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NLP with AI
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Data Science
NLP with AI
NLP with AI - Basic
Duration in Hours (4 Hours session) : 60
Duration in Days: 15
DS05 NLP with AI
Course Code: NPLB36
40 Hours of Theory
20 Hours of Assignment (Lab)
Natural Language Processing In AI (NLP)
1. NLP Concepts
Introduction
Spacy Setup & Overview
Spacy Basics
Tokenization
Stemming
Lemmatization
Stop Words
Phrase Matching & Vocabulary
2. Vector Models and Text Preprocessing
Introduction
Count Vectorizer
Vector Similarity
TF-IDF
Word-to-Index Mapping
Neural Word Embeddings
Text Summarization
3. Text Classification
Classification Matrix
Confusion Matrix
Scikit-Learner Primer
Text Feature Extraction
Text Summarization using Vectors
TextRank
4. Semantic and Sentiment Analysis
Semantics and Word Vectors with Spacy
Latent Semantic Analysis
Latent Semantic Indexing
Singular Value Decomposition
Sentiment Analysis Overview
Sentiment Analysis with NLTK
5. Deep Learning for NLP
The Basic Perceptron Model
Introduction to Neural Networks
Keras Basics
Neuron Introduction
Fitting a Line
Classification Code Preparation
Text Classification in Tensorflow
How does a model learn?
6. FeedforwardArtification Neural Networks
Forward Propagation
Geometrical Picture
Activation Functions
Multiclass Classification
Text Classification ANN in Tensorflow
Text Preprocessing in Tensorflow
Embeddings
CBOW
7. Convolutional Neural Networks
What is Convolution?
Pattern Matching
Weight Sharing
CNNs for Text
CNN for NLP in Tensorflow
8. Recurrent Neural Networks
Simple RNN / Elman Unit
RNNs: Paying Attention to Shapes
GRU and LSTM
RNN for Text Classification in Tensorflow
Named Entity Recognitin (NER) in Tensorflow
Pre-Requirements
Decent Python programming skills
Math parts: Linear algebra and probability are helpful
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