Logistic regression machine learning,
in this course we will learn about Logistic Regression Machine Learning course. Logistic regression is a powerful and widely used algorithm for classification problems, especially binary classification. In this course, you'll start by understanding the mathematical foundation behind logistic regression, including the sigmoid function and cost function. You will then implement logistic regression models step-by-step using Python libraries such as NumPy and Scikit-learn. The course covers important topics like feature scaling, decision boundaries, model evaluation using accuracy, precision, recall, and ROC curves. You'll also explore how to handle overfitting using regularization techniques like L1 and L2. Real-world datasets will be used to provide practical experience in applying logistic regression to problems such as spam detection, customer churn, and medical diagnosis. By the end of the course, you'll be equipped with both the theoretical understanding and practical skills needed to confidently use logistic regression in machine learning tasks. Learn With Jay
