This document discusses machine learning and was created by a group of 4 students. It defines machine learning as systems that can learn and improve automatically from experience without being explicitly programmed. It provides examples like chess games and e-commerce recommendations. The document then discusses common machine learning applications like spam filtering, translation, and virtual assistants. It lists the 7 steps of machine learning as gathering data, preparing data, choosing a model, training, evaluation, parameter tuning, and prediction. Finally, it discusses trends in machine learning like improving algorithms and data gathering, and applications such as object detection, medical diagnosis, and fraud detection.

2. MACHINE LEARNING

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

9. 7 STEPS OF MACHINE LEARNING

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

17. GROWTH OF MACHINE LEARNING
By Hammad Ashraf

18. MACHINE LEARNING APPROACH'S
 Speech recognition
 Natural language processing
 Computational biology
 Robot control

19. o MEDICAL OUTCOMES ANALYSIS

20. TREND ACCELERATING
 Improve machine learning through improve
algorithms
 Improve data gathering
 Networking

21. TREND ACCELERATING
COUNT…
 New sponsors/ IO devices
 Faster computer

22. APPLICATIONS OF MACHINE LEARNING
OBJECT DETECTION:

23. MEDICAL DIAGNOSIS

24. BRAIN MACHINE INTERFACE

25. DETECTING CREDIT CARD FRAUD

26. SPEECH RECOGNITION

27. PEDESTRIAN DETECTION

28. GAME PLAYING

29. CHEMICAL INFORMATICS