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MACHINE LEARNING CLASSIFICATION COURSE ONLINE

Master Machine Learning Classification—Fast

The Metis Short Immersive Course for Machine Learning (ML) Classification will equip you with in-demand skills so you can leverage classification methods to solve real-world problems.  

ENROLL NOW

Upcoming Program Dates

Live Online

Oct 18 - Oct 29

11am - 6pm ET

Full-Time | Enroll

Online Flex

Oct 18 - Nov 12

Flexible Schedule

Part-Time | Enroll

Live Online

Nov 15 - Dec 17

11am - 6pm ET

Full-Time | Enroll

Questions?
Schedule a Call

Build Sought-After Machine Learning Skills

Our Short Immersive Course will teach you to apply machine learning classification skills that companies value more and more each day. Put yourself ahead of the curve—and the competition.

Learn from
Experienced Data
Science Instructors

Your course is taught by industry-seasoned teachers who meet a high bar of instructional excellence.

Complete a
Professional Portfolio
Project

You will be required to design and complete a project to showcase your ability to apply your knowledge.

Quickly Build Skills
with Immersive
Learning

Pick up in-demand skills quickly with our immersive classes.

Be a Part of a
Professional Network

Skill-building isn’t the only benefit of taking a Metis course. You’ll join a far-reaching community of data science professionals.

Machine Learning Online Course Overview

MACHINE LEARNING CLASSIFICATION COURSE

Unit 1

Classification Basics

We’ll start with the basics. Unit one of this ML online course is about getting acquainted with classification models and metrics. We’ll also cover feature engineering for classification problems.

Unit 2

Classification Advanced

In unit two, we’ll dive deeper into classification models and how to work with imbalanced data sets. At the end of the course, you’ll present a final project.

PROJECT

Apply your new skills by choosing a realistic classification problem to solve using a real world dataset. You’ll do this by using algorithms such as logistic regression, decision trees, random forest, gradient boosting, KNN, and more. Dive deeper into techniques for supervised machine learning, address the difficulties of class imbalance, and determine the proper metrics for your modeling problem.

What's Covered

  • How to explain the theoretical underpinnings of common classification algorithms, including their assumptions and limitations
  • The merits of each classification algorithm in terms of predictive performance, interpretability, and complexity
  • Calculating key metrics used to evaluate classification models
  • Identifying appropriate use cases for each classification metric
  • Ensembling, hyperparameter tuning, and class imbalance strategies to improve classification performance
  • Applying classification concepts to real-world business problems

Prerequisites

We recommend that you have a basic understanding of:

  • Pandas, Python, and basic visualization libraries, and the ways these tools are used to explore, analyze, and visualize tabular datasets
  • Mathematics fundamentals of machine learning including linear algebra, probability, calculus, and statistics
  • Linear regression theory and its applications
  • Basic concepts in machine learning, including regularization, supervised learning terminology, gradient descent, bias/variance trade-off, and evaluation and model selection techniques

ENROLL

An Engaging Online Experience

Live Online | Online Flex

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

Go to class whenever it’s convenient for you.

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

Be held accountable by assignment deadlines, and stay on track with structured subject modules.

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

Take your learning further with assessments designed by Metis’s in-house team.

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

Connect with other students in the online platform, and schedule 1:1 time with your instructor.

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

Tune in to live speaker presentations, attend 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 Machine Learning Classification.

LIVE ONLINE FORMAT

Interact with instructors and classmates in this full-time, immersive two-week format—and skill up, at lightning speed.

ONLINE FLEX FORMAT

Study on your own with on-demand lessons, and get added support with 1:1 time from your instructor. Deadlines help keep you on track.

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

Learn From Elite Data Scientists

Our Machine Learning Classification Course is led by experts in the field with superior online teaching experience. Read up on a few of our industry-seasoned instructors now.

  • Kimberly Fessel

    Lead Data Scientist

  • Chris Bruehl

    Data Scientist

  • Rita Biagioli

    Data Scientist

Meet the Full Team

Upcoming Program Dates

Live Online

Oct 18 - Oct 29

11am - 6pm ET

Full-Time | Enroll

Online Flex

Oct 18 - Nov 12

Flexible Schedule

Part-Time | Enroll

Live Online

Nov 15 - Dec 17

11am - 6pm ET

Full-Time | Enroll

Questions?
Schedule a Call

Frequently Asked Questions

  • What are the prerequisites for the Machine Learning Classification short immersive course?

    The Machine Learning Classification short immersive is an advanced course, and while anyone can enroll, we strongly recommend students only enroll if the following applies to them:

    • - Comfort with the mathematics fundamentals of machine learning including statistics, calculus, probability, and linear algebra.
    • - Proficiency in general pandas, Python, and basic visualization libraries and an ability to use these tools in order to explore, analyze, and visualize tabular datasets.
    • - Familiarity with the theory and applications of linear regression.
    • - Understanding of foundational concepts in machine learning, including supervised learning terminology, regularization, bias/variance trade-off, gradient descent, and model selection and evaluation techniques such as cross validation and testing.
    • - Comfort with web scraping or obtaining data through application programming interfaces (APIs) is a definite plus for expanding the range of possible projects a student can complete, but is not strictly necessary to succeed in the course.
    • - Lastly, students will need a Github account. It’s free and easy to sign up. 

  • How do I enroll in this course?

    Click here to enroll in the Machine Learning Classification short immersive course. Enrollment will close at 8pm ET on the Friday before the class start date to guarantee students receive the information they need to start class on the first day.

  • How much does this course cost, and are there payment options?

    Tuition 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 kind of time commitment does the Machine Learning Classification course require?

    For the Live Online format, it is a full-time commitment, Monday through Friday for two weeks. In order to successfully complete the course and receive a certificate of completion, students must maintain attendance daily from 11:00 am - 6:00 pm ET / 10:00 am - 5:00 pm CT / 8:00 am - 3:00 pm PT. These hours include lectures, pair programming, a one-hour lunch break, and independent project work. Additionally, students will spend time outside of class completing assignments, reviewing lectures, and working on their course project.

    For the Online Flex format, it 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.

  • If I enroll in this course, do I get access to Metis career support?

    No, we do not offer career support for our short immersive course students. But our immersive course students do gain access to our network of data professionals, and to networking events and job opportunities that are shared in Slack.

  • Who teaches the Machine Learning Classification course?

    Metis short course Instructors are the same ones who teach our bootcamps. Their expertise spans engineering, physics, healthcare, business, neuroscience, and more.

ADDITIONAL FAQ

Questions?

Read our FAQs

Want Something a Little More In-Depth?

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