Natural Language Processing

Name: Natural Language Processing Nanodegree Program
Rating: 4.75 (275 reviews)

Nanodegree Program

Master the skills to get computers to understand, process, and manipulate human language. Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.

Advanced

2 months

Real-world Projects

Completion Certificate

Last Updated September 13, 2024

Skills you'll learn:

Machine translation • Alexa skill creation • Attention mechanisms • Fasttext

Prerequisites:

Intermediate Python • Neural network basics • Basic probability

Courses In This Program

Course 1 • 40 minutes

Welcome to Natural Language Processing

This section provides an overview of the program and introduces the fundamentals of Natural Language Processing through symbolic manipulation, including text cleaning, normalization, and tokenization. You'll then build a part of speech tagger using hidden Markov models.

Lesson 1

Welcome to Natural Language Processing

Welcome to the Natural Language Processing Nanodegree program!

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Lesson 1

Welcome to Natural Language Processing

Welcome to the Natural Language Processing Nanodegree program!

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Course 2 • 2 weeks

Introduction to Natural Language Processing

Lesson 1

Intro to NLP

Arpan will give you an overview of how to build a Natural Language Processing pipeline.

Lesson 2

Text Processing

Learn to prepare text obtained from different sources for further processing, by cleaning, normalizing and splitting it into individual words or tokens.

Lesson 3

Spam Classifier with Naive Bayes

In this section, you'll learn how to build a spam email classifier using the naive Bayes algorithm.

Lesson 4

Part of Speech Tagging with HMMs

Learn Hidden Markov Models, and apply them to part-of-speech tagging, a very popular problem in Natural Language Processing.

Lesson 5 • Project

Project: Part of Speech Tagging

In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.

Lesson 6

(Optional) IBM Watson Bookworm Lab

Learn how to build a simple question-answering agent using IBM Watson.

Lesson 1

Intro to NLP

Arpan will give you an overview of how to build a Natural Language Processing pipeline.

Lesson 2

Text Processing

Learn to prepare text obtained from different sources for further processing, by cleaning, normalizing and splitting it into individual words or tokens.

Lesson 3

Spam Classifier with Naive Bayes

In this section, you'll learn how to build a spam email classifier using the naive Bayes algorithm.

Lesson 4

Part of Speech Tagging with HMMs

Learn Hidden Markov Models, and apply them to part-of-speech tagging, a very popular problem in Natural Language Processing.

Lesson 5 • Project

Project: Part of Speech Tagging

In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.

Lesson 6

(Optional) IBM Watson Bookworm Lab

Learn how to build a simple question-answering agent using IBM Watson.

Course 3 • 1 month

Computing With Natural Language

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

Lesson 1

Introduction to Computing With Natural Language

An introduction of the course outline and prerequisite.

Lesson 2

Feature extraction and embeddings

Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.

Lesson 3

Topic Modeling

In this section, you'll learn to split a collection of documents into topics using Latent Dirichlet Analysis (LDA). In the lab, you'll be able to apply this model to a dataset of news articles.

Lesson 4

Sentiment Analysis

Learn about using several machine learning classifiers, including Recurrent Neural Networks, to predict the sentiment in text. Apply this to a dataset of movie reviews.

Lesson 5

Sequence to Sequence

Here you'll learn about a specific architecture of RNNs for generating one sequence from another sequence. These RNNs are useful for chatbots, machine translation, and more!

Lesson 6

Deep Learning Attention

Attention is one of the most important recent innovations in deep learning. In this section, you'll learn attention, and you'll go over a basic implementation of it in the lab.

Lesson 7

RNN Keras Lab

This section will prepare you for the Machine Translation project. Here you will get hands-on practice with RNNs in Keras.

Lesson 8 • Project

Project: Machine Translation

Apply the skills you've learned in Natural Language Processing to the challenging and extremely rewarding task of Machine Translation.

Lesson 1

Introduction to Computing With Natural Language

An introduction of the course outline and prerequisite.

Lesson 2

Feature extraction and embeddings

Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.

Lesson 3

Topic Modeling

In this section, you'll learn to split a collection of documents into topics using Latent Dirichlet Analysis (LDA). In the lab, you'll be able to apply this model to a dataset of news articles.

Lesson 4

Sentiment Analysis

Learn about using several machine learning classifiers, including Recurrent Neural Networks, to predict the sentiment in text. Apply this to a dataset of movie reviews.

Lesson 5

Sequence to Sequence

Here you'll learn about a specific architecture of RNNs for generating one sequence from another sequence. These RNNs are useful for chatbots, machine translation, and more!

Lesson 6

Deep Learning Attention

Attention is one of the most important recent innovations in deep learning. In this section, you'll learn attention, and you'll go over a basic implementation of it in the lab.

Lesson 7

RNN Keras Lab

This section will prepare you for the Machine Translation project. Here you will get hands-on practice with RNNs in Keras.

Lesson 8 • Project

Project: Machine Translation

Apply the skills you've learned in Natural Language Processing to the challenging and extremely rewarding task of Machine Translation.

Course 4 • 3 weeks

Communicating with Natural Language

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

Lesson 1

Course Introduction

Introduce the course outline and the course prerequisite

Lesson 2

Intro to Voice User Interfaces

Get acquainted with the principles and applications of VUI, and get introduced to Alexa skills.

Lesson 3

(Optional) Alexa History Skill

Build your own Alexa skill and deploy it!

Lesson 4

Speech Recognition

Learn how an automatic speech recognition (ASR) pipeline works.

Lesson 5 • Project

Project: DNN Speech Recognizer

Build a deep neural network that functions as part of an end-to-end automatic speech recognition pipeline.

Lesson 1

Course Introduction

Introduce the course outline and the course prerequisite

Lesson 2

Intro to Voice User Interfaces

Get acquainted with the principles and applications of VUI, and get introduced to Alexa skills.

Lesson 3

(Optional) Alexa History Skill

Build your own Alexa skill and deploy it!

Lesson 4

Speech Recognition

Learn how an automatic speech recognition (ASR) pipeline works.

Lesson 5 • Project

Project: DNN Speech Recognizer

Build a deep neural network that functions as part of an end-to-end automatic speech recognition pipeline.

Taught By The Best

Jay Alammar

Instructor

Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.

Arpan Chakraborty

Instructor

Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.

Luis Serrano

Instructor

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Dana Sheahen

Content Developer

Dana is an electrical engineer with a Masters in Computer Science from Georgia Tech. Her work experience includes software development for embedded systems in the Automotive Group at Motorola, where she was awarded a patent for an onboard operating system.

Ratings & Reviews

Average Rating: 4.75 Stars

275 Reviews

Qu R.

March 20, 2023

so far so good!

Leonardo F.

December 26, 2022

great!

Navneet ..

October 10, 2022

contents are explanatory and lot of reference materials provided.

Ashish K.

October 3, 2022

Very useful.

Calvin K.

July 21, 2022

It's a bit too easy and would love to see more context about POS tagging. But overall the experience is pretty great.

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