Probability and Probability Distributions for Machine Learning
- Offered byGreat Learning
Probability and Probability Distributions for Machine Learning at Great Learning Overview
Probability and Probability Distributions for Machine Learning
at Great Learning
Duration | 1 hour |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course |
Credential | Certificate |
Probability and Probability Distributions for Machine Learning at Great Learning Highlights
Probability and Probability Distributions for Machine Learning
at Great Learning
- Earn a certificate of completion
Probability and Probability Distributions for Machine Learning at Great Learning Course details
Probability and Probability Distributions for Machine Learning
at Great Learning
What are the course deliverables?
- Marginal Probability
- Bayes Theorem
- Binomial Distribution
- Normal Distribution
- Poisson Distribution
More about this course
- Probability is a branch of mathematics that teaches us to deal with the occurrence of an event after specific repeated trials
- This free course on Probability in Machine Learning provides basic foundations for probability and various distributions such as Normal, Binomial, and Poisson
- It will make you familiar with the concept of Marginal probability and the Bayes theorem
- Lastly, you will work with a demo on distributions calculations using Python
Probability and Probability Distributions for Machine Learning at Great Learning Curriculum
Probability and Probability Distributions for Machine Learning
at Great Learning
Probability and Distributions Outline
Probability - Meaning and concepts
Rules for Computing Probability
Marginal Probability and Example
Bayes' theorem and Example
Binomial Distribution and Example
Poisson Distribution and Example
Normal Distribution and Example
Demo on Distributions calculations using Python
Probability and Probability Distributions for Machine Learning at Great Learning Faculty details
Probability and Probability Distributions for Machine Learning
at Great Learning
Dr. Abhinanda Sarkar
Designation : Faculty Director, Great Learning
Description : Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT); been on the research staff at IBM; led Quality, Engineering Development, and Analytics functions at General Electric (GE); served as Associate Dean at the MYRA School of Business; and co-founded OmiX Labs.
Dr. Sarkar’s publications, patents, and technical leadership have been in applying probabilistic models, statistical data analysis, and machine learning to diverse areas such as experimental physics, computer vision, text mining, wireless networks, e-commerce, credit risk, retail finance, engineering reliability, renewable energy, and infectious diseases, His teaching has mostly been on statistical theory, methods, and algorithms; together with application topics such as financial modeling, quality management, and data mining.
Dr. Sarkar is a certified Master Black Belt in Lean Six Sigma and Design for Six Sigma. He has been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc). Over the years, he has designed and conducted numerous corporate training sessions for
Read moreProbability and Probability Distributions for Machine Learning at Great Learning Admission Process
Probability and Probability Distributions for Machine Learning
at Great Learning
Important Dates
Nov 30, 2024
Course Commencement Date
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