Natural Language Processing

Welcome to Natural Language Processing

Welcome to the Natural Language Processing Nanodegree program!

Getting Help

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

Get Help with Your Account

What to do if you have questions about your account or general questions about the program.

Jobs in NLP

Learn about common jobs in natural language processing, and get tips on how to stay active in the community.

Intro to NLP

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

Text Processing

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

Spam Classifier with Naive Bayes

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

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.

Project: Part of Speech Tagging

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

(Optional) IBM Watson Bookworm Lab

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

Introduction to Computing With Natural Language

An introduction of the course outline and prerequisite.

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.

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.

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.

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!

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.

RNN Keras Lab

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

Project: Machine Translation

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

Course Introduction

Introduce the course outline and the course prerequisite

Intro to Voice User Interfaces

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

(Optional) Alexa History Skill

Build your own Alexa skill and deploy it!

Speech Recognition

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

Project: DNN Speech Recognizer

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

Recurrent Neural Networks

Ortal will introduce Recurrent Neural Networks (RNNs), which are machine learning models that are able to recognize and act on sequences of inputs.

Long Short-Term Memory Networks (LSTM)

Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.

Hyperparameters

Learn about a number of different hyperparameters that are used in defining and training deep learning models. We'll discuss starting values and intuitions for tuning each hyperparameter.

Keras

In this section you'll get a hands-on introduction to Keras. You'll learn to apply it to analyze movie reviews.

Sentiment Analysis with Andrew Trask

Sentiment Prediction RNN

Implement a sentiment prediction RNN

TensorFlow

In this section you'll get a hands-on introduction to TensorFlow, Google's deep learning framework, and you'll be able to apply it on an image dataset.

Embeddings and Word2Vec

In this lesson, you'll learn about embeddings in neural networks by implementing the word2vec model.

Introduction to PyTorch

Learn how to use PyTorch to build and train deep neural networks. By the end of this lesson, you will build a network that can classify images of dogs and cats with state-of-the-art performance.

Embeddings & Word2Vec

In this lesson, you'll learn about embeddings in neural networks by implementing the Word2Vec model.

Implementation of RNN & LSTM

Learn how to represent memory in code. Then define and train RNNs in PyTorch and apply them to tasks that involve sequential data.

Deploying PyTorch Models

In this lesson, we'll walk through a tutorial showing how to deploy PyTorch models with Torch Script.

Python Regular Expression

BeautifulSoup Library

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Optimize Your GitHub Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.