2.0

Average Course Rating

This comprehensive course aims to equip students with the essential knowledge and practical skills to excel in machine learning using Python. The topics covered include data preprocessing, feature selection, supervised machine learning, unsupervised machine learning, reinforcement learning, model training, evaluation, and optimization. This course takes a hands-on approach. By leveraging Python's extensive libraries such as scikit-learn, TensorFlow, and PyTorch, students will gain proficiency in implementing various machine learning algorithms and building predictive models. By the end of this course, students will have the confidence and proficiency to apply machine learning algorithms in real-world business scenarios. They will be equipped with the essential knowledge and practical skills to succeed as data scientists or data analysts.