Data is growing exponentially and we need to utilize the data for various use cases which can be extracted from the data which we acquire. As a part of the Digital Developer Conference: Data & AI, we welcome you to the MEA Regional Event – Data Science & AI for Everyone where we have some really amazing sessions lined up with our developer advocates and some amazing guest speakers, for you to immerse further into the world of Data and AI.
Register for the event, join on event day on following link: https://www.crowdcast.io/e/ddc-mea2021
Sign in/Login into IBM Cloud using: https://ibm.biz/MEA-DataAI
🎓 Sessions happening at the MEA Regional Event (All times are mentioned in GMT )
Agenda and Session Slots:
2:00 PM – Predicting Fraud using Automated Machine Learning
3:00 PM – Speech Synthesis by using Advanced Machine Learning Techniques for Easy Readability of Dyslexic Children
3:10 PM – Building a recurrent neural network using TensorFlow Keras
4:10 PM – Early forest fire detection via Machine Learning
4:20 PM – 5 Minute Short Break
4:25 PM – Branch Specific AI Based Target Management
4:40 PM – Networking
👩💻 Details of Sessions:
Session 1 : 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 which 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 faster decision making process.
Sbusiso Mkhombe – Developer Advocate, IBM
Khalil Faraj – Developer Advocate, IBM
Session 2 : 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.
Geeta Atkar – Assistant Professor in G H Raisoni College of Engineering and Management
Session 3 : 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.
Mridul Bhandari – Developer Advocate, IBM
Anam Mahmood – Developer Advocate, IBM
Emeka Boris Ama – Lead Data Scientist, Law Pavilion
Session 4 : 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
Graciana Puentes – Independent Senior Researcher at National Research Council / Group Leader at University of Buenos Aires
4:20 PM – 4:25 PM (SHORT BREAK)
Session 5 – 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.
Yılmaz Meral – Business Analytics Team Leader at AIMS
Serdar Öztürk – Business Analytics Team Leader at AIMS
Session 6 – Parallel Networking Sessions
Last but not the least we will have a networking sessions with our developer advocates to mentor you on various topics, you can join these sessions by clicking on your session of interest
🎙️ Speaker(s) & Parallel Sessions:
Your Career in Data Science – Qamar un Nisa – Lead Developer Advocate, IBM
Industry Focused Practical uses of AI/ML – Fawaz Siddiqi – Developer Advocate, IBM
Ethics in AI : Build trusted and explainable AI models – Naiyarah Hussain – Lead Developer Advocate, IBM
Deep learning – Anam Mahmood – Developer Advocate, IBM
Dubai, United Arab Emirates