Building an AI model involves several key steps, starting with defining the problem and gathering quality data. Data preprocessing is essential to clean and structure the dataset, followed by selecting a suitable algorithm or model architecture, such as a neural network or decision tree. Training and testing phases refine the model’s performance, and tuning helps optimize results. Once tested, the model is deployed in production, where it can be continuously monitored and improved based on real-world feedback. Understanding these steps is crucial for developing reliable, efficient AI models that deliver accurate insights and support data-driven......See More