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 BorelCantelli 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 largescale 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 (prealgebra  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 612 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!
PreAnalysis & 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.
PreAnalysis & 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.