Machine Learning for Healthcare Analytics Projects
Build smart AI applications using neural network methodologies across the healthcare vertical market
Seiten
2018
Packt Publishing Limited (Verlag)
9781789536591 (ISBN)
Packt Publishing Limited (Verlag)
9781789536591 (ISBN)
Machine Learning in the healthcare domain is booming because of its abilities to provide accurate and stabilized techniques. This book is packed with new methodologies to create efficient solutions for healthcare analytics. We will build five end-to-end projects to evaluate the efficiency of AI apps to carry out simple-to-complex healthcare ...
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 DescriptionMachine 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 forMachine 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.
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 DescriptionMachine 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 forMachine 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.
Eduonix Learning Solutions creates and distributes high-quality technology training content. Our team of industry professionals has been developing workforces for more than a decade. We aim to teach technology the way it is used in industry and the professional world. We have a professional team of trainers for technologies ranging from mobility, web enterprises, and database and server administration.
Table of Contents
Breast Cancer Detection
Diabetes Onset Detection
DNA classification
Diagnosing Coronary Artery Disease Using machine Learning
Screening Children for Autistic Spectrum Disorder using machine learning
| Erscheinungsdatum | 02.11.2018 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-13 | 9781789536591 / 9781789536591 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Eine praxisorientierte Einführung
Buch | Softcover (2025)
Springer Vieweg (Verlag)
CHF 53,15
Künstliche Intelligenz, Macht und das größte Dilemma des 21. …
Buch | Softcover (2025)
C.H.Beck (Verlag)
CHF 25,20
Buch | Softcover (2025)
Reclam, Philipp (Verlag)
CHF 11,20
