Machine Learning for Healthcare Analytics Projects : Build smart AI applications using neural network methodologies across the healthcare vertical market

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn

Key Features

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

Book 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 book 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 book, 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 chapters, 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.

By the end of this book, you will have learned 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.

What you will learn

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

Who this book is for

Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.

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Machine Learning for Healthcare Analytics Projects : Build smart AI applications using neural network methodologies across the healthcare vertical market

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn

Key Features

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

Book 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 book 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 book, 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 chapters, 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.

By the end of this book, you will have learned 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.

What you will learn

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

Who this book is for

Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.

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