Natural Language Processing with Classification and Vector Spaces

 My first accomplishment in the new #NLP specialization by on Coursera:

"Natural Language Processing with Classification and Vector Spaces"


Although I already knew much of the contents taught in this certification, I was able to learn some new practical implementation tips and some new interesting concepts; in particular, I

#sentiment_analysis under the supervised learning paradigm, logistic regression, data pre-processing, and feature extraction,
- Error analysis, conditional probabilities. Bayes rule, Naive Bayes inference, log-likelihood optimization, and Laplace smoothing.
- Covariance and co-occurrence matrices, vector representations, similarity, vector spaces, and dimensionality reduction through #PCA.

- Hash functions and tables in NLP, approximate #KNN, locality sensitive hashing, document search, and one basic approach to #machine_translation.

Coming from a more mathematical background, I was slightly disappointed at the mathematical rigorousness, but from a practical industry point of view, I found this to be an excellent accelerated introduction. Definitely recommend!

#machinelearning #nlp #coursera #deeplearningai #ai #datascience


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