Collaborators

Reviewers

- Learning Objectives
- This shortlist will help user the basic concepts of vector semantics, word and vectors, embedding. In addition learn types vector space models namely the tf-idf and Word2vec.
- Potential Use Cases
- Vector space models are used in information filtering, information retrieval, plagiarism detection, news recommender system, comparing, indexing and relevancy rankings of text documents.
- Target Audience
- BEGINNERData Scientist new to NLP

Go through the following **annotated items** *in order*:

VIDEO 1. Vector space model

- Quick introduction to vector space model and basic technical concepts

10 minutes

BOOK_CHAPTER 2. TF-IDF

- Technical concepts explained with examples such as term frequency, cosine similarity, inverse document frequency

30 minutes

ARTICLE 3. IF-IDF implementation in python

- How to implement TF-IDF embedding with NLTK in python?

20 minutes

BOOK_CHAPTER 4. Word2Vec

- This section from chapter clearly explains dense semantic model and how Word2Vec model works?

20 minutes

ARTICLE 5. Word2Vec Implementation

- Word2Vec model is implemented from scratch which can help to learn and understand concept clearly.

9 minutes

VIDEO 6. Vector Semanics

- Clear explanation of basic concepts such as word meaning, vectors and embedding with simple examples

15 minutes

BOOK_CHAPTER 7. Lexical Semantics

- Covers basic principles of word meaning required to understand vector semantics, embedding and vector space model

10 minutes

Previewing stream ** Natural Language Processing**

Upcoming Live Sessions

Videos

Learning Packages

Past Capstones

People

Search for a tag: