Udacity | Natural Language Processing Nanodegree v1.0.0 [FCO]

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  • LanguageEnglish
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  • Last checkedApr. 13th '19
  • Date uploadedApr. 11th '19
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NANODEGREE PROGRAM–nd892

Advance Your Career as a Natural Language Processing Expert

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.

Why Take This Nanodegree Program?

Over the course of this program, you’ll become an expert in the main components of Natural Language Processing, including speech recognition, sentiment analysis, and machine translation. You’ll learn to code probabilistic and deep learning models, train them on real data, and build a career-ready portfolio as an NLP expert!

Why Take This Nanodegree Program?

The Natural Language Processing market is predicted to reach $22.3 billionby 2025

Work on the Most Cutting-Edge Applications

Work on the Most Cutting-Edge Applications

Natural Language Processing is at the center of the AI revolution, as it provides a tool for humans to communicate with computers effectively. The industry is hungry for highly-skilled specialists, and you’ll begin making an impact right away.

Focus on Putting Your Skills to Work

Focus on Putting Your Skills to Work

Master Natural Language Processing techniques with the goal of applying those techniques immediately to real-world challenges and opportunities. This is efficient learning for the innovative and career-minded professional AI engineer.

Code Your Own Models

Code Your Own Models

You’ll learn how to build and code natural language processing and speech recognition models in Python. You’ll complete three major natural language processing projects, and build a strong portfolio in the process.

Benefit From Personalized Project Reviews

The most effective way to learn is by having your code and solutions analyzed by AI experts who will give you powerful feedback in order to improve your understanding.

What You Will Learn

SYLLABUS

Start mastering Natural Language Processing!
Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation, and more!

SEE FEWER DETAILS

3 Months to complete

PREREQUISITE KNOWLEDGE

This program requires experience with Python, statistics, machine learning, and deep learning.See detailed requirements.

Introduction to Natural Language Processing

Learn text processing fundamentals, including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model.

PART OF SPEECH TAGGING

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.

MACHINE TRANSLATION

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.

SPEECH RECOGNIZER

NEED TO PREPARE?

We recommend our Deep Learning Nanodegree program as the perfect starting point for your deep learning education.

“This new era of systems is one that is not about programmes. They can talk or ingest natural language, they can understand what they read and they can help us make decisions about areas to explore and finding answers.”
— STEVE ABRAMS, VP, CHIEF DATA SCIENTIST, UNITED TECHNOLOGIES
Learn with the best
Luis Serrano

Luis Serrano

CURRICULUM LEAD

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.

Jay Alammar


INSTRUCTOR

Jay has a degree in computer science, loves visualizing machine learning concepts, and is 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.

Dana Sheahen


INSTRUCTOR

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.

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