Probability Basics for Machine Learning
Progress: 0%
Measure Theory Continue
Progress: 0%%
Description: # Measure Theory Measure theory is one of the most important analysis in theoretical probability. But what is measure theory? Why is it so important to theoretical probability? How does it work? Where can measure theory be applied? These are common questions that would ring your brain and the objective of this notebook is not only to try and answer the above questions, but also to convince and build an interest in continuing learning about measure theory. ### What is a Measure?
Read more..

Theory of Probability Continue
Progress: 0%%
Description: # Theory of Probability <br/> ## Probability Spaces
Read more..

Mutually Exclusive Events Continue
Progress: 0%%
Description: # Mutually Exclusive Events ## What are mutually exclusive events?
Read more..

Independent Events Continue
Progress: 0%%
Description: # Independent Events Consider the example of drawing a ball from a bucket consisting of many different colored balls. For simplicity, consider that there are only two different colored balls - 6 Green balls and 4 Red balls, 10 balls in total. Now draws can be made in two different ways: * with replacement - The ball drawn in every single draw is kept back into the bucket, keeping the total number of balls and mixture of colored balls constant, from draw to draw.
Read more..

Conditional Probability Continue
Progress: 0%%
Description: # Conditional Probability Conditional Probability is a key concept in Probability Theory. If there are two events A and B, the conditional probability of an event B is the probability that the event will occur given the knowledge that an event A has already occurred. The conditional probability is specified as P(B|A) or probability of B given A (meaning the probabilty of event B given event A has already occured).
Read more..

Discrete Distributions or Probability Mass Functions (PMFs) Continue
Progress: 0%%
Description: # Discrete Distributions or Probability Mass Functions (PMFs) Probability distribution is a function that generates the probabilities of occurrence of all possible outcomes in an experiment. Consider an experiement of rolling of the die. If the random variable X is used to denote the outcome of the die roll, then the probability distribution of X would take the value $\frac{1}{6}$ for $X \to \{1, 2, 3, 4, 5, 6\}$.
Read more..

Continuous Distributions Continue
Progress: 0%%
Description: # Continuous Distributions ## Uniform Distribution
Read more..