Hello, everyone, welcome to the course of machine learning with Python. In this video we shall discuss about machine learning. So there are a lot of buzzwords around us these days. So what are those buzzwords, data analysis, big data probability, artificial intelligence, deep learning, machine learning, neural network statistics, data science, etc, etc. Now we'll be trying to demystify these buzzwords. So artificial intelligence enables the machine to think and take decision like humans, it begins in late 1950s.

However, machine learning we can think of as a subset of artificial intelligence, which are nothing but a collection of statistical tools to learn from data. It begins in early 1970s and popularized in late 1980s. Deep Learning, which we can think of even a subset of machine learning is usually conceived as a multi layered neural networks, learning features automatically from data Makes decision it was popularized in 2006 onwards. in present day all the researches in the field of artificial intelligence is mainly in deep learning. Now, data science encompasses all of these deep learning machine learning and artificial intelligence. So, data science basically is nothing but making sense out of messy data.

It comprises of data acquisition, data visualization, data interpretation, data wrangling, data analysis, data validation, etc. Now, applications predictive analytics selects the weather prediction or forecasting stock price prediction, predicting the chance of defaulting alone or predicting the expected time of journey. So, all kinds of predictive analytics is basically done by machine learning and artificial intelligence nowadays. Then comes recommender system. So, this is the Netflix recommendation system. We have seen recommendation system in Amazon or free got like websites as well.

So, what is the recommendation system? So, it will recommend you some products or things based on your past history then comes natural language processing which is having a widespread application nowadays. So, this is one of the application of natural language processing. So, different kinds of voice recognition application are made using NLP like Amazon Alexa or google assistant or Apple Siri etc Then using natural language processing, it is possible for what is addition. So, what is query solution so, if you try to type something it will automatically suggest the type of query we'll be looking for NLP has greatly facilitated question answering in the form of natural language. So, let's see if I type what is the temperature it will show The temperature of the current state and the current time Okay.

Now, how machine is understanding our human language. So this is because of the natural language processing interval inside the machine learning algorithm which runs the machine. Natural Language Processing enables to have a chat bot which will interact with the user like a human being and it is greatly useful and it is also scalable natural language processing also enables machine translation let's say this is one sentence in Spanish Hola, amigo Como estas and in English it means hello Fred. How are you? If you notice in Spanish, it is only four words however, it is these five words. So it is not simple what to what transition okay.

So transition from sentence from one language to another may not be a straightforward approach. Okay, so there is a bank At the bank of the river, so wrote that here the first bank actually means financial institution. But the second one actually means the side of the river, right. But it has successfully translated that into Hindi. Nothing catered for a bank. So here that means the bank of the river.

And the last bank is basically the financial institution we'll be talking about. So the machine learning system has understood the context of the world and translated accordingly. So IBM Watson meaning geo party, it is a great landmark for the national language processing community. So God is an American peace competition game show, and God won this beating to all time favorite contestant of this particular game. Then comes the applications in computer vision. So one of the most significant application of AI AI and machine learning in computer vision is face recognition.

Another application which is gaining popularity these days is self driving car. So, in fact, Google, where all kinds of companies are now investing in self driving car, enable computer vision also give machines the ability to dictate the objects in the world computer vision also gives the machine the ability to track the object during astrophysics and space exploration. So NASA discovered Kepler 90 is using artificial intelligence and data science applied to astronomical data. Kepler 90 ai is a super art exoplanet located 2545 light years away from art. So first ever direct image of black hole was published in 10th April 2019. By analyzing terabytes of data produced by Event Horizon telescope, this black hole laughs around the center of a nearby galaxy named Messier 87, which is approximately 53 million light years away from art, no application domains of machine learning and AI is still being applied in band or the banking and financial securities institution known as bf Si, it can be applied in automobile, it can be applied in healthcare, it can be applied in fmcg it can be applied in retail and so many places.

So, it has a widespread application for machine learning as carrier. So, this is one of the famous quotation coined by Cisco. It said that data is the new oil and information is the new currency. So job market perspective, so it has a high demanded market. So around 700,000 jobs by 2020, as forecasted by IBM switch is only in the United States in India. It would be much better then but the supply of machine learning engineer or data scientist is low.

So, it is having a very high job market financial perspective, one of the few highest paying jobs in 21st century pay is normally 20% to 50% higher than traditional software engineering application perspective, as we have already seen, it has numerous domains where ml and AI can be applied, like gfsi, retail, fmcg, healthcare, manufacturing, and you name it. So this is the superpower we can say that enables a successful carrier. Thank you. See you in the next lecture, where we'll be discussing about the machine learning how it has been done, and what is unsupervised learning, supervised learning etc. So see you in the next lecture. Thank you.