Overview

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Embark on a comprehensive 10-hour journey into Natural Language Processing (NLP) and Deep NLP, delivered in Hindi and Urdu. Learn fundamental concepts, advanced techniques, and practical applications of NLP from scratch. Explore topics such as tokenization, lemmatization, part-of-speech tagging, named entity recognition, sentiment analysis, and text classification. Dive into machine learning models for NLP, including logistic regression, SVM, and naive Bayes. Discover word embeddings, topic modeling, and deep learning approaches like RNNs and LSTMs. Gain hands-on experience with popular NLP libraries such as NLTK, spaCy, and Keras. Perfect for beginners and professionals looking to enhance their AI and NLP skills, this course provides a solid foundation for building intelligent language processing systems.

Syllabus

Tutorial 01: Natural Language Processing using Python in A.I | Basic of NLP and Types Applications.
Tutorial 02: Natural language Understanding and Generation How NLP Works in Artificial intelligence.
Tutorial 03: Tokenization in NLP using Python and Spacy | Word and Sentence Tokenization Explained.
Tutorial 04: Lemmatization in NLP using Python in Urdu|Tokenization vs lemmatization in NLP in Hindi.
Tutorial 05: Stopwords in Natural Language Processing Hindi/Urdu | How to Add and Remove Stopwords.
Tutorial 06: Vocabulary and Phrase Matching in NLP Python Hindi/Urdu | nlp.vocab Phrasematcher.
Tutorial 07: POS Tagging and Visualizing in NLP Hindi | View token tags | Fine grained tags|POS tags.
Tutorial 08: Name Entity Recognition in NLP using Spacy | NER Tags Noun-chunk entity annotation NLP.
Tutorial 09: Sentence Segmentation in NLP with Deep NLP|Sentence Segmentation IN SPACY with own rule.
Tutorial 10: Stemming in NLP using Spacy NTLK | Stemming Porter Stemming Snowball Stemming in NLP.
Tutorial 11: Gentle Introduction of Bag of words in NLP with Deep NLP using Python in Hindi / Urdu.
Tutorial 12: Feature Extraction in NLP using Document Frequency Inverse Document Frequency, TF-IDF.
Tutorial 13: Hashing with HashingVectorizer in NLP | What is hashingvectorizer in NLP using python.
Tutorial 14: N-grams in NLP with basic example in Python |bigram unigram trigram in NLP using python.
Tutorial 15: How to Prepare Text Data with KERAS in NLP | Data Cleaning in NLP using KERAS.
Tutorial 16: Part 1 - Text Classification in NLP using Machine Learning (Logistic Regression) Python.
Tutorial 17: Part 2 - Logistic Regression in NLP using countvectorizer, tfidfvectorizer, pipeline.
Tutorial 18: Text Classification using SVM classifier including tfidfvectorizer and pipeline in NLP.
Tutorial 19: Text classification using K Nearest Neighbors including tfidfvectorizer and pipeline.
Tutorial 20: Text classification Naive Bayes Multinomial Classifier tfidfvectorizer and pipeline.
Tutorial 21: Sentiment/Text Classification for Restaurant Reviews using Machine Learning models.
Tutorial 22: Word Embedding using Word2Vec | Word2Vec using Continuous bag-of-words CBOW, Skip Gram.
Tutorial 23: word2vec using Gensim and Spacy with FREE [SOURCE CODE] | word embedding vs word2vec.
Tutorial 24: Sentiment Analysis of Amazon Reviews using NLTK VADER MODULE PYTHON with [SOURCE CODE].
Tutorial 25: Topic Modeling using Latent Dirichlet Allocation (LDA) Theory | LDA TOPIC MODELING.
Tutorial 26: [PRACTICAL] - Topic Modeling using Latent Dirichlet Allocation | TOPIC MODELING Python.
Tutorial 27: Topic Modeling using NMF using Sklearn 20 Newsgroups |Non-negative matrix factorization.
Tutorial 28:Topic Modeling using non negative matrix factorization NMF NATIONAL PUBLIC RADIO dataset.
Tutorial 29: NLP Deep Learning | Perceptron | Activation Function | Artificial Neural Network NLP.
Tutorial 30: Keras Basic for NLP | TensorFlow Keras Classification using IRIS dataset [ANN].
Tutorial 31: Stanza NLP Toolkit for Urdu Hindi and English Language | NLP for Urdu/Hindi Language.
Tutorial 32: iNLTK -Natural Language Toolkit for Indic Languages| iNLTK with Python Fahad Hussain CS.
Tutorial 33: Text Generation using Recurrent Neural Network Python Keras | RNN vs LSTM vs GRU.