The document outlines key aspects of implementing machine learning (ML), including defining problems, understanding data quality, and selecting appropriate learning types. It emphasizes the importance of effective feature engineering, data representation, and performance metrics while managing ML projects and expectations. The presentation also offers practical tips for project management, such as starting with a minimum viable product and leveraging A/B testing.

1. LEARN HOW TO
MAKE MACHINE
LEARNING WORK
Presented by Faris Hassan

2. Intro
Farisology
- Ai majored
- Medical imagine researcher
- Data scientist in Fave
- Pod savings (Co-founder)

3. Objectives
Functioning ML
1- Learning the 4 aspects of a functioning
machine learning project.
2- Understanding the underlying process for
correct learning problem diagnoses.

4. Covered Today
Aspects of a functioning ML
Problem statement
Data issues
Evaluation metrics
Optimisation
final notes
Summary

5. Analytics
DESCRIPTIVE
What has happened?
Widely spread across
industries
PREDICTIVE
predicting the future
The main insights
generator for data-
driven organizations
PRESCRIPTIVE
Recommend steps to
achieve desired state
Widely used in leading
organizations
Machine learning domain

6. Prescription
Are solving the right problem?
Machine Learning is an automation tool that
is aided with intelligence based on historical
events.

7. CUSTOMER SERVICE
There is a drop in customer satisfaction, more customers
complaining our long time handling their enquires and
often it result in the bad experience.

8. ML material
IS YOUR PROBLEM DEFINTION MACHINE
LEARNING USE-CASE?

9. TYPES OF LEARNING
SUPERVISED
You have the targeted data
of the prediction collected
over time, and or annotated.
UNSUPERVISED
You want to explore and
discover segments and
similarities of your data.
RENFORCED
Machine is expected to
learn from the environment
and model proper
behaviour.

11. Safety tip
How do you know your use case?
You can do your research to find similar use-
cases and learn about possible approaches
to solve the problem.
Frame the problem to naturally fit one of the
learning types.

12. Data issues
How to have adequate
representation?
Data is not monolithic, static, or
homogeneous. Especially when you are
working with supervised learning problem,
two thirds of your potential issues are rotted
to your data, class representation, outliers,
and others.

13. Insufficient Data
Bad Quality
Under/Overfitting
Sampling Bias
Irrelevancy of
Features
MAIN DATA ISSUES
To look at early

14. Sufficiency of data
Its not always about the size
Certain ML use-cases are sufficient with few
hundreds of examples. Insufficient data
cause increase in variance which contributes
to performance issues like overfitting.

15. PREDICTING SETTELMENT
COMPUTATION
Computation methods
for the same category
of case might vary.
DEAL
The case settled to be
part of a major or minor
deal offer.
OUTSIDE
INFLUENCE
Case settled under the
influence of threat,
blackmail, and or cut
loses.

16. Non-representative
Inaccrurate representation
The Machine Learning model can only
perform and generalise well when it's trained
on a data that reflects the natural
representation of the cases in real
environment.

17. Skewness
Imbalanced data where a selection of
the data is represented.
Survey empty data
In surveys you find a question that is
ignored, this make representation
incomplete.

18. Fitting
Generalization is the key

19. POOR
QUALITY
MISSING VALUES
Unrecorded to empty
observations in the data.
ERRORS
For example presence of
invalid digits or non text in
a text data.
OUTLIERS
Outliers can be handled
either by deletion or by
winsorizing.
NOISE
Irrelevant data combined
into the dataset.

20. IRRELEVANT FEATURES
GENERATION
Using existing features
combined with new ones to
generated new set of
features.
EXTRACTION
Extracting features that are
new, like using computer
vision tehniques to extract
image descriptors.
ML main component is features engineering which takes the following steps:

21. "Coming up with features is
difficult, time-consuming,
requires expert knowledge.
‘Applied machine learning’ is
basically feature engineering"
Prof. Andrew Ng

22. Bias/Variance Tradeoff

23. METRICS
How to measure success?
WHAT METRICS TO LOOK AT?

24. Statistics
MODEL ACCURACY
PRECISION
SENSITIVITY
MAE
Business KPIs
ROI
CR
CTR
INTANGIBLES

25. OPTIMIZATION
BETTER USER EXPERIENCE
Personalised and localised content plays an important
role in giving the user a unique experience tailored to the
prefrences of the user.
IMPROVE PERFORMANCE
Dynamic pricing and product recommendations are
applications of optimisation where algorithmically
products are picked and presented to increase likelihood
of conversion.

26. MAJOR
CHALLENGES
Managing expectations
Unrealistic expectation is by far the worst enemy
for your project.
Project management
Managing your data science or machine learning
project is similar and different than managing
software engineering projects.
Tracking and monitoring
Tracking the performance might require more
resources and expertise.
Maturity
Driving your ML to maturity is time consuming,
requires a lot of efforts and support.

27. Best tips
MVP FIRST
Scope down your project into a small feature, go live
and build on from there.
A/B TESTING
There is usually more than one model that performs
well in theory, A/B testing is your only friend to know
which is better for your use-case.
LEVERAGE ON TECH
CICD, containerisation, and Kubernetes are going to be
your best hits in managing the product and delivery.

28. Be Inspired
IMAGINATION IS MORE
IMPORTANT THAN KNOWLEDGE.
ALBERT EINSTEIN

29. ADDITIONAL READING
AI MACHINE LEARNING GUIDE
An executive guide to AI
10 RED FLAGS
Save your data analytics program
NOTES FROM THE AI FRONTIER
the Value of machine/deep learning

30. Get in Touch
We'd love to hear your thoughts
ADDRESS
127.0.0.1
PHONE
012 566 9870
EMAIL
fareshasan.ai@gmail.com