Artificial intelligence (AI) and machine learning (ML) are the most trending fields nowadays, machine l

artificial intelligence

earning helps machine to learn with the help of data provided and can only do specific task. In AI system is intelligent and can input  multiple data.

Just check it out:

We have another blog related to artificial intelligence in real business world.

Relationship between Artificial intelligence and Machine Learning :

Machine learning is used in AI. ML is a method through which we can feed a lot of data to a machine so that it can make its own decision.

AI is a vast field, in AI we have ML, NLP, image recognition, deep learning etc.

In AI, you try to make machines behave like human beings.

Which is better, AI or ML ?

AI makes machine to think and behave like human while ML provide data to the machine for accurate output. ML comes under AI, Both AI and ML are best in doing their work efficiently.


AI makes machine seem they have human intelligence, as it is a broad area of computer science. The term AI was first  coined back in 1956 by Dartmouth professor John McCarthy. He called a group of scientist and mathematicians to see if a machine could learn like a child does.

What is the history of AI ?

  • Artificial intelligence was first termed  in 1956 at Dartmouth College.
  • First Turing test  was taken in 1950.
  • In 1957, the first chess program was written by Alex Bernstein.
  • Unimate was the first robot used in the 1960s.
  • The first chess playing computer  (Deep blue) defeated the world chess champion in 1997.

What are domains of AI ?

  • Deep learning 

Igor Eisenberg in 2000 coined the term deep learning while there was a discussion of artificial neural networks.

Deep learning algorithms are inspired by function and structure of human brain, it is a subfield of machine learning that uses complex algorithms to train a model.


  • Machine learning

ML is an application of AI that allows a system to automatically learn and improve from experience. ML is mainly concerned with accuracy and patterns, deals with structured and semi-structured data.

  • Computer vision 

Computer vision helps computer learn and understand images and videos using digital images from cameras and videos and deep learning models, machine can accurately identify and classify objects.

  • Image processing

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful  information from it.

  • Natural language processing (NLP)

NLP is a branch of AI that gives a machine the ability to read, understand and derive meaning from human language. NLP refers to the artificial intelligence method of communicating with an intelligent system using natural language.

What are types of AI ?

There are three stages of AI :

  • Artificial narrow intelligence (weak AI)

Artificial narrow intelligence, also known as weak AI, can only perform tasks that are assigned to it. Currently are on the level of narrow intelligence. 

Alexa is  the best example of narrow intelligence.  

  • Artificial general intelligence (general ARTIFICIAL INTELLIGENCE)

Artificial general intelligence is a strong AI, machines that can perform any task that a human can.

Machines have strong processing units, but they are not capable of thinking like human beings.

  • Artificial superintelligence (strong Artificial Intelligence)

Artificial superintelligence is a term used for when machines will surpass human intelligence and can perform tasks that any human being can not.

This is yet fictional and can only be seen in movies and books.


  • Banks use AI to organize operation, invest in stocks etc.
  • AI has been applies to video games, like bots designed to stand in opponent where human aren’t available.
  • Medical clinic use AI to organize bed schedule, make staff rotation, provide medical information etc.
  • Customer support using chatbots, Siri, humanoid robot etc.


Artificial intelligence(AI) approaches and concept less than a decade. AI is the branch of computer science that aims to answer turnings question affirmative. It is the endeavor to simulate human intelligence in machines.

When people think AI, they often think big such as curing cancer or solving climatic change  everybody is dreaming up the biggest problem possible and attempting to solve them with AI. JUST 20% of surveyed executives use AI related technologies in their business.

With the right business case and the right data, AI can deliver powerful time & cost savings as well as valuable insights you can use to improve your business.

For more information regarding this topic check out our previous blog artificial intelligence in real; business world


Machine learning is a branch of AI. ML was termed by Arthur Samuel in 1959. ML helps machine to learn automatically  it does not need to be programmed regularly. ML allows machine to learn from data so that they can give accurate output.

Types of machine learning 

  • Supervised learning

In this learning machine works under the supervision. It provides output with the help of labeled data. If I show you an image of  a dog and tell you it’s a dog it is labeled data. It has feedback mechanism.

  • Unsupervised learning

In this machine have to work on data which is not labeled, machine identifies pattern and try to give response accordingly. Feedback mechanism is not there.

  • Reinforcement learning

It uses agent and environment observation action reward. It is a machine training method based on rewarding desired behavior or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

Why is Machine Learning is important ?

  • Increase in data

Tons of data is produced daily, ML is used to organize that data efficiently.

  • Solve complex problems 

ML can solve complex mathematical problems easily with the help of mathematical algorithms.

  • Improves decision-making 

Helps make faster and accurate decision based on data sheets provided.

Algorithms of Machine learning:-

Linear regression

linear regression is a linear modelling approach to find the relationship between one or more independent variables (predictors) denoted as X and dependent variable (target) denoted as Y.

Logistic regression

Logistic regression is used for a different class of problems, known as classification problems. It measures the relationship between a dependent variable and one or more independent variables using a logistic function.

Decision tree

It is a tree shaped diagram used to determine a course of action. Each  branch of tree represents a possible decision, occurrence or reaction.

Math in ML ?

Mathematics used in machine learning are statistics, probability, calculus, linear algebra and mathematical algorithms.

Application of ML ? 

  1. Image recognition
  2. automatic language translator
  3. virtual personal assistant
  4. product recommendation
  5. Email spam and malware filtering


 Latest technologies are reshaping our daily lives. We can clearly see that AI will play a crucial role in business transformation in the coming time.

Let’s see some ways How Artificial intelligence will transform business in 2021.


It is a microprocessor chip that will only allow AI-Powered apps. It will be used in improving the sophisticated software used in the games, healthcare, industrial and finance.


Nowadays, we can see that the availability of data resulted in the increase of cyberattacks. Companies are making investment to improve the cybersecurity networks. AI plays a crucial role in making corporate cybersecurity. Its costs will also be reduced as a result of the technology.


For voice search you can easily see the examples of Amazon and google, they have already dominated the market for smart, voice-controlled home goods. Apple has entered the competition by their own line of smart speakers. Many companies will develop apps to support this voice-based technology in the future.


Many companies utilize AI-powered chatbots in corporate communication in today’s digital driven environment. They are used to boosting client interaction. Chatbots powered by AI reduces the need for human interaction.

Why Artificial Intelligence and Machine Learning?

With your goals in mind (i.e., why) in mind, the next step of any practical engineering or machine learning solution is to determine (e.g., what algorithms or models should be used) to achieve a particular goal or set of goals, and finally. what the outcome will be (e.g., product, report, forecast model).

Interested in learning about artificial intelligence (AI) and machine learning (ML)? Have you ever wondered how these amazing fields can help you or your business?

Technological innovation helps individuals and businesses achieve important goals, access potential information, drive critical decisions, and create exciting, innovative, and innovative products and services.

Author Simon Sinek, in his influential book, Start With Why, does an excellent job of explaining why it should be the most important driving force in almost everything.

The golden circle

This also applies to Artificial Intelligence and Mechanical Learning, so understanding and explaining why these sites should be used for a given need is important, and how they are to be used (e.g., processes, algorithms, data. Scientists), and finally what is produced for of that (e.g., product, service, recommendation engine, smart assistant).

These fields benefit businesses and customers alike, although each has different goals. These objectives should be considered as to why that drives any artificial intelligence or machine learning solution.

Business goals include things like increasing revenue and profit, reducing costs, improving efficiency, and more. Businesses are also very interested in increasing customer acquisition, retention, and growth.

Customers, on the other hand, have goals such as getting some work done (e.g., JTBD framework), such as communicating with friends and family through social media, getting recommendations on movies you can watch or purchase, better planning, and increasing productivity. People also want to use well-designed products that provide in-depth user experience, that is, fun, easy to use, and easy to understand.

With these goals (i.e., why) in mind, the next step of any practical skill or machine learning solution is to determine (e.g., what algorithms or models should be used) to achieve a particular goal or set of goals, and finally. what the outcome will be (e.g., product, report, forecast model).

These days there are so many amazing, real-world applications of machine learning and technology that are being used to benefit customers and companies. Other categories of application include:

Predictability and categories

Recommendation systems


Computer view

Confusing integration and discovery

Indigenous language (NLP, NLG, NLU)

Hybrid & Hybrid (eg, autonomous vehicles, robots, IoT)

To learn more about machine learning and learning skills, including driving principles, definitions, types, algorithms, processes involved, important and thought-provoking trading, and examples of real-world applications for each category, check out My Tactical Performance and Machine Learning class at Skillshare!

Get two months of premium membership on Skillshare, with full access to my class, and thousands of other classes covering many different topics using this link:

Cheers, and enjoy!

Alex Castrounis is the founder and CEO of Why of AI and author of AI for People and Business. He is also an appendix to Northwestern University’s Kellogg / McCormick MBAi program.

Why Artificial Intelligence is Important – Why AI?

“It’s hard to imagine a big industry that AI could not change. These include health care, education, transportation, retail, communications, and agriculture. There are surprisingly clear ways for AI to make a big difference in all these industries. ”

– Andrew Ng, Computer Scientist and Global Leader in AI

With the growing demand for AI in recent times, businesses are at a loss as to whether it is worthwhile to get on an AI train, invest in it, or just be a bomb waiting to explode.

Everyone has questions like “why is practical wisdom important? or why do we need artificial intelligence? ”

Business and career in AI were accelerated at an astonishing rate and that, while progress in certain areas such as playing tied games and vision has been very positive. The term can also be applied to any machine that displays features related to the human mind such as learning and solving problems.

AI resides far and wide when everything is said in the information functions created that will deliver, that is, complete computer performance in excess of a variety of limited functions.

In this article, we will understand why it is important for businesses to invest in AI technology now, more than ever before.

Why is practical wisdom important?

First, let’s learn the reasons why AI is important in today’s world.

1. Edge of Competition

Most importantly, companies that aim to have a competitive edge over their competitors are turning to AI technology to achieve this.

Moving out of business infrastructure, AI has the potential to provide companies with competitive marketing because of its ability to acquire and train based data.

AI provides a lot of information on how to market, who to connect with, when and where, and why it should be connected.

For example, the Autopilot feature offered by Tesla in its cars. Tesla uses Deep Learning Algorithms to achieve automatic driving. This was previously a feature of many, but now we define genre.

2. Accessibility

Basic speed, accessibility, and overall scale have empowered bold statistics to deal with growing problems.

Not only is the device fast, here and there it comes up with certain types of processors (e.g., GPUs), accessible equally it looks like cloud management.

What used to be in some labs with access to super PCs will now be able to transfer to the cloud at a lower cost and more without any hassle.

This has achieved democracy in the categories of equipment necessary to drive AI, enabling duplication of new companies.

3. Fear of Missing (FOMO)

Yes, you read that well. Not only us, companies also feel the fear of missing out. If they do not want to get out of the market in the future, they have to adapt. They need to invest in technology that can disrupt their industries.

Several companies and startups in the housing industry have begun to use AI technology and Machine Learning to make better decisions and improve productivity.

Take the example of the banking sector, where almost all banks have invested heavily in chatbots so as not to miss the next wave of disruptions.

4. Cost Performance

With each passing day, the technology behind AI is becoming less expensive to use and although it is more advanced than hand-made suggestions, it is much cheaper than before.

AI will make it possible to avoid past design mistakes and perform completely new long-term experiments. The result is a more precise and well-designed protocol that may require minor changes, which may save many dollars on the costs previously incurred in the change applications.

This has resulted in many businesses that could not have used it in the past to use this technology.

5. Proof of the future

One thing we should all understand is that AI is the next big thing.

Businesses can and should ensure that they are proof of the future through AI technology. If that is the way the world is heading, why not make a conscious effort to move in that direction and adapt to that change?

In the future of AI-enabled, people will be ready to discuss and interact in the language of their choice, without having to stress about the intentions of non-communication.

Machine learning models will be ready to understand context, irritability, and colloquialisms that help fill human communication gaps.

This will help to gradually shift staff to Artificially Intelligent systems. It can also help to save some time as a backup in case something goes wrong.


As we have discussed how AI as a technology has a huge benefit for businesses to invest in it. Empowering businesses to transform themselves into smart businesses, AI is the light that guides them to a better future.

In conclusion, the question has not always been why but when. And the answer is today because:

“The future now.”

7 Reasons Why You Should Learn Performance

1. Artificial Intelligence in the healthcare industry:

We are now in the digital age where everything can be used

with the help of technology and the internet. Today we see that a

the doctor can monitor and diagnose the patient from a distance. This

reduce the need to be personal. Picture the same way there

the patient’s health status is assessed against the previously prescribed medications

and algorithms that explain the solution to the doctor. This could be a

great success in the entire healthcare industry. Current health care

the industry relies entirely on medical knowledge only and not

a supportive decision-making system is available to advise treatment

or medications. It comes entirely from the Doctor

experience and decision.

Imagine a situation in which all important patient information and health records are pre-evaluated and a personalized treatment plan is developed for the doctor to review and change the entire treatment process.

2. Artificial Intelligence in reply to your emails:

If you were using the latest Gmail mobile app then

answering your emails would be really easy and fun.

So based on the content of your email, the pre-defined response is already there

previously included as your tags when replying to an email. I

The latest version of the Gmail mobile app has greatly slowed you down

time to change about email response. So mobile

applications check emails now and give us the right one

suggestions while writing back to the sender. However, the possibilities

they are infinite and most importantly they are endless. So we have to wait

in the future and see how it will affect human intervention. I

The above list is a general overview of Artificial Intelligence

is already taking its childish steps and developing current processes.

Yes, the limitations are endless and one needs to understand what they are

to a lesser extent. Involvement and reconstruction of the process by

using Artificial Intelligence and Machine learning will be

it is definitely the future and it makes sense to build capacity in this

court. Many opportunities are available when used

they are not specific to the industry but this can be done generally.

3. Artificial Intelligence in the Mobile World:

The smartphone today is not considered just a communication

the device can now be called your digital wallet and much more

instead, we can classify them as your assistants. Well,

talking about personal assistants, it is worth mentioning about “Siri”.

It is one of the best examples of Artificial Invention

Intelligence and machine learning. So based on your habits and interests

“Siri” will be able to answer all your questions and give you the essentials

suggestions. This has already happened and this is the beginning of the next

a wave of technological innovation. We’ve seen days when mobile devices

we have no touch screens and now we are in the digital age there

most devices are touch screens. The next few cell phones will be

use voicemail commands other than “Siri”. This

the change will be profound and will completely change the way people are

using their cell phones right now.

4. Artificial Intelligence on Smart Home Devices:

Based on your preferences what if your home environment changes

from time to time. Wondering if this is happening or not? Well then

it is definitely possible. Over the past few years, we have seen a lot of intelligence

devices from the market that work in line with ours

favorites. So basically based on your favorite patterns, light

indoors and refrigerator temperatures and in other homes

devices can be rented and ultimately be much better

reuse settings. All of this is happening because of

basic machine learning and Intelligence built into these devices.

5. Artificial Intelligence in the Automotive Industry:

If you are updated with the latest technology events then you too

I will never forget this. The concept of self-driving cars as well

autopilot features are in the news recently and major players like “Google”

and “Tesla” are already in this stadium. Have you ever thought that you

you will travel in a car that does not require a driver to remove you from it

point A to point B. Well, this is by no means a dream, much more

the previous tests were mind-boggling vehicles that would hit the road

soon. This will definitely be the future of the car

industry. Further research and development needs to take place internally

in this area as we should consider the safety and security of

passengers. Well, we just have to wait and see what happens

it happened.

6. Artificial Intelligence in Music Recommendation Services:

Who does not enjoy watching movies and listening to music?

What if your next song or movie is recommended for you with a system based on your interests and browsing history? This would be great

ok !!! Well, they are already a few mobile apps that understand you

a selection of music and movies and recommend the same genre as a suggestion. This has been a great success in terms of sales and promotion of various products because the target market is available for brands. The ads you see in your browsers are also based on your previous activity. All your activities are reviewed and a series of

recommendations provided. With the help of recommendations, it

it will definitely help individuals to explore new options.

7. Artificial Intelligence in the retail industry:

This is going to be a big change for every retail company because if they understand the purchase pattern and requirements of their customers, will definitely have to adjust their process to be

market leader.


In the near future, the goal of maintaining the impact of AI for the benefit of society promotes research in a wide range of areas, from economics and law to technical topics such as validation, relevance, security and control. While it may be a small inconvenience if your laptop computer crashes or hits, it is very important that the AI ​​system does what you want it to do when it controls your car, your plane, your pacemaker, your automated trading. your power system or grid. Another temporary challenge is to prevent a destructive weapon race with deadly independent weapons.

Over time, an important question is what will happen if a strong AI search is successful and the AI ​​system is better than humans in all mental functions. As shown by I.J. Well in 1965, designing smart AI systems alone is a work of understanding. Such a system may have repeated self-improvement, causing an explosion of intelligence that leaves human ingenuity far away. By introducing new transformational technologies, such ingenuity can help us eradicate war, disease, and poverty, so the creation of robust AI could be a major event in human history. Some experts have expressed concern, however, that it may be the last resort, unless we learn to align AI goals with ours before they become too clever.

There are those who question whether solid AI will ever be achieved, while others insist that the creation of intelligent AI is guaranteed to be beneficial. At FLI we see both of these possibilities, but we also see the potential for artificial intelligence systems to cause significant damage intentionally or unintentionally. We believe that today’s research will help us better prepare for and prevent possible future consequences, thus enjoying the benefits of AI while avoiding pitfalls.

Link to: AI Existential Safety Community

AI Existential Safety Community

We believe that today’s research will help us better prepare for and prevent possible future consequences, thus enjoying the benefits of AI while avoiding pitfalls. Click here to view our growing community of existing AI security researchers.


Most researchers agree that the most intelligent AI is unlikely to portray human feelings as love or hate, and that there is no reason to expect AI to be deliberately kind or cruel. Instead, when considering how dangerous AI can be, experts think there are two possible scenarios:

AI is designed to do something harmful: Independent weapons are artificial intelligence systems designed to kill. In the hands of the wrong person, these weapons can easily kill many people. In addition, the AI ​​weapons race can lead to an AI war unknowingly and result in many casualties. To avoid being overrun by the enemy, these weapons would be extremely designed to “extinguish,” so that humans would not be able to control the situation. These dangers exist even if they exist with minimal AI, but they increase as the levels of AI intelligence and independence grow.

AI is designed to do something useful, but it develops a harmful way of achieving its goal: This can happen whenever we fail to fully align our AI goals with ours, which is surprisingly difficult. If you ask for an intelligent car to take you to the airport as soon as possible, it may take you a long time before you are chased by helicopters and full of vomit, not doing what you want but actually doing what you asked for. If a highly intelligent system is given the dominant function of geoengineering, it could cause damage to our ecosystem as a side effect, and view human efforts to stop it as a threat that must be met.

Link to: Lethal Autonomous Weapons Systems

Example: Deadly self-contained weapons

Slaughterbots, also known as “deadly weapons systems” or “deadly robots”, are weapons systems that use artificial intelligence (AI) to identify, select, and kill people without human intervention.

This technology already exists – and poses significant risks. Learn more about deadly deadly weapons, and what we can do to protect ourselves, here.

As these examples show, the concern for advanced AI is not the aggression but the ability. The smartest AI will be the best at achieving its goals, and if those goals don’t match ours, we have a problem. You’re probably not a bad ant ant that tramples ants for evil, but if you own a green energy project and there is anthill in the region that needs to be filled, it is very bad for the ants. The ultimate goal of AI security research is to never replace humanity with those ants.


Stephen Hawking, Elon Musk, Steve Wozniak, Bill Gates, and many other big names in science and technology recently expressed concern in the media and in open books about the dangers posed by AI, compiled by many leading AI researchers. Why is the topic in the headlines?

The idea that the search for solid AI would ultimately be successful has long been regarded as a science fiction, for centuries or more. However, thanks to recent successes, many AI scenarios, which experts considered to be more than a decade in the last five years, have now been achieved, making many experts take seriously the possibility of greater ingenuity in our lives. Although some experts still speculate that human AI will continue for centuries, many AI studies at the 2015 Puerto Rico Conference predict that it will happen before 2060. Since it can take decades to complete the required safety research, it is wise to start now. .

Because AI has the potential to be smarter than anyone else, we do not have a precise way of predicting how it will behave. We cannot use the past technological advances as the main basis because we have never created anything capable of, knowingly or unknowingly, beyond intellect.

What is machine learning? How Can Machines Learn? Machines do not have a learning brain! Or is it?

Does your mind wander when you hear such words? All right! After reading this article – why machine reading is so popular, you can also use such jargon in front of your friends!

Machine learning is the use of artificial intelligence (AI). The program provided by ML has the ability to automatically learn and improve from previous experiences. Therefore, they can do it without explicit planning. It focuses on the development of computer programs that can access data and use it for self-study.

In simple terms, this field of computer science gives the computer the ability to read without precise programming. It provides algorithms that can be trained to do the job.

Reasons why machine learning is popular

Modern challenges are “high-magnitude” in nature.

With rich data sources, it is important to build problem-solving models in a high-density environment.

With it, models can be integrated with active software. Supports the types of products demanded by the industry.

Also, Google Trends, which tracks the popularity of search terms, suggests that machine learning search will outpace artificial intelligence. Machine learning goes beyond textbooks and creates distractions that will change the future.

Now, let’s read in detail – why machine learning gets thunderstorm –

1. Filter large and informal data

More information is available today thanks to IoT. It is not possible to manage all information or data from emails, forums, blogs, podcasts or any other source.

Also, in order to maintain that information in an orderly manner it is necessary to keep up with the trend and get a competitive edge.

In the event of errors such as a lack of useful content the business may lose assets. No one knows where this idea came from and it hits you.

For example: Jennifer Lopez’s Grammys green dress inspired Google to come up with an image search feature.

For advertisers, the pressure to find and track better content is very real. However, electronic learning methods are a savior for them. It helps them provide the tools to find and recommend the most relevant content to overcome the flood of information.

What are the sources of this Data?

This happens because of the digital foot (This has nothing to do with carbon footprint, as long as you think so).

Before we talk about this, we can thank the Government for including digital and Jio for mobile data.

With so much data use the two types of feet are released.

Passive digital steps

Footprint is performed when information is collected from a user without the person knowing that this is a common occurrence.

A fun digital track is where a user intentionally shares information about him or her through social media or through websites. It is collected without the owner knowing (also known as a data exhaust) that data about him or her is being collected.

This type of footprint is stored on an online site such as “hit”. Tracks the user’s IP address. Thus, it captures the date and time it was created and where the data came from. This stamp can be stored in files, which can be accessed by administrators.

It helps to look at actions performed on a machine, without seeing who did them.

Active digital measures

Effective digital measures are created when personal data is intentionally released which means you know that his or her actions are being recorded. This is done for the purpose of sharing information about you through websites or forums.

Machine learning is smart and very easy for some groups to gather all the information and come to a conclusion.

More information can be gathered about that person using simple search engines.

An example of idle digital steps would be when a user was online and the information was stored on a web site.

2. Extensive data support in recommendations

“We now have rich data resources for building problem-solving models in a high-density environment.”

We all watch YouTube (Netflix, Hotstar or Television) for that matter.

During my childhood, I used to think that TV and I had the same passion for all my favorite programs broadcast on it. Little did I know that the data was the reason behind it. With the proliferation of data, people like and dislike were all kept in mind before the director thought of doing a show. There is a lot of data right now, as well as data that is collected and stored.

“Fullness of information” is possible and quality is something everyone looks for.

Lots of information that sends us spam on a daily basis, from emails, forums, blogs, podcasts (and endless listings). It is impossible to match completely. But, that is not the case.

Now, there will be no more worries about the lack of useful content and the pressure to find and track the best content available.

With Electronic Learning Methods the tools to find and recommend the most relevant content are available.

So now you can overcome the flood of information, sit in the back seat because everything is organized (I’m just talking about your data: P).

3. Quantified Self?

In the era of smart watches and Fitbits, Casio can’t survive (Because it doesn’t ask Casi-ho? #Pun Intended, but read it twice to understand the meaning). With quantified self tracking your life is possible. Your daily data is being collected.

Your daily knowledge such as starting with biological information such as heartbeat (Wow!), Breathing, steps, to interactions such as conversations and words spoken by you (Mind blown: O) is taken from your record.

4 Important Reasons Why You Should Study Machine Learning Now

Machine learning immerses itself in the music of our daily lives – even without our realizing it. Machine learning algorithms have been empowering the world around us, and this includes product recommendations at Walmart, fraudulent discovery of top-level financial institutions, rising prices on Uber, and content used by LinkedIn, Facebook, Instagram, and -Twitter to users. ‘feeds, and these are just a few examples, based directly on the daily lives we live.

That being said, it does not mean that the future is already here – and machine learning plays a vital role in the way our modern thinking perceives it. For example, Mark Cuban said: “Practical wisdom, deep learning, machine learning – anything you do if you don’t understand it – learn. Because otherwise you will become a dinosaur within 3 years. ”

Such is the urgency of machine learning. And if you want to take your work to the next level, it is a good tool to set the stage. Whether you are wondering how to get started with machine learning or the best way to learn machine learning, look no further than this blog!

What are the benefits of a machine learning course?

Better Career and Growth Opportunities

The TMR report notes that MLaS (Machine Learning as a Service) is projected to grow from $ 19.9 billion by the end of 2025, from $ 1.07 billion in 2016. This is an amazing amount of growth, both in terms of absolute terms, as well as year after year.

Machine learning makes fun of anything that can be called “important” – both financially and globally. If you are looking to take your work to another level, Machine Learning can do that for you. If you are looking for involvement in something that will make you a part of something global and modern-related, Machine Learning can do the same for you.

Machine learning covers an important foundation for a variety of perspectives – including image recognition, medications, online security, facial recognition, and more. As a growing number of businesses realize that business intelligence is strongly influenced by machine learning, so they choose to invest in it.

Netflix, to take just one example, has announced a $ 1 million prize for the first person to hone its ML algorithm by increasing its accuracy by 10%. This is conclusive evidence that even small improvements in ML algorithms have significant benefits for the companies that use them, and thus, the people who follow them. And with ML, you can be one of them!

Better Earnings

Leading mechanical engineers these days are paid just as much as the most famous sports personalities! And that is no exaggeration! According to, the average salary for a machine learning engineer is 8 lakhs a year – and that is just the beginning of human activity! An experienced machine learning engineer goes home anywhere between 15 to 23 lakhs a year.

If you’ve ever wondered who can learn machine learning, the answer is – you can! And if you’ve ever wondered where you can learn machine learning, here’s your answer: upGrad offers machine learning and AI, and teaches you, among other things, NLP, In-Depth Learning, Enhanced Learning and Graphical models. In addition, it also gives you a solid foundation in Predictive Analytics and Statistics.

Designed by practicing professionals and offers individual interaction with Industrial Mentors, practical workshops, and 12 lessons and assignments to be done in real life! So, you not only find a place for the theory of things, but you also get to prove its practical side! Click here for more on the subject.

Lack of Machine Learning Skills for Troubled Companies

Given the rapid rate at which technology is being implemented, many companies have been left to fend for themselves. Digital transformation is a huge industry, and the fact of the matter is that there are not enough technical experts to meet the needs of the new industry.

A New York Times report published in October 2017 estimates that the total number of people eligible for AI and Machine-related learning activities was less than 10,000 people worldwide.

This figure is likely to increase both – due to the number of jobs created – and decreased, as people gain ML reading skills on a daily basis. But the issue remains, that the supply of goods goes far beyond the need, in this case. In addition, it is also true that it does not require a separate set of qualifications to qualify for jobs in the ML field – it only requires a certain set of skills and abilities, all you can learn in the upGrad course of Machine Learning and AI!

Machine learning and Data Science are complex

If religion ruled the masses centuries before modern times, it is now true that Data Science ruled the masses, thanks to its all-encompassing nature and new trade and performance.

And Machine Learning is just a shadow of Data Science. To take your work as high as you can imagine and imagine, you can become proficient in both fields, which will enable you to analyze the alarming amount of data, and then proceed to extract the value and provide insight into the data.

In addition, in many organizations, ML engineers and Data Scientists work together on products, so you are likely to be exposed to Data Scientists if you are already an ML engineer.


So you have all the data now – who can learn machine learning, where to learn machine learning, how to start learning machine learning, and the best way to learn machine learning. It’s up to you now to make the most of this data, and take it to the next level of your career!

Leave a Comment

Table of Contents