Probability
Learn the fundamentals of probability with this comprehensive course. Suitable for mathematics students, high school students, and software developers interested in machine learning and data science. Covers various types of probability, conditional probability, random variables, and distributions. Get ready for competitive exams like IIT and JEE with practice questions and quizzes. Improve your understanding of probability and boost your academic grades.
What you’ll learn
- Fundamentals of Probability – Experiment, Random Experiment, Sample space, Event and Mutually exclusive events
- Types of Probability – Classical Probability, Statistical Probability, Axiomatic Probability, Addition theorem
- Conditional Probability – Multiplication theorem, Independent event, Partition of sample space, Total probability
- Bayes theorem
- Random Variable – discrete, continuous
- Discrete random variable – Probability Mass function, distribution function, probability distribution, expectation, moments, variance, standard deviation
- Binomial distribution, Poison distribution
- Continuous Random Variable – probability density function, distribution function, expectation, variance, standard deviation
- Normal distribution, Standard normal distribution
Basics to Advanced Probability course designed to teach students with simple and easily understandable concepts with exercise and examples about probability. All level of students is encouraged to learn probability. This course will be useful for students to get good grades in their academics. Also, it is the basics for the Machine learning, so this course will be helpful to software engineers and engineering students who wants pursue job opportunities in the Machine learning field.
This course will be useful for students taking following competitive exams:
· IIT entrance exam
· JEE entrance exam
Any competitive exams which have probability.
In this course you will learn
· Types of Probability – Classical Probability, Statistical Probability, Axiomatic Probability, Addition theorem
· Conditional Probability – Multiplication theorem, Independent event, Partition of sample space, Total probability
· Bayes theorem
· Random Variable – discrete, continuous
· Discrete random variable – Probability Mass function, distribution function, probability distribution, expectation, moments, variance, standard deviation
· Binomial distribution, Poison distribution
· Continuous Random Variable – probability density function, distribution function, expectation, variance, standard deviation
· Normal distribution, Standard normal distribution
I will update this course with competitive exam questions and quiz. Please send me your feedback to my email. Also, please feel free to ask any questions related to this course or mathematics.
Who this course is for:
- Mathematics students for their academics
- High school students
- Software developers to learn the mathematics basics for Machine learning and data science
