Iris Dataset Classification with SVM and Logistic Regression

Are you new to machine learning and eager to understand how algorithms categorize data? This course provides a foundational understanding of supervised classification, enabling you to confidently apply two powerful algorithms—Support Vector Machines (SVM) and Logistic Regression—to real-world datasets. What you'll learn: * Understand the foundational concepts of supervised machine learning and classification. * Learn the principles and practical application of Logistic Regression models. * Explore Support Vector Machines (SVM) and the role of various kernel functions. * Apply essential data preprocessing techniques for preparing tabular datasets. * Build and train classification models using standard machine learning libraries. * Evaluate model performance using key metrics, data splitting, and cross-validation. * Interpret model results and understand basic hyperparameter tuning concepts. The course begins with an introduction to classification principles, then dives into the mechanics of Logistic Regression and SVMs. You'll progress through practical steps of data preparation, model implementation, and rigorous evaluation. This course is designed for absolute beginners to machine learning, data science, or Python programming, with no prior experience required. Start your journey into machine learning classification today and build a strong foundation for future exploration.