The healthcare industry generates vast quantities of data, and so presents unique opportunities for applying machine learning. The use of machine learning in healthcare can prove transformative in the lives of people around the world. In this course, Machine Learning for Healthcare, you’ll explore machine learning techniques currently applied in the healthcare industry. First, you’ll explore a few specific use cases such as the use of ML techniques for epidemic control, AI-assisted robotic surgery, patient diagnosis, and the automation of administrative tasks. You will also get an intuitive understanding of how convolutional neural networks work and how they are used in medical imaging. Next, you will understand the steps involved in applying machine learning techniques to chronic disease prediction. You will study a case from a research paper that uses natural language processing and text extraction techniques on medical notes to diagnose chronic diseases for hospital patients. Another case study will discuss the use of medical imaging and image preprocessing techniques to detect leukemia from microscopic blood cell images. Finally, you will get hands-on coding and see how you can use regression models to predict blood pressure and classification models to predict liver disease. When you are finished with this course you will have the awareness of how machine learning can be applied in the healthcare industry and hands-on experience working with healthcare data.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more