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Free Probability Courses

Probability is the branch of mathematics that focuses on numerical descriptions of the occurrence of an event. It is applied in all branches of sciences.

Great Learning offers free Probability Courses to help you understand the mathematics guiding behind Computer Science branches. The Probability course introduces you to the ruling mathematical concepts with examples.

The Probability and Normal Distribution course will help you understand various concepts in Normal Distribution. Probability for Data Science course discusses various theories and theorems applied in the Data Science branch of Computer Science. Probability for Machine Learning explains various probability distributions and theorems with demonstrations to operate Machine Learning tasks. The Statistical Learning course solves mathematical aspects of Computer Science by employing statistics and probability calculations. Enroll in these free courses to learn Probability and earn certificates.

 

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Taught by industry experts and top faculty

Begin your learning journey

Key Highlights

certificate icon
Earn an industry-recognized certificate
flexible schedule icon
Start anytime, learn on your schedule
expert instructors icon
Taught by industry experts and top faculty

Empowering millions through professional learning

Empowering millions through professional learning

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All Probability Courses

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  • Free Probability Courses

    Probability and Probability Distributions for Machine Learning

    Great Learning Academy

    Probability and Probability Distributions for Machine Learning

    star 4.49 · 19.9K+ learners · 1.5 hours

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

    Free icon Free

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    Probability and Probability Distributions for Machine Learning

    star 4.49 · 19.9K+ learners · 1.5 hours

    What you’ll learn:

    • Probability - Meaning and Concepts
    • Rules for Computing Probability
    • Marginal Probability and its Example

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    Probability and Normal Distribution

    Great Learning Academy

    Probability and Normal Distribution

    star 4.39 · 2.1K+ learners · 1.5 hours

    Skills: Probability Distribution, Normal Distribution

    Free icon Free

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    Probability and Normal Distribution

    star 4.39 · 2.1K+ learners · 1.5 hours

    What you’ll learn:

    • Problem Discussion
    • What is Probability Distribution?
    • What is Normal Distribution?

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    Probability for Data Science

    Great Learning Academy

    Probability for Data Science

    star 4.47 · 54.1K+ learners · 1.5 hours

    Skills: Basics of Probability, Marginal Probability, Bayes Theorem

    Free icon Free

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    Probability for Data Science

    star 4.47 · 54.1K+ learners · 1.5 hours

    What you’ll learn:

    • Basics of Probability
    • Marginal Probability
    • Bayes Theorem

    View Course

    Probability and Probability Distributions for Machine Learning

    Great Learning Academy

    Probability and Probability Distributions for Machine Learning

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

    star 4.49 · 19.9K+ learners · 1.5 hours
    Free icon Free

    View Course

    Probability and Normal Distribution

    Great Learning Academy

    Probability and Normal Distribution

    Skills: Probability Distribution, Normal Distribution

    star 4.39 · 2.1K+ learners · 1.5 hours
    Free icon Free

    View Course

    Probability for Data Science

    Great Learning Academy

    Probability for Data Science

    Skills: Basics of Probability, Marginal Probability, Bayes Theorem

    star 4.47 · 54.1K+ learners · 1.5 hours
    Free icon Free

    View Course

    Learner reviews of the Free Probability Courses

    Our learners share their experiences of our courses

    4.47
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    1%
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    5.0

    “In-Depth Explanation of Statistical Overview of Probability in ML”
    The course structure was easy to follow. The instructor explained each point in a very detailed and easy-to-understand manner.

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    5.0

    “My Learning Experience in the 'Probability for Machine Learning' Course on Great Learning”
    This experience has broadened my understanding of how to quantify and manage uncertainty in machine learning tasks, which will be crucial for my future work in AI and data-driven projects.

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    5.0

    “The Practical Sessions on Jupyter Notebook”
    This helps me to succeed in my Data Science career. It will also help to improve my statistics skills.

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    4.0

    “Informative Course, Well-Structured, Engaging!”
    Informative course: The course provides valuable and useful information that enhances the learner's knowledge. Well-structured: The organization and layout of the course are logical and easy to follow, making it simpler for learners to understand the material. Engaging: The course captures the interest of the learners, keeping them motivated and actively involved in the learning process.

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    5.0

    “It's Great for Learning and I Am Completely Satisfied”
    It is a good course. Keep going, we need this type of course to improve our studies.

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    5.0

    “Easy to Follow and Topic Depth Was Good”
    The adjustable schedule was so comfortable to follow and helped a lot to complete the course.

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    4.0

    “A Course About the Basics of Probability: Good for Beginners”
    This is a good course for anyone who is starting with probability and data analysis.

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    5.0

    “Clear Explanations and Step-by-Step Solutions”
    I really appreciated the clarity and thoroughness of the explanations provided. The step-by-step breakdown of each problem made it easy to follow and understand the concepts. The examples were relevant and well-explained, which helped solidify my understanding of probability calculations. Additionally, the response time was quick, and the assistance was tailored to my specific questions, making for a very effective and enjoyable learning experience.

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    4.0

    “Probability for Data Science Course by Great Learning”
    The Probability for Data Science course by Great Learning provides a comprehensive introduction to probability concepts, crucial for data analysis and machine learning. The content is well-structured with clear explanations and practical examples. It significantly improved my understanding of conditional probability, random variables, and key distributions. Highly recommended for beginners!

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    5.0

    “Probability for Data Science: Basic and Marginal Probability”
    I appreciate Probability for Data Science because it provides essential tools for making informed decisions from data. Understanding probability helps in modeling uncertainty, predicting outcomes, and interpreting results. It enhances analytical skills and is foundational for algorithms in machine learning, enabling better insights and more accurate predictions.

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    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!

    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

    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.