Machine Learning for Healthcare Analytics Projects: Build smart AI applications using neural network methodologies across the healthcare vertical market
暫譯: 醫療分析專案的機器學習:利用神經網絡方法在醫療垂直市場構建智能AI應用程式

Eduonix Learning Solutions

  • 出版商: Packt Publishing
  • 出版日期: 2018-10-29
  • 售價: $1,180
  • 貴賓價: 9.5$1,121
  • 語言: 英文
  • 頁數: 134
  • 裝訂: Paperback
  • ISBN: 1789536596
  • ISBN-13: 9781789536591
  • 相關分類: 人工智慧Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

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.

Table of Contents

  1. Breast Cancer Detection
  2. Diabetes Onset Detection
  3. DNA classification
  4. Diagnosing Coronary Artery Disease Using machine Learning
  5. Screening Children for Autistic Spectrum Disorder using machine learning

商品描述(中文翻譯)

使用 NumPy、pandas、matplotlib 和 scikit-learn 創建實際的機器學習解決方案

主要特點



  • 使用實際數據集開發一系列醫療保健分析項目

  • 使用 Python 生態系統中的多種庫實現關鍵的機器學習算法

  • 通過構建智能 AI 應用程序,使用神經網絡方法完成中等到複雜的任務

書籍描述


機器學習(ML)改變了組織和個人使用數據來提高系統效率的方式。ML 算法使策略制定者能夠處理各種結構化、非結構化和半結構化數據。《醫療保健分析項目的機器學習》充滿了創建強大醫療保健分析解決方案的新方法和方法論。


本書將教您如何實現關鍵的機器學習算法,並通過使用 Python 生態系統中的多種庫來引導您了解其用例。您將構建五個端到端的項目,以評估人工智能(AI)應用程序在執行簡單到複雜的醫療保健分析任務中的效率。每個項目都將讓您獲得新的見解,這將幫助您有效處理醫療保健數據。在閱讀本書的過程中,您將使用支持向量機(SVM)和 k-最近鄰(KNN)模型來檢測一組患者的癌症。在最後幾章中,您將在 Keras 中創建一個深度神經網絡,以預測大量患者的糖尿病發作。您還將學習如何使用神經網絡預測心臟疾病。


在本書結束時,您將學會如何解決長期存在的挑戰,提供專業的解決方案來應對這些挑戰,並在醫療保健領域執行一系列認知任務。

您將學到什麼



  • 探索超影像和自然語言處理(NLP)以分類 DNA 序列

  • 根據提供給 SVM 的細胞信息檢測癌症

  • 應用監督學習技術來診斷自閉症譜系障礙(ASD)

  • 實現深度學習網格和深度神經網絡以檢測糖尿病

  • 使用神經網絡分析血壓、心率和膽固醇水平測試的數據

  • 使用 ML 算法檢測自閉症障礙

本書適合誰


《醫療保健分析項目的機器學習》適合數據科學家、機器學習工程師和希望實現機器學習算法以構建智能 AI 應用程序的醫療保健專業人士。希望讀者具備基本的 Python 或任何編程語言的知識,以便從本書中獲益最大。

目錄



  1. 乳腺癌檢測

  2. 糖尿病發作檢測

  3. DNA 分類

  4. 使用機器學習診斷冠狀動脈疾病

  5. 使用機器學習篩查自閉症譜系障礙的兒童