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Fawaz Siddiqi
Fawaz Siddiqi

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Data Science & AI for Everyone

As a part of the Digital Developer Conference: Data & AI ( our team conducted a regional event Data Science & AI for Everyone.

Data Science & AI for Everyone was targeted for all types of developer audience, from beginners to advanced and consisted of various sessions including Machine Learning, Deep Learning and No Code tools as well. Moreover, we were joined by some of our amazing guest speakers who gave an overview of the practical use cases of various technologies & algorithms.


Our first workshop was regarding Predicting Fraud using Automated Machine Learning


Automation and artificial intelligence (AI) are transforming businesses and will contribute to economic growth via contributions to productivity. They will also help address challenges in areas of healthcare, technology & other areas. At the same time, these technologies will transform the nature of work and the workplace itself. In this code pattern, we will focus on building state-of-the-art systems for churning out predictions that can be used in different scenarios. We will try to predict fraudulent transactions which we know can reduce monetary loss and risk mitigation. The same approach can be used for predicting customer churn, demand and supply forecast, and others. Building predictive models require time, effort, and good knowledge of algorithms to create effective systems which can predict the outcome accurately. With that being said, IBM has introduced Auto AI which will automate all the tasks involved in building predictive models for different requirements. We will get to see how Auto AI can churn out great models quickly which will save time and effort and aid in a faster decision-making process.

It was conducted by Sbusiso Mkhombe - Developer Advocate, IBM & Khalil Faraj - Developer Advocate, IBM

In the next session we had one of our amazing guest speakers who talked about Speech Synthesis by using Advanced Machine Learning Techniques for Easy Readability of Dyslexic Children


Now a days, around 60% to 70% of children are facing problem of dyslexia while reading at the age of 7-10 years. There are advanced methods of Generative Adversarial Network which can be applied for speech recognition and support dyslexic children.

It was conducted by Geeta Atkar - Assistant Professor in G H Raisoni College of Engineering and Management

After which we commenced to our 2nd workshop which was about Building a recurrent neural network using TensorFlow Keras


In this workshop, we will learn how to perform language modeling on the Penn Treebank data set by creating an RNN using the long short-term memory (LSTM) unit. The notebook runs on IBM Cloud Pak® for Data as a Service on IBM Cloud®. The IBM Cloud Pak for Data platform provides additional support, such as integration with multiple data sources, built-in analytics, Jupyter Notebooks, and machine learning. It also offers scalability by distributing processes across multiple computing resources.

It was conducted by Mridul Bhandari - Developer Advocate, IBM,
Anam Mahmood - Developer Advocate, IBM, and by one of our guest speakers from OSCA (Open Source Community Africa) and also an IBM Champion
Emeka Boris Ama - Lead Data Scientist, Law Pavilion

Our next session was about climate change and was also a submission for Call for Code which is a yearly challenge based on climate change. The session covered Early forest fire detection via Machine Learning


Forest fires have caused a lot of damage in terms of wildlife as well as climate change, this talk will present the ongoing progress in deployment of image classification algorithms based on neural networks, using the IBM Cloud, for applications in early forest fire detection.

The session was covered by our guest speaker Graciana Puentes - Independent Senior Researcher at National Research Council / Group Leader at University of Buenos Aires

After a short 5 minute break, we had a very industry based use case on AI, the session was about Branch Specific AI-Based Target Management


Setting goals across multiple branches in a financial services organization can be a challenge. Almost everyone is familiar with the scenario in which annual targets are driven primarily by a percentage increase over the prior year’s performance. Consider an alternative, in which goals are uniformly or proportionally allocated across all branches within an organization. For managers that operate a busy branch and dynamic market, that tends to make life easier; whereas branches in highly stable markets are challenged to keep meeting their targets.

We designed a data-driven predictive analytics approach that incorporates historical performance data, branch characteristics, detailed demographic data, information about competitor locations, and more.

The session was conducted by our guest speakers Yılmaz Meral - Business Analytics Team Leader
Serdar Öztürk - Business Analytics Team Leader from AIMS

Networking Sessions


Our final session was a networking session where we covered various topics such as:

Interested in earning a badge on the skills which you have learned? Check out the on-demand sessions and course at the Digital Developer Conference: Data & AI - where you can do a short course and gain a badge :D


Check out the on-demand sessions for Digital Developer Conference: Data & AI:

In case you missed the regional event check out all the resources here:

You can watch the replay of the session here:

To get started, login/sign up for IBM Cloud:

Interested in using Tech for Good? Putting your ideas to action to fight climate change? Join Call for Code, which is a yearly challenge by IBM, where you can join a large community of developers developing various solutions!


Read more about Call for Code here:

Discussion (2)

mccurcio profile image
Matt Curcio

Greetings Fawaz,
In the Networking Sessions section you mention a short course but the link is to create a new user for IBM Cloud. I could not find the short course.
Any suggestions?
BTW, Lots of work/stuff here, very cool.

zakimax profile image

it's great article