This study presents a simple and effective approach based on the application of computer vision combined with machine learning and deep learning models to identify and detect common errors in performing squat exercises. The model was trained on a dataset composed of self-recorded and online videos, video segmented into complete workout sequences, and categorized into four main error groups. Our method uses pose estimation to extract skeletal keypoints and compute joint angles such as knee angle, torso angle, and back angle, which are then processed by a Gated Recurrent Unit (GRU) model integrated with an attention mechanism to recognize and classify technical errors. The experimental results demonstrate that the model can detect errors with high accuracy, helping gym practitioners improve their technique and reduce the risk of injury during training.