Oracle Academy ML Build-A-Thon

How it Works?

1. Must be a current Oracle Academy Student

You must be a student at Oracle Academy member institute and your institute need to be registered with TASK.

2. Access Oracle Academy Student Hub

Oracle Academy student hub is web-based training faciliated by your faculty at Oracle Academy member institute. Complete the AiML Artificial Intelligence with Machine Learning in Java Learner course before the technical bootcamp.

3. Attend 3 Days Bootcamp

Attend 3 Days live instructor-led hands-on training on ML with Java to understand and work with a real use-case to build projects.

4. Enroll for a Project

Choose any domain of your interest and enroll for the project

5. Submit the project

Once your done with the project submit the files in the GitHub enabled in SmartInternz Workspace.

Program Agenda

3 Days Bootcamp on Machine Learning in Java

Timings : 5:30PM – 8:30PM IST

Mode of Training: Instructor-led (Online via Zoom)

Day-1: Getting Started Day-2: Handling Data Day-3: Introduction to Machine Learning

Eligibility:

You must be a student at Oracle Academy member institute and your institute need to be registered with TASK.
Must Register for the Program before the last date of Registration
Must Build and Submit a Valid Project to Receive the Participation Certificate

Pre-requisites:
Must have knowledge on Java programming
Must have PC with Good Internet Connectivity

Eligibility:

You must be a student at Oracle Academy member institute and your institute need to be registered with TASK.
Must Register for the Program before the last date of Registration
Must Build and Submit a Valid Project to Receive the Participation Certificate

Pre-requisites:
Must have knowledge on Java programming
Must have PC with Good Internet Connectivity

Important Instructions:

1. Student must submit the project source code, associated files in GitHub Repository

2. Project Document shall be prepared using the document writer in Project Work Space

3. Student must update the daily work status through Kanban board in Project Workspace / Slack Channel

4. Video demonstration of working solution (not more than 4 Min.) shall be uploaded to GitHub

5. Upload the code of the program with proper comments and appropriate naming convention followed in the code

11-15 May Level 1 Evaluation
18-19 May Level 2 Evaluation

Frequently Asked Questions

1. Is there any entry fee? 2. I don’t have any coding knowledge but I am interested in participating. Can I? 3. Can I participate as a Team? 4. How will the 3 Days Bootcamp be organised?

5. Will there be any guidance provided to complete the project? 6. What if I don’t work on project? 7. How will I Connect with Mentor? 8. Can a person enroll for more than one project domain?

Title	Optimized Warehouse Management of Perishable Goods for a Food Delivery Company
Description	A food delivery service has to deal with a lot of perishable raw materials which makes it all, the most important factor for such a company is to accurately forecast daily and weekly demand. Too much inventory in the warehouse means more risk of wastage, and not enough could lead to out-of-stocks - and push customers to seek solutions from your competitors. The replenishment of majority of raw materials is done on weekly basis and since the raw material is perishable, the procurement planning is of utmost importance,
Expected Solutions	Machine Learning Model to predict the demand of goods for the next 10 weeks or in the future Mobile / Web Application to Interact with ML Model deployed on IBM Cloud
Technologies & Tools	Python 2 or 3, IBM Watson Studio, IBM Cloud for Deployment, Android / any Web frameworks
Reference	1. https://www.altexsoft.com/blog/demand-forecasting-methods-using-machine-learning/ 2. https://www.kaggle.com/shashkhr25/food-demand-forecasting-challange/data

Title	Predicting the energy output of wind turbine based on weather condition
Description	Wind energy plays an increasing role in the supply of energy world-wide. The energy output of a wind farm is highly dependent on the wind conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction.
Expected Solutions	Develop a time series model to Predict the power output of wind farm based on the weather condition in the site (1Hr prediction to 72Hrs. prediction) Build an application to recommend the Power Grid to suggest the best time to utilize the energy from wind farm
Technologies & Tools	Python 2 or 3, IBM Watson Studio, IBM Cloud for Deployment, Android / any Web frameworks Use Weather Company Data from IBM Cloud
Reference	1.https://hpi.de/friedrich/docs/paper/RE1.pdf 2. https://www.kaggle.com/berkerisen/wind-turbine-scada-dataset