文件名称：Mastering Machine Learning with scikit-learn -2017.7.24
文件大小：8.23MB
文件格式：PDF
更新时间：2020-09-06 04:06:49
Machine Learning scikit-learn 机器学习
Book Description Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. What you will learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks About the Author Gavin Hackeling is a data scientist and author. He was worked on a variety of machine learning problems, including automatic speech recognition, document classification, object recognition, and semantic segmentation. An alumnus of the University of North Carolina and New York University, he lives in *lyn with his wife and cat. Contents Chapter 1. The Fundamentals of Machine Learning Chapter 2. Simple linear regression Chapter 3. Classification and Regression with K Nearest Neighbors Chapter 4. Feature Extraction and Preprocessing Chapter 5. From Simple Regression to Multiple Regression Chapter 6. From Linear Regression to Logistic Regression Chapter 7. Naive Bayes Chapter 8. Nonlinear Classification and Regression with Decision Trees Chapter 9. From Decision Trees to Random Forests, and other Ensemble Methods Chapter 10. The Perceptron Chapter 11. From the Perceptron to Support Vector Machines Chapter 12. From the Perceptron to Artificial Neural Networks Chapter 13. Clustering with K-Means Chapter 14. Dimensionality Reduction with Principal Component Analysis