a machine to efficiently perform only a predetermined set of instructions and tends to become unworthy in case new variables are introduced in the system. It can be understood better with this video: 

Deep learning helps a machine to constantly cope with the surroundings and make adaptable changes. This ensures versatility of operation. To elaborate, deep learning enables a machine to efficiently analyse problems through its hidden layer architecture which are otherwise far more complex to be programmed manually. So, deep learning gets an upper hand when handling colossal volumes of unstructured data as it does not require any labels to handle the data.

So Let’s Summarise :

Deep learning is an advanced form of machine learning which comes in handy when the data to be dealt with is unstructured and colossal. Thus, deep learning can cater to a larger cap of problems with greater ease and efficiency. Technological breakthroughs like Google’s Deepmind is the epitome of the heights that current AI can reach, facilitated by deep learning and neurological networks.

So maybe we can’t predict which came first, the chicken or the egg but will AI be able to? Stick around to find out!

Machine learning and deep learning can be daunting and difficult to learn by yourself. A gamut of online free courses have come forward to make things simpler but if you want to take up a rigorous well-respected course that employers will respect then the Post Graduate Program in Machine Learning by Great Learning which offers 130 hours of content and personalised mentorship in an extremely easy to grasp manner is an excellent choice.

Linear Regression for Beginners – Machine Learning

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Before learning about linear regression, let us get ourselves accustomed to regression. Regression is a method of modelling a target value based on independent predictors. This method is mostly used for forecasting and finding out cause and effect relationship between variables. Regression techniques mostly differ based on the number of independent variables and the type of relationship between the independent and dependent variables.

Linear Regression for Beginners

Simple linear regression is a type of regression analysis where there is just one independent variable and there is a linear relationship between the independent(x) and dependent(y) variable. The red line in the above graph is referred to as the best fit straight line. Based on the given data points, we try to plot a line that models the points the best.

The line can be modelled based on the linear equation shown below.

Y= β0 + β1x + €

Isn’t Linear Regression from Statistics?

Before we dive into the details of linear regression, you may be asking yourself why we are looking at this algorithm.

Isn’t it a technique from statistics?

Machine learning, more specifically the field of predictive modelling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability. In applied machine learning, we will borrow and reuse algorithms from many different fields, including statistics and use them towards these ends.

As such, linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but has been borrowed by machine learning. It is both a statistical algorithm and a machine learning algorithm.

Linear Regression Model Representation

Linear regression is an attractive model because the representation is so simple.

The representation is a linear equation that combines a specific set of input values (x) the solution to which is the predicted output for that set of input values (y). As such, both the input values (x) and the output value are numeric.

The linear equation assigns one scale factor to each input value or column, called a coefficient and represented by the capital Greek letter Beta (B). One additional coefficient is also added, giving the line an additional degree of freedom (e.g. moving up and down on a two-dimensional plot) and is often called the intercept or the bias coefficient.

For example, in a simple regression problem (a single x and a single y), the form of the model would be:

Y= β0 + β1x

In higher dimensions when we have more than one input (x), the line is called a plane or a hyper-plane. The representation, therefore, is in the form of the equation and the specific values used for the coefficients (e.g. β0and β1 in the above example).

Linear Regression – Learning the Model

  1. Simple Linear Regression

With simple linear regression when we have a single input, we can use statistics to estimate the coefficients.

This requires that you calculate statistical properties from the data such as mean, standard deviation, correlation, and covariance. All of the data must be available to traverse and calculate statistics.

  1. Ordinary Least Squares

When we have more than one input we can use Ordinary Least Squares to estimate the values of the coefficients.

The Ordinary Least Squares procedure seeks to minimize the sum of the squared residuals. This means that given a regression line through the data we calculate the distance from each data point to the regression line, square it, and sum all of the squared errors together. This is the quantity that ordinary least squares seek to minimize.

  1. Gradient Descent

This operation is called Gradient Descent and works by starting with random values for each coefficient. The sum of the squared errors is calculated for each pair of input and output values. A learning rate is used as a scale factor and the coefficients are updated in the direction towards minimizing the error. The process is repeated until a minimum sum squared error is achieved or no further improvement is possible.

When using this method, you must select a learning rate (alpha) parameter that determines the size of the improvement step to take on each iteration of the procedure.

  1. Regularization

There are extensions to the training of the linear model called regularization methods. These seek to both minimize the sum of the squared error of the model on the training data (using ordinary least squares) but also to reduce the complexity of the model (like the number or absolute size of the sum of all coefficients in the model).

Two popular examples of regularization procedures for linear regression are:

– Lasso Regression: where Ordinary Least Squares is modified to also minimize the absolute sum of the coefficients (called L1 regularization).

– Ridge Regression: where Ordinary Least Squares is modified to also minimize the squared absolute sum of the coefficients (called L2 regularization).

Preparing Data for Linear Regression

Linear regression has been studied at great length, and there is a lot of literature on how your data must be structured to make the best use of the model.  In practice, you can use these rules more like rules of thumb when using Ordinary Least Squares Regression, the most common implementation of linear regression.

Try different preparations of your data using these heuristics and see what works best for your problem.

– Linear Assumption

– Noise Removal

– Remove Collinearity

– Gaussian Distributions

– Rescale Inputs



In this post, you discovered the linear regression algorithm for machine learning.

You covered a lot of ground including:

– The common names used when describing linear regression models.

– The representation used by the model.

– Learning algorithms used to estimate the coefficients in the model.

– Rules of thumb to consider when preparing data for use with linear regression.


Try out linear regression and get comfortable with it. If you are planning a career in Machine Learning, here are some Must-Haves On Your Resume and most common interview questions to prepare.