This was very very informational. I was recently interviewed for a data science role and i was also asked questions on cnn and transformer model. I answered everything related to cnn but i had no idea on nlp transformer model. It has helped me a lot by watching this interview. I was also asked on the recent popular models like gpt2 and gpt3. I would like to thank Sudhanshu sir and Krish sir for giving their time. Thank you.
21:24 the solution presented selects entire columns of the dataset whose datatype are float and object; it seems the question asked was to filter the"data points" a.k.a records which have float values in day column and string values in Gender column. Kindly clarify.
@krish sadhanasu explanation with CNN is partially correct .....we can build time-sequence CNN+LSTM to do video analysis. Only reason CNN is used to extract features and reduce features but here we already have the feature . So we can directly use LSTM > RNN . Also we had done one mistake we have aggregated the data on year and removed month. We should have aggregated to a month and then based on EDA we could use SRIMAX or ARIMA
Hi Krish/All, can someone provide me any video link or any resource where I can practice these kind of practical questions like the one asked by the interviewee (python questions) just so that I can brush up my skills.
Sir just one small request, whatever questions u guys ask in the interviews pls try to pen down it and later try to provide answer of it in the description or later pin down the comment.
8:59 python question : I've a question. Why would I apply a filter to select only those records from the dataset that have floating point values in day column whose datatype is already "floating point"? To select records containing string values in Gender column whose datatype is object we have to apply filter because object type column may contain numeric values enclosed in quotes. Note: I have not watched complete video yet.
55:10 The biggest disadvantage of LSTM/GRU is they're incapable of Transfer Learning, No support for parallel distributed training, 'tanh' activation functions and also vanishing gradients
Yup.. I've also experienced vanishing gradients with LSTM timeseries. On the contrary , I was able to generate decent forecast with CNN (Conv1D) using leakyRELU.
Good interview. I watched the whole thing. It would be helpful if you could tell if the candidate would be hired or not for a data science role based on the performance in the interview...
After watched this video, I realized that now I am able to crack any Interview related to NLP and CNN. How can I approach having a Virtual Mock Interview on Computer Vision using Deep Learning and in General Data Science?
mylist = list(df.select_dtypes(include=['float','object']).columns) is this right can someone tell me for the first question if we don't use .columns we will get true false which col don't have the datatype in day or gender. Please tell me if this is the solution if some can.
I am Fresher and I don't know python but I am planning to persue my career as data analyst from last 20 day's so my friend suggested me to watch video from your RU-vid channel. really it is wonderful interview. please sir keep it up 🙏🙏🙏
Is anybody trying to find data science related jobs in delhi/ncr cause i am to ,but there aren't any ..most of the data science jobs qre in Bengaluru pune Hyderabad
Q1 soln: df['day_temp']=df.day.apply(lambda x: True if isinstance(x, float) and np.isnan(x)==0 else False) df['gender_temp']=df.gender.apply(lambda x: True if isinstance(x, np.object) else False) df[(df.day_temp==True) & (df.gender_temp==True)].index OR just df.dropna().index Q2. sorted(s1.lower())==sorted(s2.lower()) Q3. df2 = df[['year', 'gender', 'births']].groupby(['year', 'gender']).sum() df2.head() will get table having male and female births year wise which can be scatter plotted.
i was confident and applying for data science fresher role. i was frustrated,why i am not getting interview call. i thought i can easily ace teh interview , all i want is interview call. i dont know why this video came to my feed. after struggling 20 minutes i got thisss... df[["gender","day"]][df["day"].notna()] after this i am brokken.. i took 20 minutes to solve this simple thing 🤧🤧🤧🤧🤧🤧
