I am required to complete a project on ML applications. I guess there is a lot of statistics in ML, not helpful for a non-maths background. I am getting too bogged down by notations. There are too many notations.I am trying to read about what Bernoulli trials are and I can't relate to it.What is a Bayesian setting and why is Bayesian thing everywhere? What makes it such an omnipotent distribution?Are there any theorems/theories that I must know ? Any book/notes/resources where I could learn about these stuff in a relatable way (I have very little maths background, but I can learn)?
Understanding Bernoulli Trials, Bayesian Setting
machine learning;beginner;bayesian
What is a Bayesian setting and why is Bayesian thing everywhere?In very simple terms:Bayesian is a statistical setting, where the likelihood of an event  happening (called the posterior) depends on the prior trials or  observations (called the prior(s)).Bayesian networks is an extension of the above, forming a chain or a network of inferencing.Are there any theorems/theories that I must know?For understanding the Bayesian paradigm, you need to know the Bayesian theorem/relation, which is basically:$$P(\theta|d) = \dfrac{P(d|\theta)P(\theta)}{P(d)}.$$Any book/notes/resources where I could learn about these stuff in a  relatable way (I have very little maths background, but I can learn)?I would highly recommend Doing Bayesian Analysis by John Krushke