All about Machine Learning code in Python
- Bayesʼ theorem: P (H|X) = P(X|H) P (H) / P(X)
- binomial distribution: It is used when there are exactly two mutually exclusive outcomes of a trial
- conditional probability A probability computed under the assumption that some probability holds
- confidence intervals: It is a range of values which we are fairly sure that true value lies in
- distribution: How often each value appears
- estimation: Data is used from sample to estimate characteristics of population.
- hypothesis testing: To see aparant effects and evaluate wheather the effect is real.
- multinomial distribution:It is generalization of binomial distribution; Find probabilites in experiments where there are more than two outcomes
- non-parametric models: A lot of data is available but no prior knowledge
- normal distribution: Also called Gaussian and the bell curve
- probabilistic distribution: A list of all the events of an experiment together with the probability associated with each event
- random variables: It represents a process that generates a random number
- regression: Describe relationships between variables
- variance: It is intended to describe the spread