About This Course

This Machine Learning course offers a comprehensive foundation in the principles, algorithms, and practical applications of machine learning. It is designed to equip students with both theoretical understanding and hands-on skills necessary to build intelligent systems. The course begins with an overview of the evolution and paradigms of machine learning, including supervised, unsupervised, and reinforcement learning approaches. Emphasis is placed on understanding the differences between classification, regression, and clustering techniques, and when to apply them. A significant portion of the course is dedicated to data preprocessing techniques, including data scrubbing, normalization, binning, handling missing values, and dimensionality reduction through methods like Principal Component Analysis (PCA). As the course progresses, learners explore various machine learning algorithms such as Linear Regression, Decision Trees, k-Nearest Neighbors, Support Vector Machines, and ensemble methods. Model evaluation techniques including confusion matrices, precision, recall, F1-score, and ROC curves are discussed in depth to ensure effective assessment of model performance. Advanced topics such as overfitting, underfitting, cross-validation, and hyperparameter tuning are also covered to develop robust models. By the end of the course, students will be able to independently carry out a machine learning pipeline from data collection to model deployment. This course is ideal for students aspiring to pursue careers or research in artificial intelligence, data science, and analytics