How To Revise Data Science Concepts And Algorithms Efficiently Before Interviews-Most Important🔥🔥🔥🔥

Key Takeaways

hello all my name is krishnak and welcome to my youtube channel so guys today in this particular video we are going to understand how to revise concepts and algorithm before data science interviews now this is one of the most important question that was asked by many of my subscribers and recently there are many people who had made a successful transition towards data science and when i spoke to them you know they really wanted me to make this particular video uh and they said that this video will definitely be helpful for everyone because i'll tell you why i'm actually making this particular video guys because i understand that i've spoken to some of the people you know when they are learning data science you know probably machine learning or deep learning it usually takes somewhere around four to five months you know to at least get some good uh hands-on experience or to get some experience with respect to the algorithms so that they will be able to remember some of the complaint that i have already uh regularly seen from the people saying that krish when we are learning the future things probably the uh initial things that we have learned we tend to forget it okay and because of that we need to again revise those things and this is fine when we are in the learning process what about suppose we get an opportunity to attend an interviews and in that specific interviews they may ask from anywhere you know so how do we need to quickly revise the concepts of machine learning algorithms or deep learning algorithms so that we are pretty much comfortable to attend a data science interview so this was the thing that was asked this was the same question also that were asked from the people who made successful career transition and they they really wanted me to make this particular video so let's proceed and let's understand how we can revise concepts and algorithm before data science interview and guys uh it's not like i do not forget things you know i do not forget the algorithms even today if i have to take a specific class on a specific machine learning algorithm i have to revise that particular stuff you know i always do not remember all the concepts then and there okay yes i follow a strategy to remember some of the things efficiently and that same strategy i'll try to tell it to you in this particular video okay so let's begin and make sure guys you follow this strategy uh i am at least eighty percent sure that this strategy will work definitely for you uh because i've seen for many of the people when i've advised the strategy they were able to remember a lot of things so let's proceed now first of all you need to understand that uh once you get a data science interview call okay um you have done really a very amazing thing because you have set up your resume you have wrote end-to-end projects everything and finally you're successful to get the data science retrieve that is the most important thing so you have at least done the 50 percent of your task the remaining 50 of the tasks i'm just going to explain over here so uh whenever you get a data science interview call at least take four to five days of time before the interview not just like uh tomorrow only you take my interview and all yes if there is so much of urgency companies will tell you that okay take the interview tomorrow only uh otherwise most of the time what they do is that they set the interviews in the weekends so at least take four to five days of time so that you'll be able to get some amount of time for preparation so uh once you get an interview call the first point i'm saying that at least take four to five days okay you just say then that after four to five days you can set up the interview uh because this will actually give you for the interview preparation time for the interview preparation so this is the first thing that you have to take care of okay otherwise it will be very very difficult because the strategy that i'm actually going to tell you uh uh this will work okay at least take four to five days time uh before the interview and this will actually help you for the interview preparation now the second thing is that you need to focus on the job description also so probably you got an interview call you had applied your resume now suppose after four to five days you have told them okay let's go with the interview itself at a specific time then the second thing that you have to do just go and see the job description job description they'll be mentioning all the things that you really need to know just read all the things up suppose they say machine learning algorithm suppose they say deep learning algorithms suppose they say nlp techniques right so just keep a mind of that particular thing uh focus on that particular job description itself efficiently i'll tell you why this will be very very helpful then probably you have solved some end-to-end projects then then that is the reason why the people by the way why the interviewer actually liked your resumes so now go and check your end-to-end projects okay end-to-end projects or use cases that you have solved okay now in this particular end-to-end projects you have to definitely make sure and guys uh this is uh this this to end projects i know you may have implemented before the interview one before the interview only when you are implementing currently suppose if you are implementing it just don't implement this by using one or one or two algorithms try to use try to use all the machine learning algorithms all the ml or first of all let's go with ml okay suppose i'll give you an example okay okay end to end projects or use cases you really have to solve and when you're solving try to use all the ml algorithms now suppose if i am solving a regression problem statement now in regression problem statement you have various process like linear regression rich lasso you have decision tree regressor random foreign boost regressor and all the regression algorithms you basically have now when you are actually solving those kind of uh algorithm when you're solving this particular use case by using all those kind of algorithms also make sure that at the same time just try to go through that particular algorithms from where to go through it and all i have my playlist i'll show you that also all the link will be given in the description and i'll also go through all the playlists and all uh to give you an idea so whenever you want to solve any machine learning project let it be a classification or regression problem statement make sure that you try to use all the machine learning algorithms in case of regression suppose i will go ahead with five to six different different algorithms like linear lasso reg decision regressor random foreign using all this then i'll get an idea which algorithm is actually performing well and when i'm implementing it i'll have i'll have some idea about those kind of algorithms the next thing and suppose if it is a classification also problem also guys please make sure that you uh try to apply all the machine learning projects whichever you know and from those machine learning projects try to find out which is the best machine learning project how do you find out for that you have the knowledge of performance matrix okay before doing performance metrics also guys you also have to do hyper parameter tuning okay hyper parameter tuning okay then you have the performance matrix these two steps now whenever i talk about hyper parameter tuning and performance metrics performance metrics you need to know completely in depth okay like we call precision like false positive true positive true negative why did you select that specific algorithm okay suppose you had a huge amount of outliers at that time what what kind of metrics you actually followed what did you do for that kind of algorithms itself right suppose you had higher true positive uh you had higher false positive what did you do in order to reduce the false positive and based on that you select the machine learning algorithms everything of performance metrics i have already discussed in my playlist guys performance metrics is must roc auc score everything with respect to performance metrics accuracy recall precision f score everything needs to be you need to know about performance metrics guys because based on that only you will decide which machine learning algorithm to use because just consider that here you are trying to use all the machine learning algorithm it is not like just by seeing the accuracy you will go with that particular machine learning algorithm instead by checking all the performance metrics and performing the hyperparameter tuning you will go ahead in deciding which machine learning algorithm you need to select for that particular thing now suppose if these two things are very very strong guys understand learning machine learning algorithm will be very very easy because machine learning algorithm is all about maths right and uh to make your work easy i've already done that i've already created all the videos for all the machine learning algorithms if you really need to know the maths so in this particular way and this this this steps needs to be performed before the interview itself you know uh because once you get the interview call right these things all should be in your resume you need to know all these things and if you learn in this particular way will be able to connect all the dots and you will be able to efficiently learn all the machine learning algorithms which is all the best practices and all now here i've just written ml algo guys also try with deep learning algorithms let it be a n r n c n n whatever things you have actually done classification problem vision use cases anything that you want but always focus on the performance metrics which is very very much important right so five steps what i have done remember this at least take four to five days of interview preparation this is after you have submitted your resume after you have got the call then you have to also focus on the job description end-to-end project use cases you have done earlier make sure that you do it with the help of all the machine learning algorithms and you have all the results you you should be knowing why you are using this specific algorithm or why you have selected this particular algorithm for this specific use cases obviously because of performance metrics and hyperparameter tuning and internally you should also focus on two more things one is your feature engineering and feature selection like suppose i say that because this will be handy enough when you are actually telling to the interviewer now if you learn in this particular way you will be able to connect all the dots you will be able to efficiently revise everything within four to five days yeah you may be thinking but krish where is the entire algorithms and all what you're talking i'll just show you that specific playlist then in that probably when you have 4 to 5 days of times guys if you try to and i know in probably you will go with end to end projects how many end-to-end projects two projects is more than sufficient if you are able to learn efficiently in this particular manner later on the complete lifecycle of a data science project you need to revise how to explain it in front of the recruiter okay now two more important things because in the first two days i'm just writing that in the first two days if you have materials first two days you'll be able to complete all these things considering i've given you the materials i've given you the entire process where you have to learn and read it from so first two days trust me first two to three days okay let's stick two to three days you will be able to complete all these things you will be able to complete all these things right first two to three days now let's go with respect to the other days now we still have or two days suppose fourth and the fifth day in the sixth day you start follow my follow my virtual mock interviews i'll show you the playlist also guys my virtual motor contributes which i usually conduct in my youtube channel there you go and see how the candidates are answering the question what kind of questions we have answered because there i may give you a specific use case okay and tell you to basically solve it and it may happen that in the interview you should uh definitely uh you know uh take up uh in the interview they can ask you any use case right so i'm telling you follow my virtual mock interview playlist again the link and all everything i'll be showing you with respect to the videos seven if possible if you get time definitely attend attend my virtual mock interview now when i say attend attend as a candidate this virtual in mock interview i'm all doing along with sudanshi kumar who is the ceo of inu and he does take a lot of interviews where he provides you definitely a different different use cases and by following these two steps you'll be pretty much confident like suppose if are asking some questions and just imagine that you will be in front of the interviewer then will are you able to answer it just try it out just try to see it this too has actually this virtual mock interview playlist has helped everyone guys there's so many people trust me uh recently there were more than 15 people who had made a transition to data science when i spoke to them uh i just had conversation with 15 people but they have so many people who have made a transition i just asked him what did you follow they were saying sir we've just followed a virtual mock interview uh interview playlist and uh you know by that most of the questions were getting repeated and because of that we got confident we understood that how we should answer it so this is the thing that i really want to do and yes there was a major concern for many people that tend to forget things but trust me if you are able to implement the projects by using this strategy by using all the algorithms by revising all the concepts and all right within four to five days trust me you will be able to do if you do this much guys it is more than sufficient okay here i'm not just talking about ml i'm also talking about dl so dl in my dl playlist is also there in my dl playlist you'll be just able to revise all the revise all the basics when i say revise all the basics your basics needs to be very very strong guys i'll just write a star point basics needs to be very strong forget about everything your basics basics when i say with respect to machine learning algorithm algorithm maths probably if i go with deep learning optimization algorithm loss function the activation function how does neural network work because those are the base that is applied in any advanced neural networks even right now whatever the current research is happening is taking place from the researchers i think they'll be using all these basic concepts so this is needs to be very very strong and if you are able to satisfy if you are able to give all the answer to the recruiters trust me you have the ball in your court definitely you will crack the job one final thing that i really want to tell you guys explain explain the interviewers like a story suppose you want to explain your project focus focus explain i'll just write like explain in a way of life cycle of ds projects you have to explain in this particular way always start first with data gathering from where did you get the data what are the problems with the data what all things you did in the future engineering why you did it what are the problems you faced over there go with feature selection go with model creation model deployment uh hyper palette tuning retraining approach deployments wherever you did all these things how to revise these concepts trust me whenever you're learning whenever you're learning new things yes you tend to forget it i also tend to forget it guys but yes whenever i see some of the projects then i even check out my own videos to basically revise some of the concepts itself so these are the points guys how to revise concepts and algorithm before data science interview at least take four to five days focus on the job description because in job description they'll tell you specifically in nlp you have to be good at this logistic regression linear regression this that everything they'll be providing in the job description focus on all those things make sure that you implement end-to-end projects uh by considering all the machine learning algorithms and deep learning algorithms then you come to a conclusion why did you go ahead with the best machine learning how did you go ahead with that is basically hyper parameter tuning performance matrix follow my virtual mock interview playlist definitely attend my virtual mock interview playlist as a candidate because we do invite and trust because we are again going to start from the next week uh the virtual mock interview there we are also giving um some kind of prizes if you are able to perform well and those entire prizes will be sponsored by neuron okay this is what we are planning and probably this will be from monday itself uh and explain in the way of life cycle of a data science project uh this is the most important point now if i go back to my playlist so these are the playlists guys all the link will be given in the description this is my complete machine learning playlist here you'll be able to see every algorithm is completed like this you have linear regression ridge lasso okay um then you have multicollinearity uh then you have a logistic then you have decision tree everything is there guys all the algorithm k nn uh in symbol bagging random forest nada boost everything euclidean distance okay a db scan dimensional reduction pca name bias support vector machines gradient boosting xg boost everything is completed more than 90 of the algorithm is done you just have to revise the maths over there um then uh this was it uh this is my live virtual mock interview session so here you can go and check with respect to date and here is my interview questions playlist which i have specifically created with respect to some of the algorithms like uh which all uh like outliers impact which all algorithms so these are some of the common things that interview may focus on and they may ask you yes statistics is also important from all the points guys but remember statistics should also be learnt in a way where you have applied in your data science project they may ask you some simple simple question like normal distribution how do you reduce the outliers what is hypothesis testing what is p test what is t test everything i have already uploaded over here uh which will be which you will be able to understand in a pretty much better way right so this this was the thing that i really wanted to focus on and please make sure that you check out all these videos and follow this strategy guys so trust me it will definitely be helpful this is what i found out from the discussion of the people who have made a successful transition recently and yes more than if you just follow the strategy 80 percentage of the people definitely like you will be able to crack the interviews so i hope you like this particular video please do subscribe the channel if you haven't already subscribe i'll see you all in the next video have a great day thank you bye bye