1. Machine Learning

2. Data generation When personal computers and wireless communications
come to the world After that we becomes data generator.
Every time each and every person generates data.
when we are visiting any webpages, go to shopping
malls, buy something's, post on social media etc.

3. Patterns on data
Data have some pattern Using which machine can predict
future.
Not exact but he predict
Approximation.
Data are in random way.

4. Algorithm Sequence of instruction carried out to transform i/p to o/p.
There is no any particular algorithm. We have no logic
But we have data in random form. Using which we have
To trained our model.
Static line of coding: sorting algorithms
Dynamic line of coding: machine learning

5. What is machine learning
Its a procedure in which we program a computer so that
It improves his performance on basis of past data or past
Experience.
it is dynamically programed
Machine learning gives computer, the ability to perform
New things using past data .

6. Applications Image detection. Facial recognition. B612 camera.
Google search.

7. Human can does but how a robot can?

8. Types of ML.
Works with labeled data
Works with unlabeled data

9. linear Regression in supervised learning.
Error = line value – point value.

10. We have to choose the best line Contains
more no. of points.

11. Classification. Means Taking an input and map it to some category.
X= true and False.
X= male or female.
Works on discrete data.

12. Clustering.
Same types of data are in single cluster.

13. Neural Networks. Neuron:
Term “neural” is derived from the human nervous system’s
basic functional unit “neuron” or “nerve cells” which are
present in brain.
Neuron receives, processes and transmits information
Through electrical and chemical signals.

14. Biological neuron: Synapses (axon terminal) Dendrites Axon
Soma (cell body)

15. Neural Network Technique.
Neural network technique is designed to perform the
works which are performed in human brain by neuron.
It able to perform like human brain.
It takes one or more inputs, sum them and produce
output.

16. Working of Neural Network.

18. Project on Machine Learning.
Green: predicted value.
Red : actual value.

19. Thank You