• Welcome and program overview
• Team formation
• Brainstorming and evaluating ideas
• Identifying pain points and market gaps

To-dos (Deadline: Oct 18):

• Select a team lead from your team.
• Prepare a report using the provided framework/template.
• Submit the report here.

• Overview of market validation
• Techniques for validating product ideas
• Conducting market research
• Competitor and customer analysis

To-dos (Deadline: Oct 25):

• Prepare a report using the provided framework/template.
• Submit the report here.

• Understanding the MVP concept
• Identifying core ML features and functionalities
• Data collection, cleaning, and preprocessing
• Building initial Machine Learning models and prototypes

• Development process overview
• Best practices for coding, model training, and version control
• Importance of testing and validating ML models
• Writing and testing ML code
• Implementing version control using Git and model versioning

• Steps for deploying ML models and applications
• Platforms and tools for ML deployment (e.g., AWS SageMaker, Heroku, AWS EC2, Streamlit)
• Deploying the MVP to a cloud platform

• Importance of customer feedback and iteration
• Strategies for finding first customers
• Gathering and implementing user feedback
• Launching the product to a small audience
• Collecting and analyzing user feedback to improve the product