Cornell-Cs5785-2020-Applied-Ml

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cornell-cs5785-2020-applied-ml is an Open Source research & education tool that provides teaching materials for an applied machine learning course. It includes executable course notes and slides, accompanying YouTube lecture videos.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is cornell-cs5785-2020-applied-ml?

This GitHub repository, cornell-cs5785-2020-applied-ml, serves as a comprehensive resource for the Applied Machine Learning course at Cornell Tech. It provides teaching materials, including executable course notes and slides, designed for the online edition of the Fall 2020 course. These materials are intended to complement a series of YouTube lecture videos, offering students and educators a complete learning package. The repository is organized with notebooks for interactive learning and a requirements.txt file to facilitate easy setup of the necessary virtual environment, ensuring all content can be run smoothly.

Best used for

Ideal for professors and teachers who need to deliver comprehensive machine learning course content, provide interactive lecture notes, and facilitate hands-on learning experiences. Especially valuable for educators looking to share open-source educational resources and integrate with video lectures.

Common actions

share course materials

provide lecture notes

facilitate machine learning education

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Capabilities

Key features

Executable course notes
Lecture slides
Virtual environment setup
YouTube video accompaniment

Target Audience

professorteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of content is available in the cornell-cs5785-2020-applied-ml repository?

The repository contains executable course notes and slides for the Applied Machine Learning course at Cornell Tech. These materials are designed to be used in conjunction with a set of YouTube lecture videos from the Fall 2020 edition of the course.

How can I set up the environment to run the course materials?

To run the contents of the repository, you should create a new virtual environment and then install the required packages by running 'pip install -r requirements.txt'. This ensures all dependencies are met for the notebooks and other files.

Is this course material suitable for beginners in machine learning?

Given it's for an 'Applied Machine Learning' course at Cornell Tech, it is likely geared towards students with a foundational understanding of computer science and mathematics, suggesting an intermediate to advanced skill level rather than absolute beginners.

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