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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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 ☺️
All the Best Manish for your interview. Wish you crack it and come out with flying colours
This will give you an advantage
, m(dozens so where ah as
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
Sir can u make one for computer vision?
Nice, I got it what is NLP. thank you so much for your lectures.
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
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
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.
You just need to have the basic knowledge of DL
@dhruba1992 Thanks brother for this, although I have already joined an organization as a data scientist, I hope your reply would help others who are still in need of such a valuable information ☺️☺️.
Congratulations brother
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
He is that teacher which our education system genuinely needs . He’s self taught hence that is visible in his lectures. Absolutely amazing !!
P
Great Sir, Big fan of yours. Thanks a lot for detailed course. Hats off.
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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…
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
I think anyone who is in nlp has eventually followed this kind of roadmap even though the order may be a bit different.
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
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.
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
Can you please do the video on unstructured text to ontology using NLP. Thanks
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?
Were you able to figure it out ?
If yes, pls explain
Bidirectional LSTM required when you are referring to intial word later in sentence ,for example I belong to Hyderabad so i can speak Telugu well ..so in this cases you need it
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?
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 ?