We were conducting machine learning classes for past 5 Weeks at maitri every Saturday 6:30 pm

The topics we were covered so far :

1.Machine learning introduction;

2.Regression

* Centrality- mean,mode,median, variance

3.Distributions

*Gaussian distribution

* Binomial distribution

4.Data prepocessing - consists of testing, validation,training.

Here is our today's session summary:

      FUNCTIONS

Definition:

The mapping of elements from domain to co domain

OUr Gokul differentiated continuous and non continuous function with a simple graph, then Arun describes about co-variance and explains how it differ from independent

We concluded the session by discussing on K nearest neighbours and it's drawbacks...

IMPORTANT FORMULAS :

1.F is continuous at x,if

ε>0,|f(x-γ)-f(x-γ)|<ε

2.variance= summation of 1 to n (x-µ)^2

3. In K nearest neighbours

Distance formula=

√(x1-x2)^2+(y1-y2)^2