Best Free Probability Courses

Find the best online Free Probability Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings.

Probability in R. Discrete Random Variables

Infermath links mathematical theory with programming application to give high level understanding of quantitative fields

Created by Pawel Dudko - Financial Engineer


Students: 10819, Price: Free

Probability in R is a course that links mathematical theory with programming application. Discrete Random Variables series gives overview of the most important discrete probability distributions together with methods of generating them in R. Fundamental functionality of R language is introduced including logical conditions, loops and descriptive statistics. Viewers are acquainted with basic knowledge of numerical analysis.

Course is designed for students of probability and statistics who would like to enrich their learning experience with statistical programming. While basic knowledge of probability and calculus is useful prerequisite it is not essential. The suggested method of using the course is by repeating the reasoning and replicating the R code. Therefore it is essential for students to download and use R in the course.

The course consists of twelve short lectures totaling two hours of video materials. Four major topics are covered: Bernoulli distribution (2 lectures), binomial distribution (3 lectures), geometric distribution (3 lectures) and Borel-Cantelli lemma (4 lectures). Eight lectures are presented in a form of writing R code. Remaining four lectures focus solely on theory of probability.

How is Infermath different from other education channels? It equips students with tools and skills to use acquired knowledge in practice. It aims to show that learning mathematics is not only useful but also fun and inspiring. It places emphasis on equal chances in education and promotes open source approach.

Building a Binary Classification Model in Azure ML

What's the probability you'd live or die on the Titanic?

Created by Mike West - Creator of LogikBot


Students: 10139, Price: Free

"First impressions are "Finally, a practicing educator" Course delivery is smooth and spot on. Right before you lose hope a gem like this pops up - thanks."  - Don Councill

Welcome to Building a Binary Classification Model in Azure ML.

Microsoft’s goal of democratizing machine learning is taking shape.

Taking predictive analytics to public cloud seems like the next logical step towards large-scale consumerization of Machine Learning. Azure ML does just that, while making it significantly easier for the developers to build high probability machine learning models without a PhD in statistics.

In this course, we are going to build one of the simplest and most common models, the binary classification model.

The goal of binary classification is to categorize data points into one of two buckets: 0 or 1, true or false and to survive or not to survive.

Many decisions in life are binary, answered either Yes or No. Many business problems also have binary answers. For example: “Is this transaction fraudulent?”, “Is this customer going to buy that product?”, or “Is this user going to churn?” In machine learning, this is called a binary classification problem.

We will use binary classification to predict the probability of someone surviving if they had been aboard the Titanic.

We are going to create an end to end workflow. We will download the data set, clean it, model it, evaluate it then publish our results so others can use it.

Upon completing the course you’ll know how to create a model that accurately predicts the survivability of an individual based on attributes in the data set.

You’ll gain insight into the vernacular used in machine learning. 

For example, in the last sentence I used the world ‘attribute.’  An attribute in machine learning is no different than a column in a data set.

Various attributes affect the outcome of the prediction. For example, my chance of survival was 21.07% if I would have been in first class. If I would have been in second class my changes dropped to 2.16%. Either way, I wouldn't have made it. 

Thanks for your interest in Building a Binary Classification Model in Azure ML..  We will see you in the course!!!

Axiomatic Probability – Mathematics

Understanding Axiomatic Approach to Probability in Mathematics

Created by Great IT Courses - Mathematics


Students: 3681, Price: Free

In Basic High School mathematics, you'll come across three approaches to probability:

  • Empirical Probability

  • Statistical Probability (Classical Probability)

  • Axiomatic Approach to probability

This course teaches the axiomatic approach to probability by discussing the theory first and then using many useful typical example. In the end, you will be able to calculate the probability of almost any typical event, as long as it is not beyond the scope of this text.

This course is also a part of a road map that takes from the basics of mathematics (pre-algebra - class 6) all the way up to calculus (class 12) based on the Indian system of education (NCERT). The text used here will give you a sturdy and robust foundation in mathematics provided that the whole road map is studied from beginning to end. The road map is highly recommended if you are planning a career in science, mathematics or engineering. 

To access the road map, please search for "Great IT Courses" on the internet. on the website, please read the page titled as, "Mathematics 6-12 Standard". The same page has also been linked from the second lecture of the course.

To locate this particular course in the road map, please go to the page related to "Class 11". This course is title as, "16. Probability".

Thank you! 

Pre-Analysis & Probability I : Sets, Functions & Logic

Abstract Boolean Algebra

Created by Arnold Ruymgaart - AP Ruymgaart


Students: 1540, Price: Free

Although an easy introduction requiring only high school algebra, this is a preparatory course for those who are beginning their study of quantitative fields requiring advanced math such as the hard sciences, engineering, CS, data science or mathematics. This course is also well suited for those of any age (high school and up) who simply want to learn more about math. The main topics here are sets, set Cartesian product, relations and functions, operations and their properties & Boolean algebra/logic from an abstract and set theory perspective.

Math AT Home – Probability

Learning the concepts of Basis Math

Created by Sashi Bhaskar - Academic Counsellor


Students: 1418, Price: Free

This course contains all the chapters that are important to understand basic Math, like number system, statistics, geometry, linear equations, variables, square and cube of numbers and circles. With the help of these chapters one can easily clear all the concepts required to be at par with the rest. Students from 6th to 9th grade can watch the videos and learn and practice as well.

In number system we will learn all about plotting numbers on a number line. With visual graphics one can easily get what is being taught. In the case of linear equations after learning about equations in 1 variable we will also see how with more than one variable. In geometry and we learn all about different polygons and focus on triangles and quadrilaterals. In case of circles since it is the most important part of geometry and consists a lot of theorems we have taken good examples to clear all doubts. In variables we will be monomial, binomial and much more.  Well when we talk about finding squares and cubes of numbers we will get to know the best short cut tricks to find them in minutes.

We have designed the course in such a way that each chapter in divided in small videos. All the very bet.

Pre-Analysis & Probability II : Groups, Rings & Fields

Set Theory & Group Theory

Created by Arnold Ruymgaart - AP Ruymgaart


Students: 919, Price: Free

This course is about algebraic structures. It includes an introduction to identity, inverse and idempotent elements. We'll see that Boolean conjunction and disjunction can be seen as multiplication and addition and investigate possible additive and multiplicative identities and inverses. This is followed by power sets and basic operations on elements of power sets (union, intersection, complement, difference). All concepts are then combined in a discussion of algebraic structures including groups, rings and fields. We end with Boolean rings and describe set algebra as an example Boolean ring. In the review session we complete an example proof from CH1 of W. Rudin's Principles Of Mathematical Analysis using the axioms of a field.

Learn Probability- Concepts & Examples of Probability

A course on Probability that boosts your confidence and inspires you to solve probability problems with an ease.

Created by Sandeep Kumar Mathur - Faculty (Maths)


Students: 453, Price: Free

If you find it difficult to remember various formulas of Probability ? If you have a feeling of not being confident in Probability ? If you facing difficulty in solving Probability questions and feel that you need to strengthen your basics? Then you have come to the right place.

Probability is an important branch of statistics. It helps in solving many problems arise in practical situations. Generally many questions do come from this topic in competition exams. The course is useful for both beginners as well as for advanced level. Here, this course covers the following areas in details:

  • Probability as classic approach

  • Basics of Permutations and combinations

  • Various types of events

  • Conditional Probability

Each of the above topics has a great explanation of concepts and excellent and selected examples.

I am sure that this course will be create a strong platform for students and those who are planning for appearing in competitive tests and studying higher Mathematics and statistics.

You will also get a good support in Q&A section . It is also planned that based on your feed back, new material like Random variable ,mean , variance  etc. will be added to the course. Hope the course will develop better understanding and boost the self confidence of the students.

Waiting for you inside the course!

So hurry up and Join now !!

Probability Theory

By MJ the Fellow Actuary

Created by Michael Jordan - Actuary (FASSA/CERA)


Students: 437, Price: Free

Welcome to this short course on Probability Theory.

We will cover the following topics:

  • Set Theory

  • Venn Diagrams

  • Probability Theory

  • Conditional Probability

  • Bayesian Probability

We will then look at a few questions that are based on the actuarial exams.

This course at a First Year University Level and assumes you have had an introduction to Probability at school. The course is quite mathematical and you might want to brush up on your maths abilities before attempting it.