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NATURAL LANGUAGE PROCESSING AND UNSUPERVISED LEARNING COURSE

Leverage NLP Techniques in the Real World

In this Short Immersive Course for Natural Language Processing (NLP) and Unsupervised Learning from Metis, you’ll understand and apply topics like dimensionality reduction, recommendation systems, and clustering. 

Upskill in NLP—Quickly

Natural language processing is a valuable skill that employers will rely on more and more as text data sources become more prevalent. Get ahead of the curve and build NLP skills fast in our immersive online course.

Learn from
World-Class Data
Scientists

Learn from industry-seasoned practitioners who meet our high bar of instructional excellence.

Create a Professional
Project

Metis students are required to design and complete a project that applies their newly-acquired knowledge.

Learn Skills Fast with
In-Depth Classes

You’ll pick up valuable skills quickly in our immersive classes.

Connect with Other
Data Professionals

This course teaches more than skill-building. You’ll join an extensive community of data science professionals.

NLP Course at a Glance

NLP AND UNSUPERVISED LEARNING COURSE

Unit 1

Natural Language Processing and Unsupervised Learning Basics

We’ll start with the basics. Unit one is about getting acquainted with natural language processing, dimensionality reduction, and recommendation systems. We’ll also cover basic clustering techniques.

Unit 2

Natural Language Processing and Unsupervised Learning Advanced

In unit two, we’ll dive deeper into clustering algorithms and advanced natural language processing techniques. At the end of the course, you’ll complete a final assessment.

PROJECT

For your project, you’ll use natural language processing algorithms to perform text data analysis. You’ll apply different techniques for dimensionality reduction like Principal Component Analysis, and use topic models, and clustering algorithms such as K-means.

What's Covered

  • Foundational techniques in the quantification of text data including tokenization, vectorization, and the document-term matrix
  • How to properly clean and pre-process text data for quantitative analysis
  • The ways to describe and differentiate key dimensionality reduction algorithms and other modeling techniques for creating topic models in language processing
  • How to cluster data points using the k-means model and other specialized techniques, navigating the tradeoffs and use cases for each method
  • Ways to apply, compare, and contrast unsupervised learning techniques for different business case studies
  • How to build recommendation systems using content-based and collaborative filtering methods, describing the differences and tradeoffs between the two

Prerequisites

We recommend that students have a basic understanding of the following:

  • Python, pandas, and basic visualization libraries and how to use these tools to explore, analyze, and visualize tabular datasets
  • The mathematics fundamentals of machine learning including calculus, linear algebra, probability, and statistics
  • Familiarity with supervised machine learning including linear regression, basic classification models, and model evaluation
  • Web scraping or obtaining data through application programming interfaces (APIs) is a plus

Your Online Experience Brought to Life

Online Flex

Experience what a lecture is like and how you’ll participate during class.

Study on your own time with pre-recorded lectures.

Follow along during lecture as your instructor shares their screen.

With assignment deadlines and structured modules, you’ll always be on track.

Interact with your instructors and classmates on video, in the chat, and with the polling feature.

Test your knowledge directly in the platform—so you can deepen your learning immediately.

Real-time whiteboarding simulates the classroom learning experience.

Connect with peers in the learning platform and via Slack, and attend office hours with your instructor.

Collaborate with classmates during pair-programming, project work, and more.

Watch live speaker presentations, join social events, and more.

Chat with your instructors and classmates in a dedicated Slack channel.

LEARN MORE ABOUT OUR SHORT IMMERSIVE FORMAT

Our Online Flex format will have you on the fast track to upskilling in Natural Language Processing and Unsupervised Learning.

ONLINE FLEX FORMAT

On-demand lectures let you fit studying into your life. You’ll also get an organized study plan, deadlines that keep you accountable, and 1:1 support from an instructor.

Length

Tuition

Weekly Time Commitment

Schedule

Lectures

Instructors

Office Hours

Project Presentations

Social Activities

Payment Options

PAY UP FRONT

Online Flex: $2,500

FINANCING OPTIONS

Coming soon! Finance your tuition through our lending partner: Ascent.

EMPLOYER SPONSORSHIP

Find out if your employer will cover the cost of tuition to support your development.

World-Class Instructors with Online Teaching Expertise

You’ll learn from top data scientists in the Metis Natural Language Processing Course. Get to know a few of them now.

  • Rita Biagioli

    Data Scientist

Meet the Full Team

Frequently Asked Questions

  • Are there prerequisites for the Natural Language Processing and Unsupervised Learning short immersive course?

    This is an advanced course, and while anyone can enroll, we strongly recommend students only enroll if the following statements apply: 

    • - Students should be confident using basic visualization libraries, general Python, general pandas.
    • - Students should be able to use the tools listed above to analyze, explore, and visualize tabular datasets.
    • - Students should be versed in the math fundamentals of machine learning, including linear algebra, probability, statistics, and calculus.
    • - Students should have at least a working familiarity with major topics in supervised machine learning including linear regression, basic classification models, and model evaluation.
    • - While not necessary to succeed in the course, students who are comfortable with web scraping or obtaining data through application programming interfaces (APIs) have the advantage of a wider range of project possibilities.
    • - Github is a web-based hosting service for version control using Git, and all students need to sign up for a free account before class begins.

  • How can I enroll in the Natural Language Processing and Unsupervised Learning course?

    Click here to enroll before 8pm ET on the Friday before the course start date. This deadline enables us to provide students with all the information they’ll need to start class on day 1.

  • How much is tuition? Are there payment plans?

    The cost of the Natural Language Processing and Unsupervised Learning course is $2,500. Students can pay for the course in full or consider one of our financing options. We partner with Ascent to offer monthly repayment options for our students. Learn more by visiting Ascent.

  • What kind of time commitment is required for this course?

    The Online Flex course format is a 15-to-20 hours a week commitment for four weeks. Students watch on-demand lectures and engage with instructors and teaching assistants live online during 1:1 sessions weekly.

  • Does this course include Career Support Services?

    No, our short immersive courses do not include career support like our bootcamps do. 

  • Who are the Instructors for this course?

    At Metis, our qualified team of Data Science Instructors rotate to teach both the full-time bootcamps and the short immersive courses. These seasoned professionals and educators boast backgrounds spanning business, finance, engineering, healthcare, and more.

ADDITIONAL FAQ

Questions?

Read our FAQs

See if Bootcamps are a Better Fit

We can teach you a lot in two-to-four weeks, but we can teach you so much more in our longer bootcamps. You’ll get access to career support until you get a job.

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