Probability and Probability Distributions for Machine Learning
Take up free Probability for Machine learning course and forecast the variability of occurrence.
What you learn in Probability and Probability Distributions for Machine Learning ?
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
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.
Our course instructor
Dr. Abhinanda Sarkar
Faculty Director, Great Learning
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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1.5 Hours
of self-paced video lectures
