Natural Language Processing Bootcamp

A well-prepared learner should have significant experience with Python and entry-level experience with probability, statistics, and deep learning architectures.

Learners should also have the ability to write a class in Python and add comments to their code for others to read.

Lastly, learners should have familiarity with the term “neural networks” and the differential math that
drives backpropagation.

Learn text processing fundamentals including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model.

Course Project

Part of Speech Tagging

Use several techniques, including table lookups, n-grams, and hidden Markov models, to tag parts of speech
in sentences, and compare their performance. This project demonstrates text processing techniques that
allow one to build a part of speech tagging model. Work with a simple lookup table and progressively add
more complexity to improve the model using probabilistic graphical models. Use a Python package to build
and train a tagger with a hidden Markov model, and compare the performances of all these models in a
data set of sentences

Learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures.

Course Project

Machine Translation
Build a deep neural network that functions as part of an end-to-end machine translation pipeline. The completed pipeline will accept English text as input and return the French translation. Be able to explore several recurrent neural network architectures and compare their performance. Pre-process the data by converting text to sequence of integers. Build several deep learning models for translating the text into French. Run this models on English test to analyze their performance.

Learn voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks.

Course Project

Build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR pipeline. The model will convert raw audio into feature representations, which will then turn them into transcribed text. Begin by investigating a data set that will be used to train and evaluate the models.

Convert any raw audio to feature representations that are commonly used for ASR. Build neural networks that map these features to transcribed text.

Vector Databases Module: Basics of Vector Space and High-Dimensional Data
Vector Databases Module: Introduction to The Pinecone Vector Database
Vector Databases Module: Semantic Search with Pinecone and Custom (Case Study)