3. What’s the Agenda?
Clearing our Basics
Understanding AI ML DL
There is more to it
Setting up your Weapon
Understand your Weapon
Know the Libraries
Run the Code
What you want next?
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4. Know your Trainer 4
Milan Singh Thakur
Artificial Intelligence & Machine Learning Evangelist
Executive Director – Future Technologies with CIS
OWASP Mobile Project Global Leader
International speaker, trainer, learner
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5. What is the Confusion? 5
Where to start from?
Which course is better?
Should I take online/offline training?
Which direction will it take my career?
Do I need to work with lot of math?
Do I need to be a programmer?
Will my colleagues understand what I am doing?
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7. AI, ML and DL..??
Artificial
Intelligence
Machine
Learning
Deep Learning
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8. Why to use ML now?
Too much of data available
Computational power is increasing
Algorithmic research is breaking innovation barriers
Increasing support and interest of industries
ML + Cyber Security is, “The Endless Game”
IoT and IIoT needs to be smarter
Hackers have weaponized ML, when will you?
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13. Check if you are “Still Awake” 13
I want to calculate my share value. Which category?
I want to understand what my customers are expecting in next
product?
My defense system drone has to identify the enemy and execute?
My bank detects ATM fraud very quickly, how?
Face unlock on my mobile?
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14. Leveraging ML in Industry
Predictive maintenance
Demand forecasting
Workforce management
Consumer buying patterns and recommendations
Device monitoring, incident reporting, advanced analytics
Airbus has launched “Factory of Future” – a Boeing creates nearly half
terabyte data during every flight
Bentley uses it for quality vs customer satisfaction prediction
IBM Watson is extensively used in Healthcare industry for patient data
analysis, diagnostics and help doctors for decision making
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15. How you can get into Machine Learning?
Get into basic courses by Andrew NG
Install jupyter notebook – it has all you need (Yolo, PIL, pandas, sk-learn
& more)
Run sample code: iris flower, object detection
Understand you data
Understand how labelling of data is done
Look into how noise is removed
Be wise to choose right algorithm
Validate the output
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16. Setting up you environment
http://jupyter.org/install
Download and install Anaconda with python3
Download and install jupyter notebook
Start the notebook server
$Jupyter notebook
Visit http://localhost:8888 for Notebook dashboard
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18. What Libraries to focus on?
TensorFlow:
Originally developed by researchers and engineers from the Google
Brain team within Google’s AI organization, it comes with strong support
for machine learning and deep learning and the flexible numerical
computation core is used across many other scientific domains.
Numpy:
Primarily used for scientific computing, has a powerful N-dimensional
array object, useful linear algebra, Fourier transform, and random
number capabilities
OpenCV:
Multi-core processing, hardware acceleration, real-time applications
Usage ranges from interactive art, to mines inspection, stitching maps
on the web or through advanced robotics
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19. Pandas:
Library providing high-performance, easy-to-use data structures and
data analysis tools for the Python programming language
PIL – Pilow
The Python Imaging Library adds image processing capabilities to your
Python interpreter
Yolo:
You Only Look Once (YOLO), real-time object detection system based
on a Pascal Titan X it processes images at 30 FPS and has a mAP of
57.9% on COCO test-dev
Scikit-learn:
Simple and efficient tools for data mining and data analysis, Accessible
to everybody and reusable in various contexts. Built on NumPy, SciPy,
and matplotlib
StatsModels
matplotlib
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20. What are my Algorithms? 20
Classification
SVM
Nearest
Neighbors
Random
Forest
Regression
SVR
Ridge
Regression
Clustering
K-Means
Spectral
Clustering
Mean shift
Dimensionality
Reduction
PCA
Feature
selection
Non-negative
matrix
factorization
Model
Selection
Grid search
Cross
validation
metrics
Preprocessing
Preprocessing
Feature
extraction
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21. Run Run Run…!!
Let’s run our first code of ML
See Object detection video using Yolo
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22. What’s in Next Session (Part 2)…!!
Jumping into Libraries
Writing code for object detection
Natural Language Processing (NLP)
Neural Networks
SVM
Deep dive into Algorithms
Decision Trees and Forests
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23. “
”
A Vision to Secure the
Nation
COUNCIL OF INFORMATION SECURITY
Thank you for being part of this session. Your participation and feedback is
valuable for us.
Email us at: info@aimlglobal.org
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