Sonical.ly is an early stage gaming and education tech startup whose mandate is to make music creation easily accessible to everyone. The company was founded by Juno Award winning K-pop music producer/product designer/educator Elmo Chong and K-pop songwriter/developer/designer Andrew Choi, who are spearheading an exciting new phase of iterative growth towards product market fit.

To elevate successful outcomes, Sonical.ly has enlisted an elite board of advisors that are accomplished experts in AAA gaming and Venture Investing. These advisors include:

1. Charles Huang: Music game legend and founder of the Guitar Hero franchise.
2. Jungwon Hahn: Former Managing Director of Blizzard Entertainment.
3. John Kim: Silicon Valley Venture Capitalist for Amasia .

Problem we would like to solve:

The target demographic for our learning tools/games are music creators from the ages of 13-25. We would like to explore potential ML solutions to make recommended education paths for our users based on their different music genre tastes and the amount of experience they have making music.

1. Understanding User Needs and Educational Goals

• User Research: Conduct surveys, interviews, and usage data analysis to understand the needs, preferences, and challenges of our target audience.
• Goal Setting: Define clear educational objectives for users, such as mastering a specific music theory concept, improving instrument skills, or enhancing songwriting abilities.

2. Data Collection and Preparation

• Data Identification: Identify the types of data needed for the project, such as user interaction data with the app, music creation data, feedback on educational content, etc.
• Data Collection: Gather data through app usage tracking, user feedback mechanisms, and potentially external music creation datasets.
• Data Cleaning and Preprocessing: Clean and preprocess the data to ensure quality and relevance for machine learning models.

3. Feature Engineering and Model Development

• Feature Selection: Identify and create features that could be indicative of user preferences, learning progress, and engagement levels.
• Model Selection: Choose appropriate machine learning algorithms based on the project objectives, such as recommendation systems for personalized content, predictive models for user engagement, or natural language processing for analyzing user feedback.
• Model Training and Validation: Train the models using the prepared datasets and validate their performance using appropriate metrics.

4. Integration and Deployment

• Integration Planning: Plan how the machine learning models will be integrated into the existing app/game infrastructure to enhance user experience or educational outcomes.
• Deployment: Deploy the models into the production environment with considerations for scalability and performance.
• Monitoring and Maintenance: Set up systems to monitor the models' performance and update them as necessary based on user feedback and changing data patterns.

5. Evaluation and Iteration

• Impact Assessment: Evaluate the impact of the machine learning models on user engagement, learning outcomes, and overall app performance.
• User Feedback: Collect user feedback on the changes and improvements made to the app/games.
• Iterative Improvement: Use the insights gained from the evaluation and feedback to refine the models and strategies continuously.