What is Machine Learning, its differences between AI and ML, the application areas and what does it need to be a machine learning engineer

1. What is Machine Learning
Paris, 2016, EBG
Martyn Sukys

2. Machine learning (ML) is the science of finding pattern in
data and use these patterns to make predictions. Over time
machines (computers) are enabled without explicit
programming to learn, grow, and change autonomously through
real world interactions.ML grew out of a branch of AI that
studies pattern recognition and computational learning. It
is a subfield of computer science.
Machines can aid in filtering and processing vast amount of
data & to make highly accurate and informed predictions that
can be applied in a number of industries.
What is it?
Why does it matter?

3. Advantages Disadvantages
❏ Multiple applications
❏ Personalisation
❏ Efficiency
❏ Data
❏ Measuring
effectiveness
❏ Nascent stage

4. History
1980s 1990s to 2000 Early to
mid-2010s
Late 2010s 2020s+
Architecture
Server or mainframe
Architecture
Small server clusters
Architecture
Large server farms
(the cloud)
Architecture
Multiple clouds
Architecture
Clouds and fog*
Predominant theory
Knowledge engineering
Predominant theory
Probability theory
Predominant theory
Neuroscience and
probability
Predominant theory
Memory neural
networks
Predominant theory
Networks when sensing
but rules when
reasoning and acting

6. Applications
Retail
● Demand forecasting
● Price optimisation
● Recommendations
● Fraud detection
● Customer segmentation
Finance
● Credit scoring
● Fraud detection
● Risk analysis
● Client analysis
● Trading exchange
forecasting
Travel
● Demand forecasting
● Price optimisation
● Price forecasting
Healthcare
● Increase in
diagnostic accuracy
● Identifying at risk
patients
● Insurance product
cost optimisation
Other
● Object recognition
(video, photo)
● Content
reccomendation
(movies, music,
articles & news)
Marketing
● Market & customer
segmentation
● Price optimisation
● Churn rate analysis
● Customer lifetime value
prediction
● Upsell opportunity analysis
● Sentiment analysis in
social networks

7. ❏ A broad concept where computers
act intelligently on their own
❏ Where computers act according to
their environment
❏ When systems display cognitive
ability similar to humans
❏ Computers make decisions that
maximize their success
❏ One application of AI
❏ Computers observe &
analyze & learn from
experience
❏ Predict future events
based on previous
patterns
❏ Based on pre-programmed
algorithms
Artificial Intelligence (AI) Machine Learning (ML)
Machine Learning VS Artificial Intellience

8. Career in Machine Learning
Job titlesData Scientist
❏ MACHINE LEARNING RESEARCHER
[creates new algorithms,
breaks new ground in ML]
❏ MACHINE LEARNING ENGINEER
[applies algorithms to
address business problems]
❏ DATA ENGINEER [develops code
to support machine learning
solutions]
DATA SCIENTIST
No1 Job for 2016
❏ Number of job openings:
1.736
❏ Median base salary:
$116.849

9. 5 Skills You Need to Become a Machine Learning Engineer