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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.

4.49

Beginner

2.25 Hrs

19.3K+

Ratings

Level

Learning hours

Learners

Skills you’ll Learn

Marginal Probability

Bayes Theorem

Binomial Distribution

Normal Distribution

Poisson Distribution

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. Check out our PG Course in Machine learning Today.

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

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Frequently Asked Questions

Will I receive a certificate upon completing this free course?

Is this course free?

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.