Maximum Likelihood Estimation Courses and Certifications

Statistical Estimation for Data Science and AI

97 ratings at Coursera

Construct and evaluate estimators: method of moments, maximum likelihood, the Cramér–Rao lower bound, and confidence intervals for means, variances, and proportions.

• Coursera
• 6 weeks, 5 hours a week
• Self-Paced
• Paid Course

Interventions and Calibration

56 ratings at Coursera

Model vaccination and treatment interventions by extending SIR compartments, then calibrate models to epidemic data by hand and via least-squares and maximum-likelihood approaches in R.

• Coursera
• 4 weeks, 6 hours a week
• Self-Paced
• Paid Course

Bayesian Statistics: Mixture Models

63 ratings at Coursera

Learn mixture models: derive their likelihood function, fit them by maximum likelihood and Bayesian estimation, and apply them to data in R.

• Coursera
• 5 weeks, 4 hours a week
• Self-Paced
• Paid Course

Career Certificate

Practical Machine Learning: Foundations to Neural Networks

5 ratings at Coursera

Formulate learning tasks with probability and statistics, weigh Bayesian against frequentist views, fit parameters by maximum likelihood, build linear regression and classification models, and design neural networks.

• Coursera
• 12 weeks, 8 hours a week
• Self-Paced
• Paid Course
• 3 courses

Foundations for Machine Learning

Model discrete and continuous events with Bernoulli, binomial, multinomial, beta, Dirichlet, and Gaussian distributions, applying Bayes theorem plus Bayesian and frequentist parameter estimation.

• Coursera
• 8 weeks, 3 hours a week
• Self-Paced
• Paid Course

Machine Learning for Engineers: Algorithms and Applications

Learn the statistical foundations of machine learning: maximum likelihood estimation, convex optimization and gradient descent, bias-variance trade-off, cross-validation, and regularized linear regression implemented in Python.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

Stanford CS236 - Deep Generative Models I 2023

Master probabilistic foundations and algorithms for deep generative models including VAEs, GANs, normalizing flows, and diffusion models with applications across AI domains.

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

Statistics Fundamentals

A concept-focused overview of statistical distributions, inference, hypothesis testing, correlation, sampling, and maximum likelihood.

• YouTube
• 8 hours
• Self-Paced
• Free Video

Modelling and estimation

2 ratings at OpenLearn

This free course is concerned with modelling and estimation and looks in particular at the binomial distribution. Section 1 starts by defining probability, introduces relevant notation and briefly ...

• OpenLearn
• 16 hours
• Self-Paced
• Free Certificate

Point estimation

1 rating at OpenLearn

This free course looks at point estimation, that is, the estimation of the value of the parameter of a statistical model by a single number, a point estimate for the parameter. Section 1 develops ...

• OpenLearn
• 10 hours
• Self-Paced
• Free Certificate

Bayesian Networks 3 - Maximum Likelihood - Stanford CS221: AI (Autumn 2019)

Learn parameter estimation in Bayesian networks using maximum likelihood, Laplace smoothing, and expectation maximization.

• YouTube
• 1 hour 24 minutes
• Self-Paced
• Free Online Course

Deep Generative Models - Maximum Likelihood Learning - Lecture 4

Explore maximum likelihood learning in deep generative models with Stanford's Stefano Ermon. Gain insights into advanced AI techniques and their applications in this comprehensive lecture.

• YouTube
• 1 hour 24 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Inverse Methods in Heat Transfer

Discover advanced techniques for solving inverse heat transfer problems using regularization, Bayesian inference, and physics-informed neural networks.

• YouTube
• 1 day 6 hours 3 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Logistic Regression - Continued

Explore advanced concepts and applications of logistic regression for enhanced predictive modeling and data analysis skills.

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

Probability and Random Processes

3 ratings

Intuitive explanations of random variables and processes, from probability distributions to spectral density and parameter estimation in noisy measurements.

• YouTube
• 3 hours 30 minutes
• Self-Paced
• Free Video