An introduction to Machine Learning!

1. MACHINE
LEARNING
Let machine do the work!

2. What is
Machine
Learning?

3. Why
should
machines learn?

4. So why is
everyone
talking
about
Machine
Learning?

5. Objectives of
Machine Learning :
Task Oriented
studies
Cognitive
simulation
Theoretical
analysis

6. Difference
between Deep
Learning,
Machine
Learning and
Artificial
Intelligence

8. There are many kinds of algorithms
which come under supervised
learning, some of them are:
Decision tree
Ordinary least square regression
Support vector machines

9. Decision tree
It is the minimum number ofYes/No questions that
one has to ask to assess the probability of making
the correct decision

10. Ordinary Least Squares
Regression:
It is a task of fitting
a line through a set
of data points in such
a way that the sum
of the distances
between these
points must be least

11. Support vector machines:
Say you have some points of two types in a paper
which are linearly separable. SVM will find a straight
line which separates those points into two types and
situated as far as possible from all those points.

12. Unsupervised learning also have
many kinds of algorithms. Some of
them are:
Clustering algorithms
Principle component analysis
Single value decomposition

13. Clustering algorithms
Clustering is a task of grouping set of objects such
that objects in the same group (cluster)are more
similar to than hose in other groups

14. Principle component analysis
PCA is a statistical procedure that uses an orthogonal
transformation to convert a set of observations of
possibly correlated variables into a set of values of
linearly uncorrelated variables called principal
components.

15. Independent component analysis:
ICA is a statistical
technique for
revealing hidden
factors that
underlie sets of
random variables,
measurements, or
signals.

16. Applications:

18. A presentation by Chamundeswari and
Anitha…
Thank you…

19. Any
queries???