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Machine Learning for Healthcare Analytics Projects

  • Course Code: Data Science - Machine Learning for Healthcare Analytics Projects
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 1 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn

Course Snapshot 

  • Duration: 1 days 
  • Skill-level: Foundation-level machine learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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 

Topics Covered: 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 

Audience & Pre-Requisites 

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 along 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 

Course Agenda / Topics 

  1. Breast Cancer Detection 
  • Objective of this project 
  • Detecting breast cancer with SVM and KNN models 
  • Training models  
  1. Diabetes Onset Detection 
  • Detecting diabetes using a grid search 
  • Introduction to the dataset 
  • Building our Keras model 
  • Performing a grid search using scikit-learn 
  • Reducing overfitting using dropout regularization 
  • Finding the optimal hyperparameters 
  • Optimizing the number of neurons 
  • Generating predictions using optimal hyperparameters 
  1. DNA Classification 
  • Classifying DNA sequences 
  1. Diagnosing Coronary Artery Disease 
  • The dataset 
  • Fixing missing data 
  • Splitting the dataset 
  • Training the neural network 
  • A comparison of categorical and binary problems 
  1. Autism Screening with Machine Learning 
  • ASD screening using machine learning 
  • Introducing the dataset 
  • Splitting the dataset into training and testing datasets 
  • Building the network 
  • Testing the network 
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