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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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Last Updated

January 19, 2021

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Course 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.

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Course Curriculum

    • Objective of this project 00:00:00
    • Detecting breast cancer with SVM and KNN models 00:00:00
    • Training models 00:00:00
    • Detecting diabetes using a grid search 00:00:00
    • Introduction to the dataset 00:00:00
    • Building our Keras model 00:00:00
    • Performing a grid search using scikit-learn 00:00:00
    • Reducing overfitting using dropout regularization 00:00:00
    • Finding the optimal hyperparameters 00:00:00
    • Optimizing the number of neurons 00:00:00
    • Generating predictions using optimal hyperparameters 00:00:00
    • Classifying DNA sequences 00:00:00
    • The dataset 00:00:00
    • Fixing missing data 00:00:00
    • Splitting the dataset 00:00:00
    • Training the neural network 00:00:00
    • A comparison of categorical and binary problems 00:00:00
    • ASD screening using machine learning 00:00:00
    • Introducing the dataset 00:00:00
    • Splitting the dataset into training and testing datasets 00:00:00
    • Building the network 00:00:00
    • Testing the network 00:00:00

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