文件名称：Machine Learning in Action (Python)
文件大小：6.58MB
文件格式：PDF
更新时间：2020-09-26 09:27:37
Python
Part 1: Classification 1 Machine learning basics 2 Classifying with k-nearest neighbors 3 Splitting datasets one feature at a time: decision trees 4 Classifying with probability distributions: Na�ve Bayes 5 Logistic regression 6 Support vector machines 7 Improving classification with a meta-algorithm: Adaboost Part 2: Forecasting numeric values with regression 8 Predicting numeric values: regression 9 Tree-based regression Part 3: Unsupervised learning 10 Grouping unlabeled items using k-means clustering 11 Association analysis with the Apriori algorithm 12 Efficiently finding frequent itemsets with FP-Growth Part 4 Additional tools 13 Using principal components analysis to simplify our data 14 Simplifying data with the singular value decomposition 15 Big data and MapReduce