Applying Word Embeddings in Natural Language Processing

The course is for beginner and enthusiastic intermediate learners of natural language processing.

This course aims to teach you various Word Embedding techniques and you will build a classification model using text data.

A sound understanding of Deep Learning with basic knowledge of LSTM

• The basics of Natural Language Processing such as Bag of Words, TF-IDF, etc.
• Word Embeddings and their advantages in the real world
• Word Embeddings algorithms:
  • Word2Vec – Continuous Bag of Words and SkipGram Models
  • GloVe
  • FastText
• Word Embeddings intrinsic vs. extrinsic evaluations
• Word Embedding Layer with an LSTM model
• A case study on text classification with text data represented as Word Embedding Vectors