Description
This course provides a comprehensive introduction to Machine Learning (ML), a branch of artificial intelligence that enables computers to learn from data and make decisions or predictions. Participants will gain a solid foundation in ML concepts, algorithms, and practical techniques used to develop predictive models and solve real-world problems. The course covers key topics including supervised and unsupervised learning, regression, classification, clustering, dimensionality reduction, model evaluation, and feature engineering. Through hands-on projects and exercises, participants will learn to implement machine learning algorithms using Python and popular libraries such as scikit-learn and TensorFlow.
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