Learning how to build an AI model involves understanding a structured process that includes data collection, preprocessing, algorithm selection, training, evaluation, and deployment. In this blog, we walk through each step—from cleaning and labeling datasets to choosing suitable models like neural networks or decision trees. You'll discover essential tools like TensorFlow, PyTorch, and Scikit-learn, as well as techniques to avoid overfitting, improve accuracy, and scale for production. We also cover validation methods, performance metrics, and real-world deployment strategies. Whether you're a beginner or a developer seeking to improve model performance, this guide provides a comprehensive roadmap to building AI models that deliver real-world value across domains such as finance, healthcare, marketing, and automation.