Nanodegree Program

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.

ENROLLMENT CLOSING IN

  • Time
    3 months

    Study 10 hrs/week and complete in 3 months

  • Classroom Opens
    15 January 2019
In Collaboration with
  • Amazon Alexa
  • IBM Watson

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 billion by 2025

1000+ jobopportunities created
100+ Hiring partnersonboard
₹ 40 lac+ Highestannual CTC offered
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Work on the Most Cutting-Edge Applications
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

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

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

Download Syllabus
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!

Work on a variety of natural language processing techniques. Build models using probabilistic and deep learning techniques and apply them to 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
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
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
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
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.

Student Reviews

4.2

(44)

5 stars
23
52.3%
4 stars
12
27.3%
3 stars
5
11.4%
2 stars
1
2.3%
1 stars
3
6.8%
Shukhrat K.

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.

Nidhin P.

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.

Svetoslav K.

Excellent course!

Hieu M.

awesome

Daniel C.

TL;DR: Enroll in this Program! I enjoyed completing the Udacity Natural Language Processing Nanodegree Program. There are three required projects. Project difficulty ranges from easy to somewhat challenging, depending on your prior experience. Hands-on projects are the most valuable aspect of this Program. When you start the first module, you might wonder why the Program is so expensive. After all, you can find free course materials that covers most of these topics elsewhere on the Web. For me this Program is worth it, and I came up with three reasons that justify the high cost. First, the projects really help you learn the concepts. One caveat is that you should allocate more time than the estimated completion time for all projects. For example, the Automatic Speech Recognition project has a 5 hour estimated completion time. Well, training all the required models takes at least 8 hours on AWS g3.4xlarge EC2 instance, NOT including implementation, testing, and debugging time. Second, the build-in Jupyter workspaces are nice; if you choose, you can submit project within the Udacity workspaces. In addition, the Machine Translation project actually offers the feature to switch between CPU and GPU workspaces. GPU cloud instances can be expensive, and Udacity allocated a generous number of hours for the Machine Translation project. NOTE: The Automatic Speech Recognition project DOESN"T offer any Jupyter workspaces. However, knowing how to setup AWS GPU workspaces from scratch is a valuable skill. I recommend that you budget between 30 to 50 USD to provision your own AWS GPU instances. Finally, the Code Reviews are helpful. The reviewers always pinpoint deficiencies in the submissions. In all cases when I needed to resubmit, it was because I didn't read the instructions carefully. Some reviewers offer more suggestions and recommend more optional improvements than others, so I guess luck has something to do with how much you benefit from Code Reviews. As with all learning, how much you get back is proportional to how much time and effort you invest. I also recommend taking advantage of the optional Extracurricular Modules to maximize value you gain from this Program.

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Natural Language Processing
₹49900*

Introductory Price

Learn the essentials of natural language processing, including part-of-speech tagging, sentiment analysis, machine translation, and speech recognition.

Admissions Closes: 16 January, 2019

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FAQ

    Program Highlights
  • Why should I enroll in this program?
    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.
  • Does this program include Deep Learning for NLP tasks?
    Yes! There are lessons covering Recurrent Neural Network (RNN) models for sequential data tasks like Automatic Machine Translation (AMT), using different architectures like Convolutional Neural Networks (CNNs) or bi-directional RNNs for Automatic Speech Recognition (ASR), and adding attention mechanisms to your Deep Learning models.
  • Does this program cover any “classical” NLP techniques?
    Yes! The lessons cover many “classical” topics including Hidden Markov Models (HMMs) for tasks like part of speech tagging, Statistical Language Models (SLMs) that are used to increase accuracy in language translation and recognition tasks, and Term Frequency-Inverse Document Frequency (tf-idf) for information retrieval systems.
  • What jobs will this program prepare me for?
    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.
  • What software and versions will I need in this program?
    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.
  • I've graduated from the Natural Language Nanodegree program, but I want to keep learning. Where should I go from here?
    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.
    Enrollment
  • Can I enroll in the program at any time?
    Yes! We admit students on a rolling basis, and you will automatically be added to the next available term once you've successfully enrolled. Depending on when you enroll, your term may start as late as four weeks after your enrollment date.
  • Once I am enrolled, when does the content become available?
    When you enroll, you are automatically added to the next available term. Every term has a fixed start date, and content becomes available on that date.
  • Can I enter the classroom prior to the start of my term?
    Yes, but you won't be able to access the content, as it stays locked until your term begins. In the classroom, you'll see a countdown to your term's start date.
  • Are deferments an option if I'm enrolled, but not ready to start yet?
    No, deferments are not an option. We ask that you please make sure to enroll for a term only if you are able to commit to the entire time frame.
  • What are the prerequisites for enrollment?

    To succeed in this Nanodegree program, we recommend you to first take any course in Deep Learning equivalent to the Deep Learning Nanodegree program prior to entering the program. Additionally, you should have the following knowledge:

    Intermediate Python programming knowledge, including:
    • Strings, numbers, and variables
    • Statements, operators, and expressions
    • Lists, tuples, and dictionaries
    • Conditions, loops
    • Generators & comprehensions
    • Procedures, objects, modules, and libraries
    • Troubleshooting and debugging
    • Research & documentation
    • Problem solving
    • Algorithms and data structures

    Basic shell scripting:

    • Run programs from a command line
    • Debug error messages and feedback
    • Set environment variables
    • Establish remote connections

    Basic statistical knowledge, including:

    • Populations, samples
    • Mean, median, mode
    • Standard error
    • Variation, standard deviations
    • Normal distribution

    Intermediate differential calculus and linear algebra, including:

    • Derivatives & Integrals
    • Series expansions
    • Matrix operations through eigenvectors and eigenvalues
  • If I don’t meet the requirements to enroll, what should I do?
  • Other Terms and Conditions
    This program is subject to special terms and conditions located on the legal hub. Please click on the “See “Special Terms” and select the terms and conditions related to this program.
    Program Structure
  • Is this program self-paced?
    This is not a self-paced program. Students will need to keep pace with their peers throughout the duration of the program, and complete all graduation requirements before the term end date (plus any allowed extension).
  • What happens if I miss a project deadline?
    There is no penalty for missing an individual project deadline. However, you must complete all projects in the program by the term’s final deadline in order to graduate from the program.
  • What happens if I don’t complete all projects by the term deadline?
    Students who have not completed all projects by the term deadline automatically receive a free four-week extension to complete their projects.. If you have not completed all projects by the end of the extension, you will be removed from the program and will no longer be able to access course content. To resume access to the course, you would need to pay the term fee again. In such case, your progress will carry over, so you will be able to continue where you left off.
  • Will I have permanent access to the material even after the term ends?
    No. You will retain access to the program materials for a period of time after graduation, and you may download certain materials for your own records if you wish. Please note however, that students who leave the program—or who are removed from the program for failure to meet the final deadlines—prior to successfully graduating, will cease to have access from the moment they leave the program.
  • How many hours a week should I expect to spend on my coursework, in order to succeed in this program?
    Between instructional content, quizzes, projects, and other course-related activity, we estimate that investing 10 hours/week will enable you to proceed through the program at a successful pace. Students with significant prior experience may spend less time, while students with very limited prior experience may require significantly more time.
    Career Services
  • Do I have access to Propel?
    Propel is the flagship Career Fair of Udacity in India. Only Advanced Job ready Nanodegree graduates have access to Propel. Through this initiative we connect our Nanodegree graduates to new-age technology companies and help them achieve their career goals.This is a foundation Nanodegree Program and the goal of the program is to build foundation skills in new-age technologies.
    Hence, Foundation Nanodegree Program Students do not have access to Propel.
    Tuition
  • How much does the Nanodegree program cost?
    This Nanodegree program consists of one three month term. The term costs INR 49900, paid at the beginning of the term.
  • Is payment due before the term begins?
    Yes. In this way, we know exactly how many student are in a term, and can optimize our instructional and support resources accordingly. Additionally, this approach ensures a consistent and stable student body throughout the program, which fosters a deeper sense of community, and enables richer collaborations as students work together as a group.
  • Is there an installment plan for tuition?
    No, the full tuition must be paid before the start of your term.
  • Is there a free trial period for this program?
    There is no free trial period for this program.
  • Are there scholarships or financial aid available?
    All current scholarship opportunities are posted on our Scholarships page.
  • What is the refund policy?
    There is a 7-day refund policy. During this time, you can visit the Settings page of your Udacity classroom where you can unenroll and request a full refund. This 7-day window begins the day the classroom opens. After the first 7 days, course fees are non-refundable.
    Other
  • Will content from the program also be available for free outside of the Nanodegree program?
    While some of the video material is available outside of the program, most of the material will only be available to enrolled Nanodegree students. Access to project feedback, instructor support, and hiring partners are benefits exclusive to the Nanodegree programs.

Natural Language Processing

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