Study 10-15 hrs/week and complete in 3 months.
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!
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.
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.
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.
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.
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This program requires experience with Python, statistics, machine learning, and deep learning.See detailed requirements.
Learn text processing fundamentals, including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model.Part of Speech Tagging
Learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures.Machine Translation
Learn voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks.Speech Recognizer
“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
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 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 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 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.
This program is the best and well coordinated i have seen so far. The program is very concise, gives equal weightage to theoretical concepts and their practical applications. I am a data scientist who recently got the opportunity to work in NLP and was looking for a program who can help and guide me moving into this unknown domain. The program duration was apt for me to plan my workload and concentrate on this program. Thanks to this program and udacity team that i am able to understand the concepts and adding value to my work.
Overall, great course and content. I learned a lot about RNNs, which I was really looking forward to. To make the program even better (at least for me), I would prefer having more mini projects , where one could practice building particular RNN architectures one by one to really get a detailed grasp of how they work and what their advantages and disadvantages are, before actually jumping into the course project.
A wonderful overview of NLP techniques. Good overview on state of the art techniques like LSTM, Attention. The last project (voice recognition) was an interesting application of what we learned.
Learn the essentials of natural language processing, including part-of-speech tagging, sentiment analysis, machine translation, and speech recognition.
This program offers a deep dive into modern Natural Language Process techniques. Mastering these skills will prepare you to build applications involving written and spoken language. We’ve collaborated with leading innovators such as IBM and Amazon to create our world-class curriculum, and you’ll learn from an instructor team comprised of experts from both Udacity and industry professionals. Massive growth is being predicted for the Natural Language Processing software market, making now the perfect time to enter this field.
In this program, you’ll develop and refine specialized skills in natural language processing and voice user interfaces. The curriculum is not designed to prepare you for a specific job; instead, the goal is that you’ll expand your skills in the natural language processing domain. Growth predictions are extremely high for this market, and having these in-demand skills will significantly enhance your ability to advance your AI career.
If your goal is to become an expert in Natural Language Processing (NLP), this program is ideal for you. Over the course of this program, you’ll become an expert in the main components of NLP, including speech recognition, sentiment analysis, and machine translation. You’ll learn cutting edge probabilistic and deep learning models, code them and train them on real data, and build a career-ready portfolio as an NLP expert!
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
To succeed in this Nanodegree program, we recommend you first take any course in Deep Learning equivalent to our Deep Learning Nanodegree program. You also need to be able to communicate fluently and professionally in written and spoken English.
Additionally, you should have the following knowledge:
Intermediate Python programming knowledge, including:
Basic shell scripting:
Basic statistical knowledge, including:
Intermediate differential calculus and linear algebra, including:
We have a number of courses and programs we can recommend that will help prepare you for the program, depending on the areas you need to address. For example:
The Natural Language Processing Nanodegree program is composed of one (1) Term of three (3) months. A Term has fixed start and end dates.
To graduate, students must successfully complete four (4) projects, each of which affords you the opportunity to apply and demonstrate new skills that you learn in the lessons. Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Please see the Udacity Nanodegree program FAQs found here for policies on enrollment in our programs.
The full program consists of one 3-month long term at a cost of USD 799, for a total program cost of USD 999.
Payment is due before the term begins.
If you would like to explore other applications for convolutional and recurrent neural networks, and have an interest in computer vision, then consider enrolling in the Computer Vision Nanodegree program. If you are looking for additional advanced topics in AI, the Robotics Engineer and Self-Driving Car Engineer Nanodegree programs could be ideal for you. And regardless of your future career destination, you’ll find that the Artificial Intelligence Nanodegree program is full of valuable content that will serve you well in almost any AI role.
You will need a computer running a 64-bit operating system (most modern Windows, OS X, and Linux versions will work) with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.5 and supporting packages. Your network should allow secure connections to remote hosts (like SSH). We will provide you with instructions to install the required software packages. Udacity does not provide any hardware or software.