Transfers ToCSUDescriptionThis course introduces machine learning and deep learning through a blend of concepts and hands-on Python practice. Students explore supervised, unsupervised, and reinforcement learning, applying algorithms for classification, regression, clustering, and dimensionality reduction. Emphasis is on data preparation, model development, evaluation, and improvement, including basic neural network design using frameworks like TensorFlow or PyTorch. By course end, students can design, implement, and assess predictive models for real-world applications. Total of 36 hours lecture and 54 hours laboratory. 