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DEEP LEARNING FUNDAMENTALS COURSE

Learn Advanced Tech Skills to Set Yourself Apart

The online Metis Deep Learning Fundamentals Short Immersive Course will help you master key tech concepts that drive AI advances. You’ll learn from elite instructors and build on your professional project portfolio. 

ENROLL NOW

Upcoming Program Dates

Live Online

Nov 15 - Dec 3

11am - 6pm ET

Full-Time | Enroll

Online Flex

Jan 3 - Jan 28

Flexible Schedule

Part-Time | Enroll

Online Flex

Jan 31 - Feb 25

Flexible Schedule

Part-Time | Enroll

Questions?
Schedule a Call

Master Deep Learning Fundamentals

As technology advances, deep learning skills will become even more valuable than they already are. Acquire those skills and learn to apply them with our deep learning online course.

Learn from Data
Scientists with
Instructional
Expertise

Every Metis teacher is an industry-seasoned data practitioner as well as a top online instructor.

Finish a Project
Worthy of Your
Portfolio

Every Metis student will design and complete a project that shows you can apply your just-learned knowledge.

Our In-Depth Format
Helps You Upskill,
Fast

You’ll learn real-world skills quickly thanks to our immersive classes.

Join a Professional
Network of Metis
Graduates

Taking this course goes beyond skill-building. You’ll network and keep up with an extensive community of data science professionals.

Deep Learning Fundamentals Course Overview

DEEP LEARNING FUNDAMENTALS COURSE

Unit 1

Neural Networks, Embeddings and Convolutional Neural Networks

In unit one we’ll cover the basics—from simple feedforward neural networks to deep learning fundamentals. We’ll also explore embeddings, transfer learning, and convolutional neural networks.

Unit 2

Sequence Modeling

In unit two, we’ll dive deeper into deep learning methods for modeling sequential data. At the end of the course, you’ll complete a final assessment.

PROJECT

This is a great opportunity to show your ability to solve data science problems using deep learning algorithms. You’ll choose a problem related to images, time series, text, or other complicated data formats, and train your models or apply transfer learning techniques.

What's Covered

  • Converting non-tabular data sources into numerical arrays that can be analyzed and processed as neural network inputs
  • Describing the architecture, prediction process, and training methodology of feed-forward neural networks
  • Constructing neural networks in Python, tuning their hyperparameters with rigorous model training and evaluation techniques
  • Describing and building the architecture and prediction process of convolutional neural networks to handle image data inputs in Python
  • Describing and building the architecture and prediction process of recurrent neural networks to handle sequential data inputs in Python
  • Applying transfer learning to improve the quality of both image and text processing neural networks

Prerequisites

We recommend you have some data knowledge of the following topics:

  • Python and pandas basics
  • Common visualization tools used to explore, analyze, and visualize datasets
  • Machine learning math fundamentals including calculus, linear algebra, probability and statistics
  • Basic relational databases and SQL syntax, as well as how to write simple queries to extract information from relational databases
  • Major supervised machine learning topics including regression models, classification models, and model evaluation
  • Web scraping or gathering data through programming interfaces (APIs) is not required but helpful

ENROLL

An Engaging Online Experience

Live Online | Online Flex

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

Watch pre-recorded lectures on your own schedule.

Stay engaged and follow along as your instructor shares a presentation.

Stay on track with assignment deadlines and organized subject modules.

Join the conversation on video, in the chat, and with the polling feature.

Test your skills with assessments developed by our in-house team of data scientists.

We simulate the in-person learning experience with a digital whiteboard.

Set up 1:1 time with your instructor, or connect with your peers via Slack and the online platform.

You’ll collaborate with your classmates during project work and pair-programming.

Watch presentations from invited speakers, take part in social events, and more.

Keep up with your teachers and classmates in your dedicated Slack channel.

WHICH SHORT IMMERSIVE FORMAT WORKS FOR YOU?

Whether you choose Live Online or Online Flex, you’ll be on the fast track to upskilling in Deep Learning Fundamentals.

LIVE ONLINE FORMAT

Get an immersive, engaging experience that lets you focus deeply on your studies. Live Online lectures give you maximal interaction with peers and instructors.

ONLINE FLEX FORMAT

Pre-recorded lectures let you study on your own time. Assignment deadlines and structured modules keep you on track, while 1:1 time with an instructor gives you all the support you need.

Length

Tuition

Weekly Time Commitment

Schedule

Lectures

Instructors

Office Hours

Project Presentations

Social Activities

Payment Options

PAY UP FRONT

Live Online: $3,500

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.

TALK WITH US

Metis Instructors: Meet a Few Now

Elite, industry-seasoned data scientists will lead your Deep Learning Fundamentals Course. They also meet our high standard for online instruction.

  • Chris Bruehl

    Data Scientist

  • Rita Biagioli

    Data Scientist

Meet the Full Team

Upcoming Program Dates

Live Online

Nov 15 - Dec 3

11am - 6pm ET

Full-Time | Enroll

Online Flex

Jan 3 - Jan 28

Flexible Schedule

Part-Time | Enroll

Online Flex

Jan 31 - Feb 25

Flexible Schedule

Part-Time | Enroll

Questions?
Schedule a Call

Frequently Asked Questions

  • Are there prerequisites for the Deep Learning Fundamentals course?

    This short immersive course is advanced, so while anyone can enroll, we strongly suggest all incoming students demonstrate the following:

    • - Comfort with the math fundamentals of machine learning including statistics, calculus, linear algebra, and probability.
    • - Proficiency in general pandas, Python, and basic visualization libraries; the ability to comfortably use these tools to analyze, explore, and visualize tabular datasets.
    • - Theoretical knowledge of, as well as practical experience in, major supervised machine learning topics including classification models, regression models, and model evaluation.
    • - Comfort with web scraping or obtaining data through application programming interfaces (APIs). This is a definite plus for students who wish to move through the course with a wider range of possible projects. However, these skills are not necessary for course success.
    • - All students need an active Github account, which they can handle prior to the course start. It’s free and easy to do so. 

  • How should I go about enrolling in this course?

    To enroll in the Deep Learning Fundamentals course, all you have to do is click here. Be sure to do so before 8pm ET on the Friday before the course start date. That deadline allows us to send students all necessary materials and information before day one so they can start the course on the right foot.

  • What payment options are available for this course? How much does the course cost?

    Tuition for the Deep Learning Fundamentals course is $3,500 for the Live Online format and $2,500 for the Online Flex format. 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 is the time commitment for the Deep Learning Fundamentals course?

    Students in our Live Online Deep Learning Fundamentals course format are required to be online from 11:00 am - 6:00 pm ET / 10:00 am - 5:00 pm CT / 8:00 am - 3:00 pm PT five days a week for two consecutive weeks. The daily schedule includes pair programming, lectures, independent project work, and a one-hour lunch break. 

    Additionally, students should expect to spend evenings after class hours reviewing lectures and course materials, completing assignments, and working on their project. In order to receive a certificate of completion at the end of the course, students must maintain attendance and achieve passing scores on both their project and assessments. 

    Students in the Online Flex Deep Learning Fundamentals course format are required to complete 15-to-20 hours a week for four weeks. They will watch on-demand lectures and engage with instructors and teaching assistants live online during 1:1 sessions weekly.

  • Is Metis Career Support included with this course?

    Career support is not included as part of our short immersive courses. However, students will gain access to our impressive network of data professionals and to our always-updating list of networking events and job opportunities that are routinely shared in Slack.

  • Who are the Instructors who teach Metis short immersive courses?

    Those who teach our short immersive courses are the same highly-reviewed instructors who teach our bootcamps. Each brings a high level of expertise garnered from their advanced work and educational backgrounds, which span industries and areas like mathematics, finance, physics, business, and much more.

ADDITIONAL FAQ

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