This course provides a solid foundation in Natural Language Processing (NLP) techniques. Learn text preprocessing, feature engineering, classification, and the basics of sequence modeling and word embeddings. Build practical NLP applications using common libraries and methods. Assumes familiarity with Python programming and foundational machine learning concepts.
Prerequisites Python & basic ML
Level:
Text Preprocessing
Apply advanced text cleaning and normalization techniques suitable for various NLP tasks.
Feature Engineering
Implement and understand feature extraction methods like TF-IDF and N-grams for text representation.
Text Classification
Build, train, and evaluate machine learning models for text classification problems.
Word Embeddings
Understand the concepts behind distributional semantics and apply techniques like Word2Vec and GloVe.
Sequence Modeling Basics
Grasp the fundamentals of sequence modeling using RNNs and LSTMs for text data.
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