Machine Learning Courses and Certifications

UofU Data Science

Maximum Likelihood Estimation for Regression in Machine Learning - Lecture 24

Dive into maximum likelihood estimation for regression through practical examples and explore its connection to loss minimization in Bayesian learning frameworks.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

UofU Data Science

Boosting and Ensemble Methods in Machine Learning - Lecture 18

Dive into boosting algorithms and learn how to combine weak learning rules to create powerful predictive models in machine learning applications.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning: Boosting and Ensemble Methods - Lecture 19

Dive into advanced boosting techniques and ensemble methods to transform weak learning algorithms into powerful predictive models for enhanced machine learning capabilities.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

UofU Data Science

Agnostic Learning in Machine Learning - Lecture 16

Dive into computational learning theory and explore the fundamental concepts of agnostic learning in machine learning applications and theoretical frameworks.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

UofU Data Science

Positive and Negative Learnability Results in Machine Learning - Lecture 14B

Explore fundamental learnability concepts in machine learning through Occam's razor theorem applications and function class analysis.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning: Occam's Razor for Consistent Learners - Lecture 13B

Explore the theoretical foundations of Occam's razor theorem and its applications in consistent machine learning algorithms through mathematical analysis.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning: Occam's Razor and Consistent Learners - Lecture 14

Delve into the principles of Occam's razor and its application in consistent learning algorithms, exploring key theoretical foundations of machine learning complexity.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

UofU Data Science

Perceptron Mistake Bound Theorem in Machine Learning - Lecture 10

Dive into the mathematical foundations of Perceptron algorithms, exploring the theorem and proof behind mistake bounds in machine learning classification.

• YouTube
• 1 hour 23 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning: Representations and Learning in Online Systems - Lecture 9

Explore how hypothesis representation choices impact online learning algorithms and their effectiveness in machine learning applications.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

UofU Data Science

Linear Classifier Expressiveness in Machine Learning - Lecture 7

Delve into the theoretical foundations of linear classifiers, exploring their expressive capabilities and understanding their role as a fundamental hypothesis class in machine learning.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

UofU Data Science

Linear Models and Their Geometry in Machine Learning - Lecture 6

Explore the geometry and fundamental concepts of linear classifiers, examining their role as essential tools in machine learning classification problems.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning Fundamentals and Perceptron Algorithm

Dive into fundamental machine learning concepts, from feature vectors to binary classification, culminating in a detailed exploration of the Perceptron algorithm.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning Fundamentals - Lecture 11

Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications at a graduate level.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning Fundamentals - Lecture 10

Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning Debug Tutorial - Fall 2023

Master essential debugging techniques for machine learning algorithms, focusing on practical troubleshooting methods and common error resolution in ML development.

• YouTube
• 43 minutes
• Self-Paced
• Free Video