Machine Learning Courses and Certifications

IMSA

Exact and Approximate Symmetries in Machine Learning Models

Explore exact and approximate symmetries in machine learning models, examining their impact on model performance and interpretability. Gain insights into leveraging symmetries for more efficient and robust AI systems.

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

Anyscale

Overview of CloudKitchens's Ray-Powered Machine Learning Platform

Discover how CloudKitchens leverages Ray and Kubernetes to build a state-of-the-art ML platform, featuring seamless integration of Metaflow, Daft, and Argo Workflows for enhanced development efficiency.

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

IPhT-TV

Mathematical Problems in Machine Learning - Lecture 3

Delve into advanced mathematical probability concepts and their applications in machine learning through Stanford expert Andrea Montanari's comprehensive theoretical exploration.

• YouTube
• 1 hour 59 minutes
• Self-Paced
• Free Video

IPhT-TV

Mathematical Problems in Machine Learning - Lecture 2

Delve into mathematical probability fundamentals and their applications in machine learning through advanced theoretical concepts and problem-solving approaches from Stanford expertise.

• YouTube
• 1 hour 54 minutes
• Self-Paced
• Free Video

UofU Data Science

Practical Advice for Building Machine Learning Applications - Lecture 28

Gain insights into practical strategies and best practices for implementing real-world machine learning systems, from system design to deployment considerations.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

UofU Data Science

Logistic Regression - Machine Learning Lecture 24

Master logistic regression classification through maximum likelihood and maximum a posteriori criteria, building practical skills for effective machine learning model development.

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

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