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[Music] hello everyone this is Ethel from Ed Eureka and welcome to today's session on what is machine learning as you know we are living in a world of humans and machines humans have been evolving and learning from the past experience since millions of years on the other hand the era of machines and robots have just begun in today's void these machines or the robots are like they need to be programmed before they actually follow your instructions but what if the Machine started to learn on their own and this is where machine learning comes into picture machine learning is the core of many futuristic technological advancement in our world today you can see various examples or implementation of machine learning around us such as Tesla's self-driving car Apple Siri Sophia AI robot and many more are there so what exactly is machine learning well machine learning is a subfield of artificial intelligence that focuses on the design of system that can learn from and make decisions and predictions based on the experience which is data in the case of machines machine learning enables computer to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task these programs are designed to learn and improve over time when exposed to new data let's move on and discuss one of the biggest confusion of the people in the world they think that all the three of them the AI the machine learning and the deep learning all are same you know what they are wrong let me clarify things for you artificial intelligence is a broader concept of machines being able to carry out tasks in a smarter way it covers anything which enables the computer to behave like humans think of a famous Turing test to determine whether a computer is capable of thinking like a human being or not if you are talking to Siri on your phone and you get an answer you are already very close to it so this was about the artificial intelligence now coming to the machine learning part so as I already said machine learning is a subset or a current application of AI it is based on the idea that we should be able to give machine the access to data and let them learn from themselves it's a subset of artificial intelligence that deals with the extraction of pattern from data set this means that the machine can not only find the rules for optimal behavior but also can adapt to the changes in the world many of the algorithms involved have been known for decades centuries even thanks to the advances in the computer science and parallel computing they can now scale up to massive data volumes so this was about the machine learning part now coming over to deep learning deep learning is a subset of machine learning where similar machine learning algorithm are used to train deep neural networks so as to achieve better accuracy in those cases where former was not performing up to the map right I hope now you understood that machine learning AI and deep learning all three are different okay moving on ahead let's see in general how a machine learning work one of the approaches is where the machine learning algorithm is strained using a label or unlabeled training data set to produce a model new input data is introduced to the machine learning algorithm and it make prediction based on the model the prediction is evaluated for accuracy and if the accuracy is acceptable the machine learning algorithm is deployed now if the accuracy is not acceptable the machine learning algorithm is strained again and again with an argument a training data set this was just in high-level example as there are many more factor and other steps involved in it now let's move on and sub categorize the machine learning into three different types the supervised learning unsupervised learning and reinforcement learning and let's see what each of them are how they work and how each of them is used in the field of banking healthcare retail and other domains don't worry I'll make sure that I use enough examples and implementation of all three of them to give you a proper understanding of it so starting with supervised learning what is it so let's see a mathematical definition of supervised learning supervised learning is where you have input variables X and an output variable Y and you use an algorithm to learn the mapping function from the input to the output that is y equal FX the goal is to approximate the mapping function so a lot whenever you have a new input data X you could predict the output variable that is why for that data right I think this was confusing for you let me simplify the definition of supervised learning so we can rephrase the understanding of the mathematical definition as a machine learning method where each instances of a training data set is composed of different input attribute and an expected output the input attributes of a training data set can be of any kind of data it can be a pixel of image it can be a value of a database row or it can even be a audio frequency histogram right for each input instance and expected output values associated the value can be discrete representing a category or can be a real or continuous value in either case the algorithm learns the input pattern that generates the expected output now once the algorithm is trained it can be used to predict the correct output of a never seen input you can see I am age on your screen right in this image you can see that we are feeding raw inputs as image of Apple to the algorithm as a part of the algorithm we have a supervisor who keeps on correcting the machine or who keeps on training the machine it keeps on telling him that yes it is a Apple no it is not an apple things like that so this process keeps on repeating until we get a final train model once the model is ready it can easily predict the correct output of a never seen input in this slide you can see that we are giving an image of a green apple to the machine and the machine can easily identify it as yes it is an apple and it is giving the correct result right let me make things more clearer to you let's discuss another example of it so in this slide the image shows an example of a supervised learning process used to produce a model which is capable of recognizing the Ducks in the image the training data set is composed of label picture of ducks and non ducks the result of supervised learning process is a predictor model which is capable of associating a label duck or not duck to the new image presented to the model now once trained the resulting predictor model can be deployed to the production environment you can see a mobile app for example once deployed it is ready to recognize the new pictures right now you might be wondering why this category of machine learning is named as supervised learning well it is called as supervised learning because the process of an algorithm learning from the training data set can be thought of as a teacher supervising the learning process if we know the correct answers Gorem iteratively makes while predicting on the training data and is corrected by the teacher the learning stops when the algorithm achieves an acceptable level of performance now let's move on and see some of the popular supervised learning algorithm so we have linear regression random forests and support vector machines these are just for your information we'll discuss about these algorithms in our next video now let's see some of the popular use cases of supervised learning so we have Co Donna oh Donna or any other speech automation in your mobile.phone trains using your voice and once trained it started working based on that training this is an application of supervised learning suppose you are telling ok Google call Sam or you say hey Siri call Sam you get an answer to it and the action is performed and automatically a call goes to Sam so these are just an example of supervised learning next comes a weather app based on some of the prior knowledge like when it is sunny the temperature is higher when it is cloudy humidity is higher any kind of that they predict the parameters for a given time so this is also an example of supervised learning as we are feeding the data to the machine and telling that whenever it is sunny the temperature should be higher whenever it is cloudy the humidity should be higher so it's an example of supervised learning another example is biometric attendance where you train the Machine and after couple of inputs of your biometric identity beat your thumb your iris or your earlobe or anything once trained the machine can validate your future input and can identify you next comes in the field of banking sector in banking sectors supervised learning is used to predict the creditworthiness of a credit cardholder by building a machine learning model to look for faulty attributes by providing it with a data on deliquent and non deliquent customers next comes the healthcare sector in the healthcare sector it is used to predict the patient's readmission rates by building a regression model by providing data on the patient's treatment administration and readmissions to show variables that best correlate with readmission next comes the retail sector and retail sector it is used to analyze a product that a customer buy together at as this by building a supervised model to identify frequent itemsets and association rule from the transactional data now let's learn about the next category of machine learning the unsupervised part mathematically unsupervised learning is where you only have input data X and no corresponding output variable the goal for unsupervised learning is to model the underlining structure or distribution in the data in order to learn more about the data so let me rephrase you this in simple terms an unsupervised learning approach the data instances of a training data set do not have an expected output associated to them instead unsupervised learning algorithm detects pattern based on init characteristics of the input data an example of machine learning tasks that applies unsupervised learning is clustering in this task similar data instances are grouped together in order to identify clusters of data in this slide you can see that initially we have different varieties of fruits as input now these set of fruits as input X are given to the model now once the model is trained using unsupervised learning algorithm the model will create clusters on the basis of its training it will group the similar fruits and make their cluster let me make things more clearer to you let's take another example of it so in this slide the image below shows an example of unsupervised learning process this algorithm processes an unlabeled training data set and based on the characteristics it groups the picture into three different clusters of data despite the ability of grouping similar data into clusters the algorithm is not capable to add labels to the crop the algorithm only knows which data instances are similar but it cannot identify the meaning of this group so now you might be wondering why this category of machine learning is named as unsupervised learning so these are called as unsupervised learning because unlike supervised learning ever there are no correct answers and there is no teacher algorithms are left on their own to discover and present the interesting structure in the data let's move on and see some of the popular unsupervised learning algorithm so we have here k-means Apriori algorithm and hierarchical clustering again these are just for your information sake we'll discuss about these algorithms in our next video now let's move on and see some of the examples of unsupervised learning suppose a friend invites you to his party and where you meet totally strangers now you will classify them using unsupervised learning as you don't have any prior knowledge about them and this classification can be done on the basis of gender age group dressing education qualification or whatever way you might like now why this learning is different from supervised learning since you didn't use any past or prior knowledge about the people you kept on classifying them on the book as they kept on coming you kept on classifying them yeah this category of people belong to this group this category of people belong to that group and so on okay let's see one more example let's suppose you have never seen a football match before and by chance you watch a video on the internet now you can easily classify the players on the basis of different criterion like player wearing the same kind of jersey or in one class player wearing different kind of jersey are in different class or you can classify them on the basis of their plane style like the guys are attacker so he is in one class he's a defender he's in another class or you can classify them whatever way you observe the things so this was also an example of unsupervised learning let's move on and see how unsupervised learning is used in the sectors of banking health care undertale so starting with banking sector so in banking sector it is used to segment customers by behavioral characteristic by surveying prospects and customers to develop multiple segments using clustering in healthcare sector it is used to categorize the MRI data by normal or abnormal images it uses deep learning techniques to build a model that learns from different features of images to recognize a different pattern next is the retail sector in retail sector it is used to recommend the products to customer based on their past purchases it does this by building a collaborative filtering model based on the past purchases by them I assume you guys now have a proper idea of what unsupervised learning means if you have any slightest doubt don't hesitate and add your doubt to the comment section so let's discuss the third and the last type of machine learning that is enforcement learning so what is reinforcement learning well reinforcement learning is a type of machine learning algorithm which allows software agents and machine to automatically determine the ideal behavior within a specific context to maximize its performance the reinforcement learning is about interaction between two elements the environment and the learning agent the learning agent leverages two mechanism namely exploration and exploitation when learning agent acts on trial and error basis it is termed as exploration and when it pacts based on the knowledge gained from the environment it is referred to as exploitation and this environment rewards the agent for correct actions which is reinforcement signal leveraging the rewards obtain the agent improves its environment knowledge to select the next action in this image you can see that the machine is confused whether it is an apple or it's not an apple then the machine is trained using reinforcement learning if it makes correct decision it get rewards point for it and in case of wrong it gets a penalty for that once the training is done now the machine can easily identify which one of them is an apple let's see an example here we can see that we have an agent who has to judge from the environment to find out which of the two is a duck the first task he did is to observe the environment next he selects some action using some policy it seems that the machine has made a wrong decision by choosing a bunny as a tack so the machine will get penalty for it for example - 50 Boeing for a wrong answer right now the machine will update its policy and this will continue till the machine gets an optimal policy from the next time machine we'll know that bunny is not a duck let's see some of the use cases of reinforcement learning but before that lets see how Pablo trained his dog using reinforcement learning or how he applied the reinforcement method to train his dog Pablo integrated learning in four stages initially Pablo gave me to his dog and in response to the meet the dog started salivating next what I did he created a sound with the Bell for this the dog did not respond anything in the third part it tried to condition the dog by using the Bell and then giving him seeing the food the dog started salivating eventually a situation came when the dog started salivating just after hearing the bell even if the food was not given to him as the dog was reinforced that whenever the master will ring the bell he will get the food now let's move on and see how reinforcement learning is applied in the field of banking healthcare and retail sector so starting with the banking sector and banking sector reinforcement learning is used to create a next best offer model for a call center by building a predictive model that learns over time as user accept or reject offer made by the sales staff fine now in health care sector it is used to allocate the scarce medical resources to handle different type of er cases by building a mark of decision process that learns treatment strategies for each type of er case next and the last comes the retail sector so let's see how reinforcement learning is applied a retail sector in retail sector it can be used to reduce excess stock with dynamic pricing by building a dynamic pricing model that are just the price based on customer response to the offers I hope by now you have attained some understanding of what is machine learning and you are ready to move ahead if you like this video stay tuned for my next machine learning tutorial and yes don't forget to hit the bell icon and subscribe yourself for our upcoming tutorial series before I end this video if you have any confusion regarding the topic you can add your query to the comment section thank you I hope you have enjoyed listening to this video please be kind enough to like it and you can comment any of your doubts and queries and we will reply them at the earliest do look out for more videos in our playlist and subscribe to Eddie Rica channel to learn more happy learning

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