This presentation targets people with a beginner background of Artificial Intelligence and Machine Learning, so anyone interested in understanding the working of the machine learning model and how to implement them in general is welcome to join us.

1. Maha K. Alhobishi
Naimah A. Alaqeel
Exploring Machine
Learning & TensorFlow

4. Machine Learning Track
1- Baseline: Data, ML, AI.
2- Intro to ML: Language Processing.
3- BigQuery for Machine Learning.
4- Intermediate ML: TensorFlow on
GCP.
5- Machine Learning APIs.

7. Outline
Section 1
Intro to Artificial Intelligence
Section 2
Machine Learning
Subfield of Machine learning
Application of Machine learning
Section 3
Introduction to TensorFlow

8. Why Artificial Intelligence?
What Artificial Intelligence?
0
1
02
Artificial Intelligence

9. Artificial Intelligence and Machine Learning
Artificial Intelligence
Machine learning

10. Machine Learning
What Why
How
“The goal of machine
learning is to build computer
systems that can adapt and
learn from their experience.”
• Economically efficient.
• consider larger data.
• Can formalize learning
problem to explicitly
identify goals and criteria.
When

11. Machine Learning
Data
Program
Output
Program
Data
Output
Traditional Programming
Machine Learning

14. The Prosses of Machine Learning
Data
Preparation
Feature
Engineering
Data
Modeling
Performance
Measure
1 2 3 4
DATA ALGORITHMS MODEL
Performance
Improvement
5

17. Subfield of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning

18. Supervised Learning

19. Supervised Learning
 Supervised Learning is the first type of machine learning, in which labelled data used to
train the algorithms.

20. Unsupervised Learning
 Unsupervised Learning type of machine learning, which unlabelled data used to train the
algorithms.

21. Semi-supervised Learning
 Semi-supervised Learning: an algorithm learns from a dataset that includes both labeled and
unlabeled data, usually mostly unlabeled.

22. Reinforcement Learning
 Reinforcement learning: the agent that acts on its environment, it receives some evaluation of
its action (reinforcement), but is not told of which action is the correct one to achieve its goal

23. Machine learning is not
a dogma!!

24. Applications of ML

25. Applications of Machine Learning
Netflix Gmail Mario Game

26. Google Cloud
Platform
Suit of computing services provided by google.

27. Google Cloud Platform

28. Introduction to
TensorFlow

29. TensorFlow
TensorFlow is an open source framework developed by researchers to run machine
learning, deep learning and other statistical and predictive analytics workloads.
TensorFlow
Architecture
Preprocessing the
data
Build the model
Train and estimate
the model

31. Register in Quick Lab
01
Start AI Platform Lab
02
Follow the instructions
03
YOUR
MODEL IS
READY
04
How?

32. Quick Labs TensorFlow Lab
01 Register in Quick Lab

33. @soil_sa
Our Partners
@IAU_CCSIT
IAU_CCSIT soil_sa
Our Partners

34. khmahaa
Mahaakhh@gmail.com
Maha Alhobishi Naimah Alaqeel
@NaimahAaqeel
naimahaq77@gmail.com
Speakers
DSC
@DSC_IAU