#42 Machine Learning & Data Science Challenge 42
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
What are binomial distribution and polynomial distribution?
Binomial Distribution:
A binomial distribution can be thought of as simply the probability of a SUCCESS or FAILURE outcome in an experiment or survey that is repeated multiple times.
- The binomial is a type of distribution that has two possible outcomes (the prefix “bi” means two, or twice).
For example:
- a coin toss has only two possible outcomes: heads or tails, and taking a test could have two possible outcomes: pass or fail.
Multinomial / Polynomial Distribution:
Multi or Poly means many. In probability theory, the multinomial distribution is a generalization of the binomial distribution.
For example:
it models the probability of counts of each side for rolling a k-sided die n times.
For n independent trials each of which leads to success for exactly one of k categories, with each category having a given fixed success probability, the multinomial distribution gives the probability of any particular combination of numbers \of successes for the various categories.
