Healthcare Analytics with Machine Learning

This is a high-level list of topics covered in this course. Please see the detailed Agenda below • Explore super imaging and natural language processing (NLP) to classify DNA sequencing • Detect cancer based on the cell information provided to the SVM • Apply supervised learning techniques to diagnose autism spectrum disorder (ASD) • Implement a deep learning grid and deep neural networks for detecting diabetes • Analyze data from blood pressure, heart rate, and cholesterol level tests using neural networks • Use ML algorithms to detect autistic disorders

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Hours :

Hands on Labs

Requirements

  • This course is geared for attendees with Python skills who wish to know how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain.. Pre-Requisites: Students should have • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow alo
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    ng exercises or team with others to complete them. • Good foundational mathematics or logic skills • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
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Description

Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This course will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the course, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final lessons, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. Working in a hands-on learning environment, led by our Python expert instructor, students will learn about and explore: • Develop a range of healthcare analytics projects using real-world datasets • Implement key machine learning algorithms using a range of libraries from the Python ecosystem • Accomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologies

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About the Instructor

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About the Instructor

Ernesto Lee is an impassioned blockchain entrepreneur and technologist from the Miami/Fort Lauderdale Area. For over 25 years Ernesto has been asking hard questions and pursuing tough answers. Ernesto was an original founding member, co-owner and the CTO of Blockchain Training Alliance and former Chief Solutions Architect at TechBlue.com. Presently, Ernesto is the CEO at Ernesto.Net and Engineer at Kaiser Permanente.  Ernesto’s career illustrates a lifelong commitment to pushing the envelope on innovation and growing opportunities for all around him.

As a graduate of Old Dominion University (BS, Physics), Virginia Tech (MS, Software Engineering), and Harvard Extension School (Graduate Certificate, Business Communication), Ernesto has always had a passion for technology and teaching. It has been a cornerstone of Ernesto’s career.