Data Preparation

To ensure the success of the platform, it is crucial to prepare the data effectively. This will involve collecting, cleaning, and organizing the data from various sources, such as courses, PDF books, and other materials. The following steps outline the data preparation process:

Data Collection and Preprocessing

• Compile all relevant text data from resources, including course transcripts, PDF books, seminar notes, and coaching materials.
• Extract textual content from non-text sources, such as converting video transcripts or presentation slides into a text format.
• Obtain any available metadata, such as course topics, difficulty levels, or target audience information, which may be useful for enhancing the model's performance.

Data Preprocessing

• Clean and normalize the text data by removing any irrelevant elements.
• Standardize the text by converting it to lowercase and, if necessary, applying other normalization techniques.
• Ensure that any personally identifiable information (PII) or sensitive data is removed or anonymized to protect user privacy and maintain compliance with data protection regulations.

Data Organization

• Organize the text data into a structured format, such as a CSV file, JSON file, or a database, to facilitate efficient processing and analysis.
• Create a coherent and logical hierarchy of topics, subtopics, and sections within the educational materials to enhance the model's understanding of the content.
• If necessary, split the text data into smaller chunks or paragraphs to make it more manageable for training and fine-tuning the AI model.

Data Validation

• Perform quality checks on the prepared data to ensure that it is clean, consistent, and accurate.
• Address any issues or inconsistencies discovered during the validation process to ensure that the data is of the highest quality before proceeding with model training.