| 1 |
To implement text preprocessing techniques such as tokenization, case folding, stemming, lemmatization, stop word removal and calculate the edit distance between text strings. |
| 2 |
To construct a Part-of-Speech (POS) tagger using the Hidden Markov Model (HMM) and implement the Viterbi algorithm to decode the most probable sequence of tags for a given sentence. |
| 3 |
To implement word embedding using TF-IDF, Bag of Words, Word2Vec and Glove techniques for a given sentence. |
| 4 |
To implementing n-gram language model with Lapalace smooting |
| 5 |
Introduction to Named Entity Recognition (NER) and Its Implementation Using NLTK. |
| 6 |
To implement review classification on IMDB dataset using RNN. |
| 7 |
To implementing review classification on IMDB dataset using LSTM. |
| 8 |
To construct Context-Free Grammars (CFGs)to generate sentences applying top-down and bottom-up parsing algorithms for syntactic analysis. |
| 9 |
To implement Probabilistic Context-Free Grammars (PCFGs) for statistical parsing and explore dependency parsing using graph-based and transition-based methods for syntactic analysis. |
| 10 |
To build a Neural Machine Translation model using sequence-to-sequence (Seq2Seq) architecture with attention mechanisms. See the Tutorials |