Maximum Likelihood Estimation Courses and Certifications

Professor Knudson

Maximum Likelihood Estimation

Explore maximum likelihood estimation through examples of binomial, Poisson, and uniform distributions, enhancing statistical analysis skills.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Statistical Inference and Modeling for High-throughput Experiments

9 ratings at edX

A focus on the techniques commonly used to perform statistical inference on high throughput data.

• edX
• 4 weeks, 2-4 hours a week
• On-Demand
• Free Online Course (Audit)

情报监视侦察信息融合技术

Dive into intelligence surveillance reconnaissance and information fusion techniques, covering sensor technologies, tracking systems, and advanced image processing methods for military applications.

• XuetangX
• On-Demand
• Free Online Course

Probability and Statistics III: A Gentle Introduction to Statistics

Explore probability distributions, statistical concepts, and estimation methods. Learn about normal distribution, Central Limit Theorem, and sampling distributions for practical applications in data analysis.

• edX
• 4 weeks, 6-10 hours a week
• On-Demand
• Free Online Course (Audit)

Statistics for Finance

389 ratings at Udemy

Learn core probability and statistics with Python tools and real applications in finance and business

• Udemy
• 1 day 17 hours 30 minutes
• On-Demand
• Paid Course

Mathematical Statistics for Data Science

372 ratings at Udemy

Ex-Google data scientist's guide to mathematical statistics, covering method of moments, maximum likelihood, and more

• Udemy
• 4 hours 14 minutes
• On-Demand
• Paid Course

Statistical Estimation for Data Science and AI

90 ratings at Coursera

Learn statistical inference, sampling distributions, and confidence intervals. Explore estimation methods, construct good estimators, and develop confidence intervals for various settings. Gain practical skills for data analysis and interpretation.

• Coursera
• 1 day 5 hours 23 minutes
• On-Demand
• Paid Course

Statistics for Applications

1 review

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications.

• MIT OpenCourseWare
• 28 hours
• On-Demand
• Free Video

Loss Distributions for Actuarial Models

175 ratings at Udemy

Master statistical distributions and actuarial modeling in R Studio, covering loss analysis, reinsurance treaties, parameter estimation, and goodness of fit testing for professional actuarial practice.

• Udemy
• 4 hours 30 minutes
• On-Demand
• Paid Course

Mastering Probability & Statistic Python (Theory & Projects)

91 ratings at Udemy

Statistic & Probability for Machine Learning & Data Science: Learning Statistics, Probability & Bayes Classifier, Python

• Udemy
• 13 hours 51 minutes
• On-Demand
• Paid Course

Bayesian Statistics: Mixture Models

62 ratings at Coursera

Explore mixture models in Bayesian statistics, covering concepts, estimation methods, and practical applications. Gain hands-on experience with R software for real-world data analysis.

• Coursera
• 21 hours 32 minutes
• On-Demand
• Paid Course

Microcredential

Practical Machine Learning: Foundations to Neural Networks

Master probability, statistics, and neural networks to build rigorous machine learning models from linear regression to deep learning applications.

• Coursera
• 12 weeks, 8 hours a week
• On-Demand
• Paid Course
• 3 courses

Statistical Data Analysis for Scientists and Engineers

Extend your knowledge and skills from univariate and bivariate statistical analysis to multivariate methodology and data analysis.

• edX
• 12 weeks, 5-7 hours a week
• On-Demand
• Free Online Course (Audit)

Generative AI: Foundations and Concepts

Explore the mathematical foundations of generative AI, from neural networks to advanced deep learning models, with practical implementation techniques and applications in engineering.

• Coursera
• 21 hours 36 minutes
• On-Demand
• Paid Course

Machine Learning for Engineers: Algorithms and Applications

Master practical machine learning algorithms through hands-on implementation in Python and PyTorch, covering supervised and unsupervised learning techniques for real-world applications in computer vision, NLP, and robotics.

• Coursera
• 17 hours 31 minutes
• On-Demand
• Paid Course