3. GROUP MEMBERS
Irfan Abbas 14-ARID-1410
Hamza Wilayat 14-ARID-1409
Hammad Ashraf 14-ARID-1407
Kamran Saleem 14-ARID-1412
4. WHAT IS MACHINE LEARNING..
o Machine learning is an application of artificial
intelligence (AI) that provides systems the ability to
automatically learn and improve from experience
without being explicitly programmed. Machine
learning focuses on the development of computer
programs that can access data and use it learn for
themselves.
o Examples: Chess game, suggestion product in e-
commerce
5. HAVE YOU EVER WONDERED
How Google classify your
email as spam/Non Spam.
How Google translate
translate to more then 100
languages
6. CONT….
Indigent voice assistant
Siri for IOS
Cortana for windows
Self Driving cars
Siri, Cortana gives you a
correct replies
7. WHY DO WE NEED MACHINE LEARNING
Fast working(xerox carbon copy)
Handle complex task
Accuracy rate
Example
Stanford student
Radio helicopter
Sort of amazing stuns
Flips & hovering upside, down
10. GATHERING DATA
This step is very crucial as the quality and quantity
of data gathered will directly determine how good
the predictive model will turn out to be. The data
collected is then tabulated and called as Training
Data.
11. DATA PREPARATION
The data is loaded into a suitable place
Prepared for use in machine learning training
The order is randomized
12. CHOOSING A MODEL
The next step that follows in the workflow is
choosing a model among the many that
researchers and data scientists have created over
the years. Make the choice of the right one that
should get the job done.
13. TRAINING
After the before steps are completed, you then
move onto what is often considered the bulk of
machine learning called training where the data is
used to incrementally improve the model’s ability to
predict
Initialize random values for A and B
Each cycle of updating is called one training step
14. EVALUATION
Once training is complete, you now check if it is
good enough using this step
You evaluate the results and conducts whether they
meet your desire or not
15. PARAMETER TUNING
Once the evaluation is over, any further
improvement in your training can be possible by
tuning the parameters
There were a few parameters that were implicitly
assumed when the training was done
16. PREDICTION
So this is the final step where you get to answer few
questions
This is the point where the value of machine learning is
realized