Interview Prep Day 1-How To Learn Machine Learning Algorithms For Interviews- Naive Bayes Classifier

Key Takeaways

hello all my name is krishnank and welcome to my youtube channel so guys many of you were actually asking me with respect to data science interview preparation right so i've started it guys uh and initially you know this is with respect to data scientists where you are actually focusing on machine learning deep learning you know and right now i'm just starting with something important like how you should learn machine learning and reviews for how you should learn machine learning algorithms for interviews and this is pretty much important the reason why i'm preparing this is guys recently in many of the virtual interviews right when i used to ask okay which is a machine learning algorithm which is your favorite machine learning algorithm people used to say about different different machine learning algorithms itself but when we used to ask more in-depth questions right let it be with respect to theoretical concepts let it be with respect to concepts like missing values outliers you know they were not able to answer that frequently and what i have seen in candidates is that you know when when they are just focusing on the math part they miss some of the important parts also so with respect to this what i'll do is that now i'll be preparing uh this kind of videos and in every videos i'll be talking about one machine learning algorithms or two machine learning algorithms you can tell me like in one video whether you want three machine learning algorithms for me to explain i'll explain that apart from that i'll also note down all the important questions and i'll also be giving my youtube link from where you can actually read all those things and if from my youtube tutorials you're not able to find everything i'll also be giving other youtube links so you know where you can actually refer all those things so please make sure that you follow this interview preparation compulsory guys it will be very very beneficial for you because the reason is that i've i've called so many people i've spoken them in live interview sessions and all uh for their transition phase what did they focus on what did the interviewer ask and they were pretty much clear they told that krish they just asked the maths part they asked some tricky questions and once they were able to answer that you know they were able to get the job so in this video i'll be talking about nay bias classifier and its important interview questions itself and i have also given the link from where you should actually refer and what you should actually refer this is pretty much important for everyone guys please make sure that you follow this religiously at least for one month trust me your interview sessions will go definitely well right and i'm not telling that you have to be perfect with respect to each and every machine learning algorithm suppose in your project as a fresher as an experienced person what type of algorithms you are actually using let it be machine learning and deep learning algorithms but make sure that the things that we discuss over here you are prepared with respect to that now in this video we'll be talking about knee bias classifier now definitely the theoretical understanding i've already uploaded two videos uh i think three videos in my complete machine learning playlist here i've given you tutorials 48 49th so here you can see 48 and 49 this is all about name bias classifier in-depth intuition where we have discussed about the maths similarly tutorial 49 uh we have discussed about this how it actually gets performed on test text data because name bias classifier works really well with respect to text data projects like sentiment analysis and all so definitely you can go through the two theoretical videos from here now we need to understand these all things because when you say that which is your favorite machine learning algorithm if you say name bias probably the interviewer will be asking out of all these particular questions so let me just say that what are the basic questions so what are the basic assumptions when should you use a new bias classifier the basic assumption with respect to your data set is that the features are independent right all your independent features needs to be independent itself why i'm telling you this this is pretty much important because all the further things will be derived based on these basic assumptions okay you need to have a basic assumption where your features are independent okay then what are the advantages when you're preparing or when you're working with nate bias so advantage is that it works very well with many number of features now why i'm telling you this particular point i told you that nlp related things where you're doing sentiment analysis right at that time you basically use name bias and in sentiment analysis uh the text data the nlp text data right what you do is that you use some pre-processing technique like word to work tfidf bag of words and with the help of that you convert that into many vectors and sometimes those vector sizes like 5000 features 6000 feature 10000 features that based that depends on the base of the unique words that is present in tf idf or in bag of words but in case of word to back also you will be getting a very huge size of vectors it may be of 10 000 features it may be of 15 000 features that is considered based on the number of words in the dictionaries right so this first feature the first point it works well with many number of features this is pretty much important right if if if if a interviewer asked that okay for this nlp statement why did you use any bias you can tell all these advantages guys why other algorithms will not work well i'll be preparing that in other interview questions under interview preparation like day 2 day 3 but understand why you're using nave bias you need to know that these are the advantages it works very well with many number of features it works very well with large training data sets right suppose if you have a huge training data sets like nlp sentences so many work sentences are there it will definitely be very very handy with respect to that it converges faster when we are training the model because nave bias completely works on probability guys okay it works in probability so the converging also will happen very very quickly it also performs well with category features right when i consider back of words right you'll be either getting values like ones zeros zeros ones it works it works very nicely with the help of sparse matrix okay so these are some important points this you need to be prepared with respect to interview questions right because they may ask you like okay it works with value well very well with my high number of features why you have to answer them they'll not just say that directly they'll not say that okay just do the coding but instead they'll ask this all things these all important things okay now what is the one of the disadvantage i hope everybody's clear with these four advantages and this is pretty much important you need to remember this guy's theoretical understanding you can get from these two videos okay now what are the disadvantages disadvantages is basically its core correlated feature effects performance now what does this basically mean is that suppose you have you have 5000 features right after you perform some nlp techniques and you get actually five five thousand features uh i mean text pre-processing techniques and get five thousand features in that five thousand features there are many many number of features which are actually correlated and that time it may be a problem it will definitely become a problem right so for this particular thing correlated feature affects the performance with respect to name bias because it exactly works on probabilities right probabilities is the main key concept inside it right so this is the advantages if you really want to and what you can do is that you can also go and search this in google you'll be getting all the answers over there just take this particular point why nave bias works very well with many number of features why uh nave bias does not work with correlated features effect why it affects performance right performance is definitely an impact over there guys because understand when you are actually calculating the probability of one feature with respect to the other feature definitely that that many number of probabilities needs to be getting computed so if definitely it affects the performance now this is again another question that i would like to ask my students also whether feature scaling is required or not and i've also told you that for which all algorithms feature scaling is required okay you can you can name down those algorithms in the description of this particular video but in this case in a bi since it works based on probability no feature scaling is required okay now coming to the next question that is the impact of missing values does and this is all our common question guys you need to understand with x respect to each and every machine learning algorithms it will be very very easy now this impact of machine values guys i searched various resources various articles because i'm also not perfect you know with some of the things i need to search google okay so you can consider my example guys and never feel demotivated if you don't know anything you just have to go and search for google impact of missing values from one of the high resource articles it says that name buyers can handle missing data attributes are handled separately by the algorithm at both the model construction time and the prediction time and for this amazing there is one explanation in this youtube video the youtube video channel name is of victor lavenco he's an amazing teacher guys i think you can follow his channel also they are so amazing videos with respect to different different where you will be able to understand the theoretical understanding okay so in this missing values impact of missing values nay bias does not get impacted by missing values because they can handle those missing values okay so here you can see that we have covered so many things we have covered uh advantages disadvantages whether feature scaling is required or not impact of missing values and here is basically that name bias can handle the missing value data and with respect to this try to implement this with the help of practical examples also and see guys when there are some missing values how nate bias is actually using it is actually solving it is actually creating the model this you need to explore it okay i can definitely create a video of that and if you really want a video you can tell me neighbors i'll take an example probably in one of my live stream where i've used name nlp techniques i've created neverwise but i did not take care of missing values at that time missing values i handled it in a different way but it is saying that name buys can actually handle the missing values itself so definitely watch this tutorial guys you'll be able to understand the whole theoretical understanding and i have referred also many videos over here from here to actually clear my understanding it is very very good uh with victor laverinko again uh he's a he has an amazing thing so with respect to neighbor's classifier you can see that he has just uploaded 14 videos you can deep dive into each and everything over here okay so coming to the next thing what is the impact of outliers this is the most favorite question by the interviewers i have you have also seen my examples in virtual interviews i've asked this question regularly what is the impact of outliers you know so in case of nepal it is actually very very robust to outliers again some of the articles will be saying that it is not robust too it is sensitive to outliers but trust me guys since nape bias always remember whenever you want to answer this kind of question understand how the maths of the those algorithm actually works okay so it usually uh you know it is robust to outliers so this is the part that is pretty much important for you all and what are the different problem statements you can solve using name bias it is sentiment analysis spam classifier tutorial sentiment analysis document categorization you can search all this in the internet guys but again the main thing is that i really want to explain you in this particular way suppose you are considering one algorithm what are the important things that you should consider i've given you the theoretical understanding i've given you this different problem statements that they can be used i've also told you that what are the possible important interview questions that the interviewer may ask and i will be creating like this for all the machine learning algorithms if you really want me to cover multiple algorithms in one session you can tell me if you want this kind of session again if you feel that this is very very important and this is all things i'll be giving you in the github guys just let me know whether this format you like i've given the youtube link also i've given other things also right and whether you like all this particular format or not then only i'll be continuing is i really need your feedback with respect to this uh in every interview preparation day two day three day four i'll be taking one machine learning algorithm so that you will also be able to prepare it so probably how many machine learning algorithms are there i'll cover that all completely then i'll go and cover all the deep learning algorithms you know like and cnn rnn and all those things i'll then i'll go to the advanced stage so like this whether you want this or not just let me know i hope you like this particular video please do subscribe the channel if you are not already subscribed and again the github link along with this all the details will be given in the description of this video so i hope you like this guys and i'll see you in the next video have a great day thank you bye