Are you familiar with regression - https://m.youtube.com/watch?v=aq8VU5KLmkY ?
One way to view Ml is regression on steroids....

Let's consider supervised learning first. You are given n labeled data
points,

( x1,y1),...,(xn,yn). Your objective is to find a function f(x)=y that best
predicts y on a new batch of x's. When y is continuous it is called regression
and when its discrete it is called classification.

There are two things to notice right away

1. To solve this an optimization problem is defined, e.g., a minimization
of square error in our original regression problem

2. Trying to explain the given data completely which is sometimes called
extrapolation is actually a pitfall, you may capture random trends and your
prediction power may be hindered. This is called overfitting

The basic intuition underlying many approached to the classification
problem is that had we known p(x, y) and given a new x we would have calculated
p(x, y) for each y and choose y with the greatest probability. The difficulty
is that it is not easy to estimate p(x, y).

A simplifying independence assumption leads to the naive Bayes approach
that is intuitively covered in the first part of Ariel Kleiner's crash course
on ML at http://ampcamp.berkeley.edu/wp-content/uploads/2012/06/ariel-kleiner-ampcamp-2012-machine-learning-part-1.pdf.

References

1. Introduction to programmers on why ml is useful to master -

Ignores the challenges of applying it where it excels and dealing with
drift.

2. Nice overview that start with classification https://m.youtube.com/watch?v=z-EtmaFJieY only
thing to be careful of is the claim that neural network are not statistical
models. Estimating a neural network performance should be done using the same
standard statistical tools, e.g., cross validation.

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