Implement machine learning algorithms to real-world life sciences problems
About This Video
- Apply machine learning concepts to real-world scenarios
- Implement popular ML algorithms
Machine learning today has changed the way we look and the way we interact with technology. Even the healthcare sector is being transformed by the ability to record massive amounts of information about individual patients; the enormous volume of data being collected is impossible for the human to analyze. Machine learning provides a way to automatically find patterns and reasons for data, which enables healthcare professionals to move to personalized care.
This course covers five python implementations with the project series, which will explore medically related data sets by solving the critical issues using state of the art machine learning techniques.
- This course will give you the hands-on experience working on the Breast cancer detection project. We will be training a K nearest algorithm model as a support vector machine to predict whether the cell is cancerous or not.
- The second project is followed with the Diabetes onset prediction. We will be focusing on neural networks that will help users to learn similar implementation on a wide variety of problems.
- The third project will be the DNA classification project, here we will using the sequence of equal eye DNA as our input data, by creating a classification based machine learning algorithm.
- The fourth project will be the Heart disease prediction project. In this course, we will be building a training algorithm that predicts coronary heart disease.
- The fifth project will take you to the Autism screening, which will cover several supervised learning techniques to diagnose Autistic Spectrum Disorder based on behavioural features.
This course is perfect for anyone who wants to quickly get up-to-speed on the applied Machine Learning with python to the health care sector.
All the necessary code files are placed at: https://github.com/PacktPublishing/Applied-Machine-Learning-For-Healthcare
Table of Contents
- Chapter 1 : Introduction
- Chapter 2 : Breast Cancer Detection
Chapter 3 : Diabetes Onset Detection
- Deep Learning Grid Search - The Dataset Part 1 00:12:17
- Deep Learning Grid Search - The Dataset Part 2 00:13:45
- Deep Learning Grid Search - Batch Size and Epochs Part 1 00:14:35
- Deep Learning Grid Search - Batch Size and Epochs Part 2 00:15:39
- Deep Learning Grid Search - Learning Rate and Dropout 00:13:40
- Deep Learning Grid Search - Initialization, Activation, and Neurons Part 1 00:13:59
- Deep Learning Grid Search - Initialization, Activation, and Neurons Part 2 00:13:00
- Chapter 4 : DNA Classification - The Dataset
- Chapter 5 : Heart Disease Prediction with Neural Networks
- Chapter 6 : Autism Screening
- Title: Applied Machine Learning For Healthcare
- Release date: December 2018
- Publisher(s): Packt Publishing
- ISBN: 9781789951189