NLP with vector spaces

Sentiment analysis with logistic regression, Sentiment analysis with naive bayes, Vector space models, Machine translati

Udemy
platform
English
language
Data Science
category
instructor
NLP with vector spaces
288
students
33 mins
content
May 2022
last update
FREE
regular price

What you will learn

Advance knowledge at NLP

Understand NLP

Advance knowledge at DL

Understand DL

Description

I am Nitsan Soffair, A Deep RL researcher at BGU.

In this course you will learn NLP with vector spaces.

You will

  1. Get knowledge of

    1. Sentiment analysis with logistic regression

    2. Sentiment analysis with naive bayes

    3. Vector space models

    4. Machine translation and document search

  2. Validate knowledge by answering a quiz by the end of each lecture

  3. Be able to complete the course by ~2 hours.

Syllabus

  1. Sentiment analysis with logistic regression

    1. Supervised ML

    2. Feature extraction

    3. Logistic regression

  2. Sentiment analysis with naive bayes

    1. Bayes rule

    2. Laplacian smoothing

  3. Vector space models

    1. Euclidean distance

    2. Cosine similarity

    3. PCA

  4. Machine translation and document search

    1. Word vectors

    2. K-nearest neighbours

    3. Approximating NN

  5. Additional content

    1. GPT-3

    2. DALL-E

    3. CLIP

Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers (such as index terms). It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System.

Supervised learning (SL) is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias). This statistical quality of an algorithm is measured through the so-called generalization error.

The parallel task in human and animal psychology is often referred to as concept learning.


Resources

  • Wikipedia

  • Coursera

Content

Sentiment analysis with logistic regression

Supervised ML
Supervised ML
Feature extraction
Feature extraction
Logistic regression
Logistic regression

Sentiment analysis with naive bayes

Bayes rule
Bayes rule
Laplacian smoothing
Laplacian smoothing

Vector space models

Cosine similarity
Cosine similarity
PCA
PCA

Machine translation and document search

Word vectors
Word vectors
K-nearest neighbours
K-nearest neighbours
Approximating NN
Approximating NN

Additional content

GPT-3
DALL-E
CLIP
4704796
udemy ID
5/26/2022
course created date
6/3/2022
course indexed date
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course submited by