Machine Learning Applications Across Industries

Key Takeaways

hello everyone welcome to the session so in this video we will talk about the applications of machine learning but before that I hope that you now already have understood that how many different types of machine learning algorithms that we have so in the last session I have classified into four major categories supervised unsupervised semi-supervised and the last one is reinforcement and I have tried to explain with a practical use case so that you will be able to clearly understand the concept between these four differen now let's talk about the applications of machine learning like in what domains uh it is being used I would say that it is heavily used in almost every domain nowadays but specifically talking about a few use cases with you all let's say Healthcare if you remember in the last session I have given you the example of diabetic prediction right that belongs to a healthcare again uh scenario disease diagnosis maybe if you want to detect given the patient data given the feature or The Columns of a patient data whether a person is having a cancer cancer or not whether a person is having a diabetes or not whether is a whether a person is having a high BP or not and many more scenarios can come here again drug Discovery personalized treatment recommendation we can create with the help of the machine learning algorithms that we have followed by in finance sector again machine learning is playing a very pival role why is that so because you all know in order to get the loans in order to get UC education loan in order to get the home loan how much loan a person will be able to get depends upon the Civil score depends upon their documentations and the features that we have with respect to the person apart from this uh uh what is the you know credit risk assessment usually Banks does and if let's say the risk is very high then Banks usually don't provide the loans so it's a very strict uh version and that's where the P2P lending and all those things came into picture and and uh that's why usually said that that that's very unsafe in order to put your money as a investment so what I'm trying to convey here is that there are so many algorithms in machine learning which are heavily being used in the finance domain specifically I'm saying about banking sectors similarly let's say uh we want to detect whether the person is a fraud or not right so again depending upon the features features meanings whatever be the information which bank usually enters by the customer depending upon that depending upon his or her past history uh machine learning models will be applied and with the help of that we will be able to get that okay whether a person is having a fraud or not don't worry whatever things I'm saying here theoretically in the complete session you will observe upcoming sessions we will do the implementation of all these things on a different different data set whether it be a healthcare domain whether it be a finance domain whether it be a marketing domain I'll talk about every domain of the Practical demonstration of everything we will do the live implementation so make sure that you will stay tuned and you will be able to get the understanding but as of now just try to appreciate that you know how this domain is really helpful for these sectors similarly in marketing in the last session itself I have tried to explain you the example of clustering where I have said you that let's say I want to create three clusters having a different customer base One customer is having a very high earning of maybe more than one lakh another customer is having a moderate earning Maybe in between 50,000 to 1 lakh and the Third customer is having a earning Which is less than 50,000 so depending upon that maybe if let's say I own a company uh let's say I'm the owner of Amazon so I will recommend that person according to their own choices maybe a person who is having a very good salary I'll recommend them iPhone if a person is having a very moderate salary I'll recommend maybe a OnePlus phone you know and if a person is having a very low salary maybe I'll recommend a Nokia phone depending upon the range of prices which a person can afford a business can be able to generate so much of Revenue and that's why if you will observe so many businessmen uh nowadays usually hires data scientist because with the help of a data they will be able to generate the revenue uh because of the insights which data scientists will be able to give to them right so this is about the utility or the applications of machine learning domain I hope that now this domain uh you know really excites you and you will definitely learn a lot more depth things in this particular domain uh in the upcoming sessions because in every domain which I I'm talking about here I will take the data set and will try to show you the Practical demonstration of each and everything so that you will be you know value that thing instead of just learning theoretically you will be able to implement those things and if you will go to the industry you will be able to be become a important asset for that as well so with this let's end today's video here I just want to talk about the applications and I I think I have already explained to you all and apart from this in other sectors also it's it's not about only in this sectors machine learning is there nowadays machine learning is I think everywhere in every other sector whether it be edtech in edtech again companies are using machine learning algorithms to recommend the courses which a person or student should buy depending upon their interest whether it be a course era whether it be a very big big Ed Texs you will observe that recommended system is working on internally which recommends the courses with respect to the interest a person is you know sharing to the uh as a feature as a as a column so internally again these things are happening so again I'm saying it's a very vast domain and I think it's a very good time to start this Learning Journey uh to be aware about the concepts that how these things are really helpful for us in order to have a bright future okay so with this let's end today's video I'll see you all very soon in the upcoming video where now we will talk about further things in machine learning bye-bye everyone and see you