Study 10-15 hrs/week and complete in 3 months.
Classroom opens 7 days after enrollment closes
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.