Name: Probability and Probability Distributions for Machine Learning
Rating: 4.44 (435 reviews)

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What you learn in Probability and Probability Distributions for Machine Learning ?

Marginal Probability

Bayes Theorem

Binomial Distribution

Normal Distribution

Poisson Distribution

About this Free Certificate 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

Our course instructor

Dr. Abhinanda Sarkar

Faculty Director, Great Learning

351.5K+ Learners

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

With this course, you get

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1.5 Hours

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Frequently Asked Questions

What are the prerequisites to learn probability for machine learning?

Probability is one of the essential skills one must possess to have a good hold on machine learning concepts, and it is helpful in prediction and decision-making processes. Other prerequisites to learning machine learning include: Algebra, Linear Algebra, Trigonometry, Statistics, Calculus(for advanced topics), Python Programming, Terminal or Cloud Console.

How do beginners learn probability in machine learning?

Probability is not a challenging concept to learn, but it involves more than a few basic concepts to deal with while working with domains like machine learning. You will have to apply other concepts such as linear algebra, statistics, and calculus and also be able to work with python programming comfortably. You can start by learning Probability and Machine learning before diving into this Probability for Machine Learning course.

How long does it take to learn probability for machine learning?

The probability for machine learning course is a 2.5 hours long course, but you can learn it at your pace since the course is self-paced. With all the prerequisites mastered, it will not take much time to understand the concepts in this course. If you are supposed to start with learning all the basics such as statistics, calculus, python programming, and other such topics, you will take anywhere from 3 to 6 months before you are good at probability for machine learning.

Will I get a certificate after completing this course?

Probability for machine learning is a free course. You will be assigned with a few tasks after you complete the course to test your understanding. You can take this course in your leisure to learn and understand the subject since it is self-paced. You will be given with a certificate of completion after you successfully complete the course, and you can put it on your LinkedIn and other such platforms.

What are my career opportunities in probability in machine learning?

Machine Learning Engineer, Data Scientist, NLP Scientist, Business Intelligence Developer, and Human-Centered Machine Learning Designers are a few of the many career opportunities if you have mastered probability for machine learning. However, it is not just sufficient to learn probability; but you must also basket other skills such as statistics, linear algebra, calculus, python programming, and other concepts to be a top professional in machine learning.