We always start with Bayes formula for Bayesian Learning. In my oppinion this is not intuitive at all because the actual crux of Bayesian learning comes from this statement
With frequentist approach we are updating parameters for model ( Lets call that model of data ) that models data. Bayesian learning goes further. Instead of updating directly parameters of data modeling function, we update the distribution (Lets call that distribution of parameters) that models the parameters for the function modeling the data. Difference is that each parameter for function that models the data now gets it’s own probability distribution. Each instance of parameter now gets its own probability.
The distribution of parameters is created and updated by iterative update process as we get more and more data. In crux: the more likely to see certain data with parameter $\theta$, the more likely we are going to make that $\theta$
$P(\theta|data)=P(\theta)P(data|\theta)$
The simplest way to think of this is to imagine you have a some arbitrary function F() with 10 possible input parameters $\theta_1,\theta_2,..\theta_{10}$ and you have some data that this F() tries to model. ( Don’t think about what this F() is but think that you can get from it $P(data|\theta_i)$: the likelihood of seeing data assuming parameter $\theta_i$ )
You will now try out to get the likelihood of seeing data with each of these parameters