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University & Pro Programs

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McCombs School of Business at The University of Texas at Austin

23 Weeks  • Online

Free Probability Courses

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Probability and Probability Distributions for Machine Learning
star   4.49 20.8K+ learners 1.5 hrs

Skills: Marginal Probability, Bayes Theorem , Binomial Distribution, Normal Distribution, Poisson Distribution

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Probability and Normal Distribution
star   4.38 2.3K+ learners 1.5 hrs

Skills: Probability Distribution, Normal Distribution

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Probability for Data Science
star   4.47 55K+ learners 1.5 hrs

Skills: Basics of Probability, Marginal Probability, Bayes Theorem

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Probability and Probability Distributions for Machine Learning

Skills: Marginal Probability, Bayes Theorem , Binomial Distribution, Normal Distribution, Poisson Distribution

free icon BASICS
Probability and Normal Distribution
star   4.38 2.3K+ learners 1.5 hrs

Skills: Probability Distribution, Normal Distribution

free icon BASICS
Probability for Data Science
star   4.47 55K+ learners 1.5 hrs

Skills: Basics of Probability, Marginal Probability, Bayes Theorem

Learn Probability for Free and Get Certificates

Probability is the branch of mathematics that describes the possibility of the occurrence of an event. It gives a numerical description of how likely the proposition is true. The probability of occurrence of any event lies between numbers 0 and 1. 0 indicates the probability of impossibility and 1 indicates a sure event. Any number in between will simply indicate near possibility or impossibility. The higher the number, the higher probability of occurrence of an event. A very simple and common example can be the tossing of a fair or unbiased coin. There are two outcomes in a fair coin toss, either heads or tails. Both are equally probable. The probability of occurrence of heads is equal to the probability of occurrence of tails. There are no other possible outcomes while tossing a coin, so the probability of either heads or tails is ½, this can also be indicated as 50% or 0.5.

 

Probability is expressed in terms of axiomatic mathematical formalization in probability theory. It is applied to a variety of fields such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science, game theory, philosophy and many others. For example to draw inferences about the expected frequency of events. The theory is used to describe mechanics and regularities of complex systems in addition. 

 

Experiment: An experiment is an operation that produces well-defined outcomes, for example, tossing a coin. The outcome is either head or tails. It is well-defined.

 

Random experiment: Random experiment is when all the possible outcomes are known, but the exact outcome cannot be determined in advance, for example, rolling of a dice. Dice has 6 faces, when rolled, one cannot say what number amongst 1, 2, 3, 4, 5 and 6 may appear on the upper face.

 

Sample space: Sample space contains all the possible outcomes of an experiment, for example, there are six outcomes when a dice is rolled. All of these six outcomes comprise the sample space. It is indicated by “S”, S = {1, 2, 3, 4, 5, 6}

 

Outcome: Outcome is defined as the possible result of the sample space S for a random experiment, for example, when a dice is rolled, the upper face can be 2 or when a coin is tossed, the outcome can be heading. 

 

Event: Event is any subset of sample space S. It is denoted by E. An event is said to have occurred when any outcome belonging to the subset E takes place. Contrarily, when the outcome that does it belong to subset E takes place, it is said that the event has not occurred, for example, assume rolling a dice. The sample is S = { 1, 2, 3, 4, 5, 6 }. Suppose E indicates the event of ‘occurrence of a number less than 3’. So the event E = { 1, 2 }. If the number 1 or 2 appears on the face of the dice, then it is said the event E has occurred, but if the outcome is other than 1 or 2, then it is said that the outcome has not occurred since it does not belong to subset E. 

 

Trail: Performing a random experiment is called a trial, for example, tossing a fair coin or rolling an unbiased dice. 

 

The free Probability certificate course offered by Great Learning will help you understand the basic concepts of probability, terminologies, operations, and other related concepts. Probability is one of the basic concepts that is applied across various disciplines of sciences, mathematics, and other disciplines such as finance, gambling, etc. This course will help you understand the concept better and apply it in practical situations to predict the outcome of the processes. You will be given a certificate of completion after clearing the assigned tasks. Enroll today to avail of the course for free. Happy Learning!

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Learner reviews of the Free Probability Courses

Our learners share their experiences of our courses

4.48
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Reviewer Profile

5.0

Country Flag Canada
“Fantastic Course. Recommended to Everyone”
This course truly stands out due to its comprehensive content, engaging instruction, and practical applications. Whether you're a beginner or someone looking to deepen your understanding, this course offers valuable insights and hands-on experience that can benefit learners at any level.
Reviewer Profile

5.0

Country Flag India
“It was cool learning new things and making new technologies”
It was cool learning new things and making new technologies.
Reviewer Profile

5.0

“The Course Was Very Good and Covered Many Topics”
The instructor explained all the concepts very clearly, and it was easy to follow him as he focused on different topics.
Reviewer Profile

5.0

Country Flag Qatar
“My Learning Experience Was Good and Understandable”
Excellent pace and use of time. Well delivered. Good subject knowledge. Training has already been put into practice.
Reviewer Profile

5.0

Country Flag India
“Good Experience with All the Videos”
Very good explanation of every subtopic, and the quiz was informative too.
Reviewer Profile

5.0

Country Flag India
“Loved the Tools Used and Flow of Course”
Overall, the course was good. I liked the tools and curriculum that was recent.
Reviewer Profile

5.0

“It Was Amazing. I Really Enjoyed This Course”
I found the curriculum to be very comprehensive and well-structured, covering all the essential topics in depth. The instructor was engaging and explained complex concepts clearly. The quizzes and assignments were challenging but fair, helping to reinforce my understanding. Overall, the course was easy to follow and provided valuable skills and tools for practical application.
Reviewer Profile

4.0

Country Flag India
“Probability and Probability Distributions for Machine Learning: Foundations and Applications”
This course provides an in-depth understanding of probability theory and probability distributions, essential for building a solid foundation in machine learning (ML).
Reviewer Profile

5.0

“A Must-Take for Anyone Looking to Strengthen Their Statistical Foundation. Highly Recommended!”
This course is a fantastic introduction to probability and probability distributions, specifically tailored for machine learning applications. The instructor adeptly explains complex concepts with clarity, making the material accessible. The course seamlessly integrates theoretical knowledge with practical exercises, ensuring learners can apply what they learn to real-world machine learning problems.
Reviewer Profile

5.0

“Great Course, Clear Explanations, and Useful Examples”
The course provides a strong foundation in probability and its applications to machine learning. The explanations of key concepts such as Bayes' Theorem, Binomial, Poisson, and Normal distributions were clear and thorough. Practical Python examples helped solidify the understanding of the topics, making it an engaging learning experience. The course is well-structured and suitable for beginners and those looking to expand their knowledge in the field.

Meet your faculty

Meet industry experts who will teach you relevant skills in artificial intelligence

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Dr. Abhinanda Sarkar

Senior Faculty & Director Academics, Great Learning
  • 30+ years of experience in data science, ML, and analytics.
  • Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.
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Dr. P K Viswanathan

Professor, Analytics & Operations
Dr. P K Viswanathan, currently serves as a professor of analytics at Great Lakes Institute of Management. He teaches subjects such as business statistics, operations research, business analytics, predictive analytics, ML analytics, spreadsheet modeling and others. In the industrial tenure spanning over 15 years, he has held senior management positions in Ballarpur Industries (BILT) of the Thapar Group and the JK Industries of the JK Organisation. Apart from executing corporate consultancy assignments, Dr. PK Viswanathan has also designed and conducted training programs for many leading organizations in India. He has degrees in MSc (Madras), MBA (FMS, Delhi), MS (Manitoba, Canada), PHD (Madras).   Noteworthy achievements: Ranked 12th in the "20 Most Prominent Analytics & Data Science Academicians In India: 2018". Current Academic Position: Professor of Analytics, Great Lakes Institute of Management. Prominent Credentials: He has authored a total of four books, three of which are on Business Statistics and one on Marketing Research published by the British Open University Business School, UK. Research Interest: Analytics, ML, AI. Patents: He has original research publications exclusively on analytics where he has developed modeling and demonstrated their decision support capabilities. These are: Modelling Credit Default in Microfinance — An Indian Case Study, PK Viswanathan, SK Shanthi, Modelling Asset Allocation and Liability Composition for Indian Banks. Teaching Experience: He has been teaching analytics for more than two decades but has been into active and intense teaching since analytics started witnessing a meteoric growth with the advent of R and Python. Ph.D. in the application of Operations Research from Madras University.

Frequently Asked Questions

What is the probability course?

The probability course will cover syllabus on the introduction of the subject, basic terminologies, operations, examples, formulas for different models and working with those models. Enrol in the Great Learning Academy to learn probability courses. 

What is the easiest way to learn probability?

Probability is a very easy concept in mathematics although it is applied in various disciplines of science, mathematics, finance and other domains. The easiest way to learn it is with some guidance. We at Great Learning, will help you understand the concept better. So enroll today to learn the probability course for free. 

What are the basics of probability?

Basic terminologies, operations, formulas, different models used in probability make the base of probability.

Is probability hard to learn?

No, it is very easy to learn probability. It is dealing with simple formulas and applies simple operations. The output need not be deeply analyzed too.