Skills you'll learn:
Machine translationAlexa skill creation
Natural Language Processing
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.
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
Last Updated November 5, 2024
Prerequisites:
Intermediate PythonNeural network basicsBasic probabilityObject-oriented programming basicsDeep learning framework proficiency
Advanced
2 months
Last Updated November 5, 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.
Course 2 • 2 weeks
Introduction 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
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.
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.
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: 4.5 Stars
309 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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