answer the following question with detail steps and code, thank you.

A Bernoulli distribution has the following likelihood function for a data set D: where Ni is the number of instances in data set D that have value 1 and No is the number in D that have value 0. The maximum likelihood estimate is Ni + No (a) (5 points) Derive the maximum likelihood estimate above by solving for the maximum of the likelihood. Le., show the mathematics that get from Equation (1) to Equation (2) (b) (5 points) Suppose we now want to maximize a posterior likelihood p(D) where we use the Bernoulli likelihood and a (slight variant of a) symmetric Beta prior over the Bernoulli parameter p@) x (1-0). Derive the maximum posterior mean estimate A Bernoulli distribution has the following likelihood function for a data set D: where Ni is the number of instances in data set D that have value 1 and No is the number in D that have value 0. The maximum likelihood estimate is Ni + No (a) (5 points) Derive the maximum likelihood estimate above by solving for the maximum of the likelihood. Le., show the mathematics that get from Equation (1) to Equation (2) (b) (5 points) Suppose we now want to maximize a posterior likelihood p(D) where we use the Bernoulli likelihood and a (slight variant of a) symmetric Beta prior over the Bernoulli parameter p@) x (1-0). Derive the maximum posterior mean estimate