Watch the replay at- https://www.crowdcast.io/e/build-deploy-ml What will you learn? In this workshop, you will learn how to quickly build and deploy machine learning models using Jupyter Notebooks in IBM® Watson™ Studio. Using Jupyter Notebooks we can run small pieces of code that process your data and immediately show you the results of your computation in an interactive environment. Come learn how to use Jupyter Notebooks in a secure cloud environment to analyze data. You will learn how to prepare and normalize the data for machine model building, split data into training sets for model validation, train the model by using machine learning algorithms and finally deploy the model so that it can be accessed outside of the notebook.  Session outcomes What are Jupyter Notebooks and their benefits Data science pipeline stages Learn how to perform data preparation and visualization (hands-on) Exploratory Data Analysis (hands-on) Model training using various Machine Learning algorithms (hands-on)  Agenda Event kickoff with an introduction to Jupyter Notebooks and IBM Watson Studio Hands-on session Future Steps Q&A ‍ Who should attend Anyone who is interested in AI and Data Science, looking to explore a starting point. Data Science & AI enthusiasts who want to learn what IBM is doing in the field. Software developers who want to know more about the field of Data & AI ‍ Prerequisites Log in or Sign up for an IBM Cloud account: https://ibm.biz/BdfEMZ ️ Speakers Sidra Ahmed, IBM Developer Advocate (https://www.linkedin.com/in/sidra-fatima/) Anam Mahmood, IBM Developer Advocate (https://www.linkedin.com/in/anam-mahmood-sheikh/)

1. Developer Series
Join our webinar series to expand your developer skills!
Security Tuesdays
Data Science and AI Wednesdays
Cloud Native and Red Hat OpenShift
Thursdays
meetup.com/IBM-Cloud-MEA

2. Build and deploy your Machine Learning
models effortlessly
—
Sidra Ahmed
Developer Advocate, UAE
Anam Mahmood
Developer Advocate, UAE
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at:
https://ibm.biz/BdfEMZ

3. Let’s get started
• Sign up/Log in to your IBM Cloud
Account
https://ibm.biz/BdfEMZ
• Follow along for the hands-on:
https://github.com/Anam-
Mahmood/watson-studio-learning-path-
assets
Get started at: https://ibm.biz/BdfEMZ
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation

4. 4
chat with everyone!
Q&A here!
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Workshop Resources
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation

5. Survey
https://www.surveygizmo.com/s3/6083679/9e93461cbf3e
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at: https://ibm.biz/BdfEMZ

6. Agenda
6
Jupyter Notebooks 16
Hands-On 17
What is Data Science? 08
What is Machine Learning 09
Supervised and Unsupervised
Learning 10
Classification Algorithms 11
Logistic Regression 12
k- nearest neighbors 13
Decision Trees 14
Random Forest 15
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at: https://ibm.biz/BdfEMZ

7. "Data is the new oil. It’s
valuable, but if
unrefined it cannot
really be used."
-Clive Humby
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at: https://ibm.biz/BdfEMZ

8. What is Data Science?
Data science is an
interdisciplinary field leveraging
insights from many fields to
extract knowledge from data.
8
https://blog.finxter.com/artificial-intelligence-machine-learning-deep-learning-and-data-science-whats-the-difference/
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at: https://ibm.biz/BdfEMZ

9. What is Machine Learning?
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at: https://ibm.biz/BdfEMZ

10. Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at: https://ibm.biz/BdfEMZ

11. Classification
Algorithms
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at: https://ibm.biz/BdfEMZ

12. Logistic Regression
• Extension to the linear regression
algorithm
• Instead of actual continuous value,
we predict the probability of an
outcome
• Sigmoid Function
• It is used when the output is
categorical:
To predict whether an email is spam
(1) or (0)
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148
Get started at: https://ibm.biz/BdfEMZ

13. k-nearest neighbors
(KNN)
• “Birds of a feather flock together.”
• Classifies data points based on the
points that are most similar to it.
• Non-parametric, lazy learning
• Classified by majority vote of K
nearest neighbors
• Assigned to class most common
among K neighbors
• Examples: Pattern recognition,
Recommender systems, image
classification
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
https://importq.wordpress.com/2017/11/24/mnist-analysis-using-knn/
Get started at: https://ibm.biz/BdfEMZ

14. Decision Trees
• Flow-chart like tree structure
• uses training data to derive
classification rules
• Works for both categorical and
continuous input and output
variables
• Non-parametric
• Visualization mimics human thinking
• Easy to understand and interpret
• Prone to over fitting models
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
https://algobeans.com/2016/07/27/decision-trees-tutorial/
Get started at: https://ibm.biz/BdfEMZ

15. Random Forest
Supervised learning approach
• Learning from the input data
• Uses the learning to classify new
observations
• Predicts categorical class labels
• Examples: Speech recognition, Face
recognition, Car type recognition
• Ensemble model that grows multiple
tree and classify objects based on
the “votes” of all the tree
• Fits decision trees on sub-samples of
datasets
• Uses average to improve accuracy of
the model and controls over-fitting
• Slow real time prediction
• Examples: Fraud prediction, stock
market analysis
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
https://towardsdatascience.com/a-quick-and-dirty-guide-to-random-forest-regression-
52ca0af157f8
Get started at: https://ibm.biz/BdfEMZ

16. Image source: https://plotly.com/python/ipython-notebook-tutorial/
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
Get started at: https://ibm.biz/BdfEMZ

17. Hands-On
Get started at: https://ibm.biz/BdfEMZ
• Sign up/Log in to your IBM Cloud
Account
https://ibm.biz/BdfEMZ
 Follow along for the hands-on:
https://github.com/Anam-
Mahmood/watson-studio-learning-path-
assets
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation

18. Usecase
Get started at: https://ibm.biz/BdfEMZ
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation
https://www.predicagroup.com/blog/customer-churn-analysis/
• Churn refers to customers no longer
using a company’s services or products.
• Churn analysis is the evaluation of a
company’s customer loss rate in order
to reduce it.

19. Wrap Up
• Machine Learning is a subset of AI techniques which uses statistical methods to
enable machines to improve with experience.
• Machine Learning has two main types of algorithms: Supervised and Unsupervised.
• Jupyter Notebook, as web-based environment for interactive computing. You can run
small pieces of code that process your data, and you can immediately view the results of
your computation.
• Lastly, we have concluded the presentation with an interactive demo for performing data
preparation and visualization as well as model training using Jupyter Notebooks.
Get started at: https://ibm.biz/BdfEMZ
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation

20. Useful Links
IBM Developer: https://developer.ibm.com/
Meetup: https://www.meetup.com/IBM-Cloud-MEA/
Learning:
https://cognitiveclass.ai/
https://learn.ibm.com/
IBM Watson Studio:
https://www.ibm.com/cloud/watson-studio
Get started at: https://ibm.biz/BdfEMZ
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation

21. Survey
https://www.surveygizmo.com/s3/6083679/9e93461cbf3e
Get started at: https://ibm.biz/BdfEMZ
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation

22. Thank you
Sidra Ahmed
Developer Advocate, UAE
Sidra.Ahmed@ibm.com
@acciogibberish
Anam Mahmood
Developer Advocate, UAE
anam.mahmood@ibm.com
Get started at: https://ibm.biz/BdfEMY
Build and deploy your Machine Learning models effortlessly February 3rd, 2021 / © 2021 IBM Corporation