# NLP with vector spaces

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

## 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

Get knowledge of

Sentiment analysis with logistic regression

Sentiment analysis with naive bayes

Vector space models

Machine translation and document search

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

Be able to complete the course by ~

**2 hours**.

**Syllabus**

Sentiment analysis with logistic regression

Supervised ML

Feature extraction

Logistic regression

Sentiment analysis with naive bayes

Bayes rule

Laplacian smoothing

Vector space models

Euclidean distance

Cosine similarity

PCA

Machine translation and document search

Word vectors

K-nearest neighbours

Approximating NN

Additional content

GPT-3

DALL-E

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