Natural Language Processing Nanodegree v5.0.0

Natural Language Processing Nanodegree

Nanodegree key: nd892

Version: 5.0.0

Locale: en-us

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.

Content

**Part 01 (FreePreview):** Welcome to Natural Language Processing

Module 01: Welcome to Natural Language Processing
- Lesson 01: Welcome to Natural Language Processing
  Welcome to a Free Preview of the Natural Language Processing Nanodegree program! Come and take a sneak peek at what our program offers.

**Part 02 (FreePreview):** Building an NLP Pipeline

Module 01: Building an NLP Pipeline
- Lesson 01: Building an NLP Pipeline
  Learn about text processing, feature extraction, and part-of-speech tagging.

**Part 03 (FreePreview):** Voice User Interfaces

Module 01: Voice User Interfaces
- Lesson 01: Voice User Interfaces
  Learn about phonetics, acoustic models, and deep neural networks.

**Part 04 (FreePreview):** What's Next?

Module 01: What's Next?
- Lesson 01: What's Next?
  Here's how to keep building your NLP expertise!
  - Concept 01: Ready to Level Up with NLP?

**Part 05 (FreePreview):** Project

Module 01: Project
- Lesson 01: Project: Part of Speech Tagging
  In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.
  
  Project Description - Part of Speech Tagging
  
  Project Rubric - Part of Speech Tagging
  - Concept 01: Introduction

Part 06 : 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.

Module 01: Introduction to the Nanodegree
- Lesson 01: Welcome to Natural Language Processing
  Welcome to the Natural Language Processing Nanodegree program!
  - Concept 01: Welcome to the Natural Language Processing Nanodegree
  - Concept 02: Community Guidelines
- Lesson 02: Knowledge, Community, and Careers
  You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
- Lesson 03: Get Help with Your Account
  What to do if you have questions about your account or general questions about the program.
  - Concept 01: FAQ
  - Concept 02: Support
- Lesson 04: Intro to NLP
  Arpan will give you an overview of how to build a Natural Language Processing pipeline.
- Lesson 05: 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 06: Spam Classifier with Naive Bayes
  In this section, you'll learn how to build a spam e-mail classifier using the naive Bayes algorithm.
- Lesson 07: Part of Speech Tagging with HMMs
  Luis will give you an overview of several part-of-speech tagging, including a deeper dive on hidden Markov models.
- Lesson 08: Project: Part of Speech Tagging
  In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.
  
  Project Description - Part of Speech Tagging
  
  Project Rubric - Part of Speech Tagging
- Lesson 09: (Optional) IBM Watson Bookworm Lab
  Learn how to build a simple question-answering agent using IBM Watson.
Module 02: Career Services
- Lesson 01: Nanodegree Career Services
  The Careers team at Udacity is here to help you move forward in your career - whether it's finding a new job, exploring a new career path, or applying new skills to your current job.
  - Concept 01: Access the Career Portal
- Lesson 02: Jobs in NLP
  Learn about common jobs in natural language processing, and get tips on how to stay active in the community.
- Lesson 03: 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.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile

Part 07 : Computing with Natural Language

Module 01: Computing with Natural Language
- Lesson 01: 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 02: 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 03: 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 04: 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 05: 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 06: 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 07: Project: Machine Translation
  Apply the skills you've learnt in Natural Language Processing to the challenging and extremely rewarding task of Machine Translation. Bonne chance!
  
  Project Description - Project: Machine Translation
  
  Project Rubric - Project: Machine Translation
Module 02: Career Services
- Lesson 01: 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.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile

Part 08 : Communicating with Natural Language

Module 01: Communicating in Natural Language
- Lesson 01: Intro to Voice User Interfaces
  Get acquainted with the principles and applications of VUI, and get introduced to Alexa skills.
- Lesson 02: (Optional) Alexa History Skill
  Build your own Alexa skill and deploy it!
- Lesson 03: Speech Recognition
  Learn how an ASR pipeline works.
- Lesson 04: Project: DNN Speech Recognizer
  Build a deep neural network that functions as part of an end-to-end automatic speech recognition pipeline.
  
  Project Description - Project: DNN Speech Recognizer
  
  Project Rubric - Project: DNN Speech Recognizer

**Part 09 (Elective):** Recurrent Neural Networks

Module 01: Recurrent Neural Networks
- Lesson 01: 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.
- Lesson 02: 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.
- Lesson 03: 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.

**Part 10 (Elective):** Keras

Module 01: Keras
- Lesson 01: Keras
  In this section you'll get a hands-on introduction to Keras. You'll learn to apply it to analyze movie reviews.

**Part 11 (Elective):** Sentiment Analysis Extras

Module 01: Sentiment Analysis
- Lesson 01: Sentiment Analysis with Andrew Trask
- Lesson 02: Sentiment Prediction RNN
  Implement a sentiment prediction RNN

**Part 12 (Elective):** TensorFlow

Module 01: TensorFlow
- Lesson 01: 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.

**Part 13 (Elective):** Embeddings and Word2Vec

Module 01: Embeddings and Word2Vec
- Lesson 01: Embeddings and Word2Vec
  In this lesson, you'll learn about embeddings in neural networks by implementing the word2vec model.

**Part 14 (Elective):** PyTorch

Module 01: Introduction to PyTorch
- Lesson 01: 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.
- Lesson 02: Embeddings & Word2Vec
  In this lesson, you'll learn about embeddings in neural networks by implementing the Word2Vec model.
- Lesson 03: 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.
- Lesson 04: Deploying PyTorch Models
  In this lesson, we'll walk through a tutorial showing how to deploy PyTorch models with Torch Script.

**Part 15 (Elective):** Additional Text Preprocessing