面向工程师的实用机器学习和AI(影印版)

许多AI入门指南可以说都是变相的微积分书籍 ，但这本书基本上避开了数学。作者Jeff Prosise 帮助工程师和软件开发人员建立了对AI的直观理解 ，以解决商业问题。需要创建一个系统来检测雨林 中非法砍伐的声音、分析文本的情感或预测旋转机 械的早期故障？这本实践用书将教你把AI和机器学 习应用于职场工作所需的技能。 书中的示例和插图来自于Prosise在全球多家 公司和研究机构教授的AI和机器学习课程。不说废 话，也没有可怕的公式 —— 纯粹就是写给工程师 和软件开发人员的快速入门，并附有实际操作的例 子。

Foreword Preface Part I. Machine Learning with Scikit-Learn 1. Machine Learning What Is Machine Learning? Machine Learning Versus Artificial Intelligence Supervised Versus Unsupervised Learning Unsupervised Learning with k-Means Clustering Applying k-Means Clustering to Customer Data Segmenting Customers Using More Than Two Dimensions Supervised Learning k-Nearest Neighbors Using k-Nearest Neighbors to Classify Flowers Summary 2. Regression Models Linear Regression Decision Trees Random Forests Gradient-Boosting Machines Support Vector Machines Accuracy Measures for Regression Models Using Regression to Predict Taxi Fares Summary 3. Classification Models Logistic Regression Accuracy Measures for Classification Models Categorical Data Binary Classification Classifying Passengers Who Sailed on the Titanic Detecting Credit Card Fraud Multiclass Classification Building a Digit Recognition Model Summary 4. Text Classification Preparing Text for Classification Sentiment Analysis Naive Bayes Spam Filtering Recommender Systems Cosine Similarity Building a Movie Recommendation System Summary 5. Support Vector Machines How Support Vector Machines Work Kernels Kernel Tricks Hyperparameter Tuning Data Normalization Pipelining Using SVMs for Facial Recognition