<ul data-eligibleForWebStory="true"><li>Data preparation for machine learning involves key stages like data collection, integration, cleaning, transformation, splitting, feature engineering, annotation, balancing, and final preprocessing checks.</li><li>Data collection entails gathering data from various sources, while integration involves merging data into a unified format. Data cleaning is crucial for removing errors, and transformation prepares data for the ML model.</li><li>Data splitting is essential to avoid overfitting, and feature engineering significantly impacts model accuracy. Data balancing addresses class imbalances, and final preprocessing checks ensure smooth model execution.</li><li>Understanding data preparation is crucial for AI roles. Enrolling in AI training programs can provide practical experience, helping individuals build a competitive edge in the job market.</li></ul>

Artificial Intelligence Course | Best Training Institute

Discover more