Hi all,

Below is a list of publicly available resources that we have found useful in our journey of learning Machine Learning. The list of resources is currently ordered by priority, with multiple recommendations based on the format of the content. We highly suggest going through this list and picking the resources based on your current background and learning styles.

Google has an excellent page that provides resources for people based on their level of knowledge in machine learning. It also provides adjunct materials for helping with coding expertise with regards to NumPy or Pandas: Prerequisites and prework | Machine Learning | Google for Developers

Resource we want students to go through first:

Machine Learning | Google for Developers

Video explanation of concepts
Modularized with interactive visualizations
Exercises provided in modules
Crash course - should require approximately 15-30 hours.

Highly recommended as a supplement for understanding Machine Learning fundamentals:

Sebastian Raschka | Introduction to Deep Learning

Short-form video lectures covering a wide range of topics.
Post-lecture python notebooks.
Equivalent to a semester’s worth of material.

Highly recommended for students with little to no experience in ML or Deep Learning.

Alternately,

MIT Open Library: Introduction to Machine Learning Course

Video lectures with transcripts covering in depth.
Post-Lecture exercises.
Equivalent to a semester’s worth of material.

Highly Recommended for students with little to no experience in ML.

Alternately,

Andrew Ng's Machine Learning Course from Stanford

Intro to Machine Learning Theory.
Similar to UMN’s CSCI 5521: Machine Learning Fundamentals
Builds students’ understanding of the mathematical theory of machine learning.

Dive into Deep Learning

Interactive deep learning book with code, math, and discussions
Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow
Adopted at 500 universities from 70 countries

For an alternative visual way of learning neural networks, back-propagation, convolutions and transformers:

Neural Networks Playlist | 3Blue1Brown

Visualized format for understanding neural networks in-depth
Channel also provides video explanations in other mathematical concepts such as probability and convolutions.
Quick introduction and explanation of Neural Networks - approx 2 hours.
If using this resource, supplement with Google Crash Course to have real-world examples.

Additional optional resources that may be of interest:

Gilbert Strang's Course on Linear Algebra

Linear Algebra course.
Similar to UMN’s MATH 4242: Applied Linear Algebra
Builds students’ understanding of linear algebra.

Alexander Amini's Deep Learning crash course

Not a mathematics-heavy deep learning crash course.

Andrew Ng's Deep Learning course

Mathematics heavy deep learning course.
Similar to UMN’s CSCI 5527: Deep Learning: Models, Computation, and Applications.

Introduction to Deep Learning -- 170 Video Lectures from Adaptive Linear Neurons to Zero-shot Classification with Transformers

https://sebastianraschka.com/blog/2021/dl-course.html

Introduction to Machine Learning-- Video Lectures about Python Basics, Tree-based Methods, Model Evaluation, and Feature Selection

https://sebastianraschka.com/blog/2021/ml-course.html