Free Natural Language Processing Tutorial – NLP with vector spaces
Learn NLP with vector spaces in this comprehensive course by Nitsan Soffair. Master sentiment analysis, machine translation, and more in just 2 hours. Perfect for anyone interested in AI and NLP.
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
Who this course is for:
- Anyone intersted in NLP
- Anyone intersted in AI
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