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Probability and Probability Distributions for Machine Learning

Take up free Probability for Machine learning course and forecast the variability of occurrence.

Instructor:

Dr. Abhinanda Sarkar
4.47
average rating

Ratings

Beginner

Level

2.25 Hrs

Learning hours

17.5K+
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Learners

Skills you’ll Learn

About this Course

Probability is a branch of mathematics that teaches us to deal with the occurrence of an event after specific repeated trials. The value here is expressed from zero to one. It aids us in understanding exactly how a particular event is going to behave in a given set of variables. It also aids us in predicting possible variations in the behavior of the variable in a fluctuating environment.

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.

Several world-class universities, such as the UT Austin and SRM Institute of Science and Technology, have formed a collaboration with Great Learning. They designed various post-graduate artificial intelligence courses and degree programs, which are India’s #1 ranked programs in the industry. An extensive curriculum has been prepared by top-class faculty so that the learners could develop advanced AIML skills. Various industry experts from top-notch organizations offer personalized mentorship to our learners, providing guidance to become successful in their careers.

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Course Outline

Probability - Meaning and Concepts

You will learn what probability means and its concepts in this module through some examples. The instructor will discuss what an experiment is with an example and define and thoroughly explain the formula to determine the likelihood of an event. Later, a diagram will assist you in comprehending the extreme probability values and mutually exclusive events.
 

Rules for Computing Probability
Marginal Probability and its Example
Bayes' Theorem and its Example
Binomial Distribution and its Example
Poisson Distribution and its Example
Normal Distribution and its Example

This section explains normal distribution for continuous functions and the association between mean, median, and mode in a normal distribution with solved examples and also discusses its properties and practical applications. You will also understand density function and standard distribution with solved example problems.

Demo - Probability Distributions using Python

Our course instructor

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Dr. Abhinanda Sarkar

Faculty Director, Great Learning

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503.7K+ Learners
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17 Courses
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 technology and business professionals. He is a recipient of the ISI Alumni Association Medal, IBM Invention Achievement Awards, and the Radhakrishan Mentor Award from GE India

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Probability and Probability Distributions for Machine Learning

2.25 Learning Hours . Beginner

Why upskill with us?

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Free life time Access