للغة العربية NLP - Arabic Natural Language Processing
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Section 1: Introduction

lecture 01: Introduction
lecture 02: Installing Anaconda
lecture 03: NLP Applications
lecture 04: Course Structure
lecture 05: Course Requirements
Section 2: Text Preprocessing

Section_2_files.zip
lecture 06: Tokenization with NLTK
lecture 07: Tokenization with SPACY
lecture 08: Multi-Word Tokenization
lecture 09: Sentence Segmentation with SPACY
lecture 10: Sentence Segmentation with NLTK
lecture 11: POS Tagging (اعراب)
lecture 12: Stop Words SPACY
lecture 13: Stop Words NLTK
lecture 14: Stemming NLTK
lecture 15: Lemmatization NLTK
lecture 16: Lemmatization SPACY
lecture 17: Word Embedding / Word 2 Vector Part _ 1
lecture 18: Word Embedding / Word 2 Vector Part _ 2
lecture 19: Bag of Words
lecture 20: TF-IDF Explanation
lecture 21: TF-IDF Code
Section 3: Text Classification

Section_3_files.zip
lecture 22: Data Input
lecture 23: Data Cleaning Part 1
lecture 24: Data Cleaning Part 2
lecture 25: NLTK Processes: Tokenization, Stop Words removal
lecture 26: Data Analysis
lecture 27: Model Training
lecture 28: Support Vector Machine Classifier
lecture 29: Results Evaluation
Section 4: Chatbot-Classification Based

Section_4_files.zip
lecture 30: Deep Neural Network -intro.
lecture 31: Data Creation
lecture 32: Needed Libraries and Data Loading
lecture 33: Text data Preparation
lecture 34: Training data creation
lecture 35: Model Building Training
lecture 36: Chatbot Inference Program setup
lecture 37: Words Clean Up, Bag of Words
lecture 38: Chatbot testing
Section 5: Build Your Own ChatGPT

Section_5_files.zip
lecture 39: Transformer for ChatGPT
lecture 40: Load Data
lecture 41: Data Preprocessing
lecture 42: Training Data Creation
lecture 43: Transformer Model Setting
lecture 44: Training Result
Section 6: Build Your Own Translation Model

Section_6_files.zip
lecture 45: RNN_LSTM
lecture 46: seq_2_seq translation
lecture 47: Data Input
lecture 48: Input Data Preprocessing
lecture 49: Model Build Up- part1
lecture 50: Model Build Up- part2
lecture 51: Results Review part 1
lecture 52: Results Review part 2

lecture 17: Word Embedding / Word 2 Vector Part _ 1

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