Complete NLP Machine Learning In One Shot

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The-Grand-Complete-Data-Science-Materials

Timestamp

00:00:00 Roadmap To Learn NLP
00:16:00 Practical Usecases Of NLP
00:21:23 Tokenization And Basic Terminologies
00:31:31 Tokenization Practicals
00:45:11 Text Preprocessing Stemming Uing NLTK
01:02:02 Text Preprocessing Lemmatization USing NLTK
01:14:40 Stopwords, Parts Of Speech, NAmed Entity Recognition
01:50:50 Different types Of Encoding
02:45:18 Word Embedding, Word2vec
03:29:37 Skipgram Indepth Intuition
03:39:06 Average Word2vec With Implementation
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[srikrithibhat]

Differences between NLTK and Spacy :
i)Purpose and Design: Most of the times NLTK will be focused more on Research, Academic and Educational sectors and Spacy is designed with a focus on efficiency and production use.
ii) Performance: NLTK is slower than Spacy which can be a drawback for large-scale or real-time processing tasks. Whereas spaCy is optimized for high performance, making it much faster than NLTK. It’s designed to handle large volumes of text data efficiently.
iii)Features and Capabilities: NLTK provides tools for almost every NLP task and It is rich in Educational Resources. spaCy includes pre-trained models for a variety of languages and spaCy integrates well with deep learning frameworks like TensorFlow and PyTorch, which is advantageous for modern NLP tasks.
Ease of Use: Due to its flexibility and range of options, NLTK can be more complex to use, especially for beginners. Whereas spacy is User-Friendly and it has a more straightforward API and is easier to get started with, especially for common NLP tasks.
Community and Ecosystem: NLTK has been around longer, with a larger number of academic users and more extensive resources. Whereas spaCy has a rapidly growing community, especially in industry, and it benefits from regular updates and new features.
Summary:
Use NLTK if: You need a comprehensive, flexible toolkit for detailed NLP tasks, research, or learning.
Use spaCy if: You need a fast, production-ready solution with pre-trained models and an easy-to-use API for common NLP tasks.
Important Note : Each library has its strengths, so the choice between them depends on the specific needs of your project.

[schan]

I am both happy and sad. Happy that I discovered the channel today. Sad that I discovered this channel only today. I wish I discovered this channel 4 years ago. So the overall sentiment of my post is positive :)

[kmrjt]

Thanks for all the amazing information you keep sharing over youtube. Please keep up the excellent work, your data science knowledge is of great help to the community of aspiring data scientists.

[huepalette-eye]

I watched this video entirely, its very useful for me. I paid 55K for a data science and I am learning from here you are much better than any one there .

Thank you Krish 😍

[SambitSatapathy-eb]

Thank you for uploading such nice and comprehensive lectures (not videos) and explaining it so nicely. Your commitment is quite commendable. Please make such one shot videos in the future too.

[ushabathula]

Thank you for being a guiding light in the vast sea of information, providing clarity and understanding to those who seek knowledge. Your commitment to the betterment of individuals and society as a whole is truly uplifting.

[Mani_Ratnam]

Who needs institutes when krish sir is ready to give everyone this much free resources.

[aghazohaib]

NLTK is a comprehensive and educational toolkit suitable for a wide range of NLP tasks, while SpaCy is a focused and efficient library designed for production use, particularly for tasks like entity recognition and part-of-speech tagging.

[indukashyap]

Excellent Video, Just started with it, got clear about basics of NLP

[abdallahelray]

Aww, you did an incredible job, Krish! I was fully engaged for the entire 4 hours and didn't get bored once.

[harshmodi]

Upon reading the documentation and paper on CBOW, I have two questions -

1. You explained that when we chose a window size, for example 3, we take 3 consecutive words from the corpus and take the middle word as target word and words before and after (1 each in this case) to provide context for the target word. However the documentation says that the number of words we take as window size determines the number of words taken before and after the target word. So for example if we take window_size = 3, we take 3 words before and 3 words after the target word to provide context.

2. We can chose the hidden layer to be any size. It is not important that it matches the window size, since the input layer does average or sum of the input vector and hence it's size is always [1 x V] where V is the vocabulary size. The input-hidden layer matrix is on size [V x N] where N is the hidden layer size, and then the hidden-output layer matrix is of [N x V] and finally the output layer if [V x 1]

Can you please clarify my doubts here

[SaahilPriyaPrasannajeet]

Thanks for sharing and educating us. Keep it up.

[ujjawalagrawal]

Thankyou sir.. It was very useful to have it in single video.. All concepts were very clearly explained. God bless you sir.. I am 45 and trying to learn AI Ml😊

[fansclub]

Thankyou so much Krish Sir for this amazing video.

[Naveen-hygy]

Not sure if you will be reading these comments.. I really really like the way you teach.. its very informative and made it very easy to understand with the examples (giving the not so good ones first and then coming to the one which fixes all, this way gives the clear picture, also helps in clearing interview questions). very good narration, like a director of a movie narrating a story, which shows that you are so passionate in teaching and making others understand what you are explaining. And finally very very good voice. Thanks a lot KRISH NAIK SIR. Subscribed and waiting for more videos from you.

[fasiowaizahmed]

such a GREAT video on NLP. i just LOVED your explanation!! keep up the good work!

[lagnaray]

The difference between NLTK and spacy are:
1. NLTK supports various languages whereas spaCy have statistical models for 7 languages (English, German, Spanish, French, Portuguese, Italian, and Dutch). It also supports named entities for multi language.
2. NLTK is a string processing library. It takes strings as input and returns strings or lists of strings as output. Whereas, spaCy uses object-oriented approach. When we parse a text, spaCy returns document object whose words and sentences are objects themselves.
3. spaCy has support for word vectors whereas NLTK does not.
4. As spaCy uses the latest and best algorithms, its performance is usually good as compared to NLTK. In word tokenization and POS-tagging spaCy performs better, but in sentence tokenization, NLTK outperforms spaCy. Its poor performance in sentence tokenization is a result of differing approaches: NLTK attempts to split the text into sentences. In contrast, spaCy constructs a syntactic tree for each sentence, a more robust method that yields much more information about the text.

[aliasad]

NLTK:
Purpose: Teaching, research, and experimentation.
Ease of Use: More complex, requires manual setup.
Speed: Slower, prioritizes flexibility.
Models: No pre-trained models by default.
Data Handling: Uses Pythonic lists, trees.
Visualization: Basic, limited tools.
Learning Curve: Steeper for beginners.
Community: Strong in academia, research-focused.
spaCy:
Purpose: Industrial use, production-ready applications.
Ease of Use: Simple API, pre-built pipelines.
Speed: Fast, optimized with Cython.
Models: Provides pre-trained models out of the box.
Data Handling: Uses optimized objects like Doc, Token.
Visualization: Interactive, built-in tools like displaCy.
Learning Curve: Easier, beginner-friendly.
Community: Growing, production-focused ecosystem.

[DeekshithTN-eu]

Great video Krish much needed
Thank you so much, You are Awesome!!!!

[b.ayushpatil]

great guy, complete description for every term