Basic Understanding of Probability for Machine Learning

Basic Understanding of Probability for Machine Learning udemy course

Probability and Beyond: Advanced Concepts and Applications

Once upon a time in the city of Athens, there was a young mathematician named Anna. Anna was always fascinated by the chance of uncertainty and she knew that Probability was the only way to understand this. Wanting to share her knowledge with the world, she created a Probability Course.

This Course was open to all and it promised to make Probability easy to understand. As the students gathered, Anna began her class by rolling a dice. She asked her students the probability of getting 7 by rolling two dice.

Under Anna's guidance , the students learnt that there were multiple ways to get 7 by rolling two dice. As the course progressed, Anna introduced her students to conditional probability, independent events, Bayes Theorem and more. One of Anna's favourite parts of the course was when the students experimented with random events and realised how to distinguish between a random and Binomial variable.

By the end of the course, Anna had inspired her students to understand Probability, not as something to be feared, but to help them understand the world around them. And so, Probability, became a beloved course where students of all backgrounds came to learn, thanks to a mathematician named Anna.

Have you wanted to learn Probability on your own? Each problem here follows a different approach. Here's helping you to understanding Probability better.

I am Suman Mathews. I have a double master's in Mathematics and I have taught Maths to high school and college students for the past three decades. A good knowledge of Probability will also go a long way in helping you in SAT and GRE QUANT exams.

To start with, you'll learn the basic definition of Probability and how to apply it in problem solving. You need to keep in mind that probability=(number of favourable events)/total number of events. You'll learn about Conditional Probability, Mutually exclusive and independent events and properties regarding these.

Moving on, you learn about Total Probability and how it leads to Bayes' Theorem. There area a number of solved examples illustrated here for you to practice. This comes to the end of the first part of the course.

Next, you'll learn what are Random variables and how to calculate the mean and variance of discrete and continuous random variables. The course teaches you how to distinguish between a discrete and continuous variable. Moving on, you'll come to Binomial distributions and how to check if a variable is a random variable or follows Binomial distribution.

This is extremely important and the course teaches you how to distinguish between the two. Learn how to calculate the mean and variance of random variables and a Binomial distribution. You're also given all the formulas taught in the course as one module.

Bonus-Learn about the Poisson Distribution in Probability. You'll learn about the mean and variance of the Poisson distribution and its application in problems.

Enrol for the course and enhance your learning. Thank you!