Breast Cancer Detection with AI – Using Logistic Regression

Advancements in machine learning have significantly impacted the healthcare industry, enabling more accurate and efficient diagnostic models. This course provides a comprehensive guide to building a breast cancer detection model using logistic regression, one of the most widely used classification techniques in medical diagnostics.

Through a structured, hands-on approach, you will learn how to preprocess medical data, develop a predictive model, and evaluate its effectiveness. By the end of this course, you will have a solid understanding of logistic regression and its role in machine learning for healthcare applications.

What You Will Learn:

• Understand the fundamentals of logistic regression and its application in medical diagnosis
• Perform data preprocessing, including handling missing values and preparing datasets
• Train and optimize a machine learning model for breast cancer detection
• Implement logistic regression using Python and Scikit-Learn
• Analyze model performance and interpret results effectively
• Explore the role of AI and machine learning in medical diagnostics

Course Highlights:

• Step-by-step guidance suitable for beginners and professionals
• Real-world breast cancer dataset for hands-on learning
• Best practices for improving logistic regression model performance
• Insights into the impact of AI and machine learning in healthcare

By the end of this course, you will have the knowledge and practical experience to develop a logistic regression-based breast cancer detection model and apply machine learning techniques to real-world medical data.

Enroll now to gain hands-on experience in AI-driven breast cancer detection.