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Complete Road Map To Prepare NLP-Follow This Video-You Will Able to Crack Any DS Interviews🔥🔥

In this video we will discussing about the complete
road map to prepare for NLP so that you can crack or ace any
data science interviews
NLP Playlist:
Complete Deep Learning playlist :
Bottom Top Approach Of Learning
1.Text Preprocessing Level 1- Tokenization,Lemmatization,StopWords,POS
2.Text Preprocessing Level 2- Bag Of Words, TFIDF, Unigrams,Bigrams,n-grams
3.Text Preprocessing- Gensim,Word2vec,AvgWord2vec
4.Solve Machine Learning Usecases
5.Get the Understanding Of Artificial Neural Network
6.Understanding Recurrent Neural Networks, LSTM,GRU
7.Text Preprocessing Level 3- Word Embeddings, Word2vec
8.Bidirectional LSTM RNN, Encoders And Decoders, Attention Models
9.Transformers
10.BERT
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Recording Gears That I Use

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Gpay: krishnaik06@okicici
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#naturallanguageprocessing
#nlp
#nlpinterviews
#revisionfordatascienceinterviews

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30 comments

  1. In 3 to 4 days I will be appearing for interview and my project are related to NLP sentiment analysis and recommendations system. It is going to help me a lot ☺️

  2. It was really helpful. Can u make videos on Grammer Correction using Rule based methord, Language Models & classifiers.
    its really hard to understand it otherwise

  3. i come across with all these concepts in my organisation but its hard me to working on question and answering using tensorflow or pytorch but i am aware of rasa core n rasa nlu where it is easy to generate question and answers. krish why dont you make a video on question and answering using tensorflow

  4. My project 1
    Income qualification:
    Objective:You are given a dataset of costa Rican household characteristics

    Problem statement:
    Identify the level of income and predict the level of need

  5. Hello Sir I have been following you and have currently completed your Machine Learning playlist. So can you tell me that how much prior knowledge we need to have to start with this course, do we need deep learning, if it is so then please tell me do I need advance deep learning like Boltzmann Machine, Autoencoders, GANs and etc or is it okay to start now.

  6. Have you ever used spacy when it comes to NLP? If yes: Would be lovely if you could cover how you used it…Thanks for your work

  7. He is that teacher which our education system genuinely needs . He’s self taught hence that is visible in his lectures. Absolutely amazing !!

  8. These guys in iNeuron are showing how the education world is suppose to work, how it can be free but also provide quality at the same time and not just a fancy useless certificate…

  9. Can you please guide me on learning NLP with R, I guess most of the libraries mentioned are python based. I am not sure if these libraries can also be used in R language as well – Please advise. Thanks

  10. I just love how you keep reassuring us in the video that you got us covered from bottom to top. This is super helpful. Thank you

  11. i love how you spelling it, if we could build such a pyramid for other techs, life of the learners would be much easier as it s enough to keep in mind this intuitive/organic scale of complexity.

  12. I was looking some one explain what has to learn exactly but I couldn’t find finally I got it on this channel, great thanks a lot sir

  13. At 13:00 you had mentioned about weakness of LSTM. If we take the use case of Statistical Machine Translation and I have 2 sentences in my training set :
    1) “I cross the river bank to reach primary school”
    2) “I need to go to the bank to urgently withdraw funds”
    Both are longish sentences. And if after conversion to Vector Representation, the word “Bank” has a different meaning in sentence 1) where we need to look at previous word river, while in sentence 2) we need to look ahead (right context).
    Is this the issue with plain LSTM that Bidirectional LSTM is able to overcome?

  14. And, could you also put up some material on how Hidden Markov Models are used in NLP? have studied them way back in 2011 during my Master’s degree in the pre Deep Learning era. But don’t have much practical exposure to NLP? And does acoustic model for phonemes recognition come more under speech Recognition? Could you also provide a short description on that?

  15. HI sir ,thanks for sharing your knowledge it really helps me alot sometime, i have a question.
    if LSTM has problem , why cant we directly use bidirectional LSTM instead of LSTM , can we skip LSTM and directly apply Bidirectional LSTM ?

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In this video we will discussing about the complete road map to prepare for NLP so that you can crack or ace any data science interviews NLP Playlist: Complete Deep Learning playlist : Bottom Top Approach Of Learning 1.Text Preprocessing Level 1- Tokenization,Lemmatization,StopWords,POS 2.Text Preprocessing Level 2- Bag Of Words, TFIDF, Unigrams,Bigrams,n-grams 3.Text Preprocessing- Gensim,Word2vec,AvgWord2vec 4.Solve Machine Learning Usecases 5.Get the Understanding Of Artificial Neural Network 6.Understanding Recurrent Neural Networks, LSTM,GRU 7.Text Preprocessing Level 3- Word Embeddings, Word2vec 8.Bidirectional LSTM RNN, Encoders And Decoders, Attention Models 9.Transformers 10.BERT ----------------------------------------------------------------------------------------------------------------------- Recording Gears That I Use --------------------------------------------------------------------------------------------------------------------------------------------------------- Please donate if you want to support the channel through GPay UPID, Gpay: krishnaik06@okicici Discord Server Link: Telegram link: Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more Please do subscribe my other channel too Connect with me here: Twitter: Facebook: instagram: #naturallanguageprocessing #nlp #nlpinterviews #revisionfordatascienceinterviews
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