This machine learning course teaches students key concepts and techniques such as supervised and unsupervised learning, model selection and evaluation, feature engineering and selection, and deep learning with neural networks. Students will have hands-on experience using popular tools and libraries such as scikit-learn, TensorFlow, and feature selection methods. By the end of the module, students will have a strong foundation in machine learning and statistical modeling, enabling them to develop effective solutions in diverse domains.