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)
- Introduction
- Spacy Setup & Overview
- Spacy Basics
- Tokenization
- Stemming
- Lemmatization
- Stop Words
- Phrase Matching & Vocabulary
- Introduction
- Count Vectorizer
- Vector Similarity
- TF-IDF
- Word-to-Index Mapping
- Neural Word Embeddings
- Text Summarization
- Classification Matrix
- Confusion Matrix
- Scikit-Learner Primer
- Text Feature Extraction
- Text Summarization using Vectors
- TextRank
- Semantics and Word Vectors with Spacy
- Latent Semantic Analysis
- Latent Semantic Indexing
- Singular Value Decomposition
- Sentiment Analysis Overview
- Sentiment Analysis with NLTK
- 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?
- Forward Propagation
- Geometrical Picture
- Activation Functions
- Multiclass Classification
- Text Classification ANN in Tensorflow
- Text Preprocessing in Tensorflow
- Embeddings
- CBOW
- What is Convolution?
- Pattern Matching
- Weight Sharing
- CNNs for Text
- CNN for NLP in Tensorflow
- Simple RNN / Elman Unit
- RNNs: Paying Attention to Shapes
- GRU and LSTM
- RNN for Text Classification in Tensorflow
- Named Entity Recognitin (NER) in Tensorflow
- Decent Python programming skills
- Math parts: Linear algebra and probability are helpful