Natural Language ProcessingSchool of Artificial Intelligence

Natural Language Processing Specialization Bootcamp

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About This Course

This program will enhance learners’ existing machine learning and deep learning skills with the addition of natural language processing and speech recognition techniques. These skills can be used in various applications such as part of speech tagging and machine translation, among others. Learners will develop the skills they need to start applying natural language processing techniques to real-world challenges and applications.

Certificate

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Requirements

  • 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.
  • Learners need access to a 64-bit operating system with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.5 and supporting packages. The network should allow secure connections to remote hosts (like SSH).

Curriculum

385 Lessons

Intro to NLP

NLP Overview1:12Preview
03. Structured Languages1:31
03. Structured Languages
04. Grammar0:35
04. Grammar
05. Unstructured Text1:17
05. Unstructured Text
06. Counting Words
07. Context Is Everything1:34
08. NLP and Pipelines0:48
09. How NLP Pipelines Work1:11
10. Text Processing1:56
11. Feature Extraction2:48
12. Modeling0:54

Text Processing

Spam Classifier with Naive Bayes

Part of Speech Tagging with HMMs

Project Part of Speech Tagging

Feature extraction and embeddings

Topic Modeling

Sentiment Analysis

Sequence to Sequence

Deep Learning Attention

RNN Keras Lab

Cloud Computing Setup Instructions

Project Machine Translation

Intro to Voice User Interfaces

(Optional) Alexa History Skill

Speech Recognition

Project DNN Speech Recognizer

Recurrent Neural Networks

Long Short-Term Memory Networks (LSTM)

Keras

Sentiment Prediction RNN

Embeddings and Word2Vec

Project Part of Speech Tagging

LLMs Module: Introduction to Large Language Models

LLMs Module: The Transformer Architecture

LLMs Module: Getting Started With GPT Models

LLMs Module: Hugging Face Transformers

LLMs Module: Question and Answer Models With BERT

LLMs Module: Text Classification With XLNet

LangChain Module: Introduction

LangChain Module: Tokens, Models, and Prices

LangChain Module: Setting Up the Environment

LangChain Module: The OpenAI API

LangChain Module: Model Inputs

LangChain Module: Message History and Chatbot Memory

LangChain Module: Output Parsers

LangChain Module: LangChain Expression Language (LCEL)

LangChain Module: Retrieval Augmented Generation (RAG)

LangChain Module: Tools and Agents

Vector Databases Module: Introduction

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)

nlp nd
Level
Intermediate
Lectures
385 lectures
Subject
Natural Language ProcessingSchool of Artificial Intelligence

Material Includes

  • Workspaces
  • Hands-on Projects
  • Quizzes
  • Progress Tracker

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