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Course Skill Level:

Foundational to Intermediate

Course Duration:

1 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and basic IT & Linux skills

Who should attend & recommended skills

  • Python experienced developers, analysts or others with Python skills who are intending to create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn, and 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.
  • Skill-level: Foundation-level machine learning skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them.

About this course

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.

Skills acquired & topics covered

  • Developing a range of healthcare analytics projects using real-world datasets
  • Implementing key machine learning algorithms using a range of libraries from the Python ecosystem
  • Accomplishing intermediate-to-complex tasks by building smart AI applications using neural network methodologies
  • Exploring super imaging and natural language processing (NLP) to classify DNA sequencing
  • Detecting cancer based on the cell information provided to the SVM
  • Applying supervised learning techniques to diagnose autism spectrum disorder (ASD)
  • Implementing a deep learning grid and deep neural networks for detecting diabetes
  • Analyzing data from blood pressure, heart rate, and cholesterol level tests using neural networks
  • Using ML algorithms to detect autistic disorders

Course breakdown / modules

  • Objective of this project
  • Detecting breast cancer with SVM and KNN models
  • Training models

  • 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

  • Classifying DNA sequences

  • The dataset
  • Fixing missing data
  • Splitting the dataset
  • Training the neural network
  • A comparison of categorical and binary problems