Explore and Learn Machine Learning Concept

In my last blog [ Essential Maths for ML - Part 2 ]  we have discussed about addition rule, multiplication rule of probability and conditional probability  .  In this blog, we will discuss the Bayes Theorem which plays an important role in most of the machine learning algorithms. So let’s consider A1, A2, A3, and A4 be the mutually exclusive and exhaustive event of a random experiment. Let B be the common event i.e. the event B is made-up of 4- mutually exclusive and exhaustive events.   P(B) = P(A1 ⋂ B) + P(A2⋂B) + P(A3⋂B) + P(A4⋂B) P(B) = Σ P(Ai ⋂ B)…………(1) We already know from the concept of the conditional probability that P(A1⋂ B) = P(B) * P(A1/B) P(A1/B) = P(A1⋂ B) / P(B)………(2) Replacing the value of P(B) from the eq2 we can say that P(A1/B) = P(A1 ⋂ B) / Σ P(Ai ⋂ B) P(A1/B) = P(A1) * P(B/A1) / Σ P(Ai ⋂ B) So the Bayes Theorem states that if A1, A2, A3……….An, are n mutually exclusive and exhaustive events with prior probabilities P(A...

Let's discuss some terms and their definitions related to statistics and probability. It will help us in brushing our concepts of probability which are essential for machine learning algorithms Definition of Probability  Probability is a numerical measurement which indicates the chances of occurrence of an event, say A. It is denoted by P(A). It is the ratio of favorable outcomes of an event A say m to the total outcomes of the experiment say n. P(A) = m/n where m represents the number of favorable outcomes of an event A and n is the total number of outcomes of the experiment. Let's understand the experiment term in more details now. An operation that results in a definite outcome is called an experiment E.g.  Tossing a coin is an experiment as it can have two outcomes either Head or Tail and it is definite in number. Throwing a fair dice is an experiment as it can have only 6 outcomes which is definite in number. Random Experiment When the outcome of an ex...