文件名称：机器学习算法Machine Learning Algorithms,
文件大小：62KB
文件格式：ZIP
更新时间：2022-04-19 15:54:23
机器学习
In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, XGBooster, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.
【文件预览】：
Machine-Learning-Algorithms-master
----Chapter07()
--------2kernel_svm_1.py(873B)
--------1linear_svm.py(1KB)
--------4svr.py(1KB)
--------3controlled_svm.py(1KB)
--------2kernel_svm_2.py(995B)
--------2kernel_svm.py(1KB)
----Chapter03()
--------1categorical.py(2KB)
--------2data_scaling.py(2KB)
--------2data_normalization.py(647B)
--------5kernel_pca.py(1KB)
--------1missing_features.py(710B)
--------4pca.py(2KB)
--------3feature_selection.py(1KB)
--------5nmf.py(783B)
--------5dictionary_learning.py(898B)
--------3feature_filtering.py(1KB)
----Chapter08()
--------2decision_tree_2.py(971B)
--------4random_forest_2.py(866B)
--------6adaboost_2.py(593B)
--------3random_forest.py(868B)
--------1decision_tree.py(1KB)
--------7gradient_tree_boosting.py(1KB)
--------8voting_classifier.py(3KB)
--------5adaboost.py(860B)
----Chapter06()
--------3gaussian.py(2KB)
--------2multinomial.py(1011B)
--------1bernoulli.py(1KB)
----Chapter14()
--------1gradients.py(1KB)
--------2logistic_regression.py(3KB)
--------4convolution.py(1KB)
--------3mlp.py(3KB)
--------5keras_cifar10.py(2KB)
----Chapter05()
--------1logistic_regression.py(2KB)
--------4classification_metrics.py(2KB)
--------3grid_search_2.py(1KB)
--------3grid_search.py(1011B)
--------5roc_curve.py(1KB)
--------2perceptron.py(1KB)
----Chapter15()
--------2pipeline_2.py(3KB)
--------3feature_union.py(1KB)
--------1pipeline.py(1KB)
----Chapter09()
--------3spectral_clustering_2.py(1KB)
--------2dbscan.py(1KB)
--------3spectral_clustering.py(1KB)
--------1k_means.py(1KB)
--------1k_means_2.py(1KB)
----LICENSE(1KB)
----Chapter04()
--------5polynomial_regression.py(1KB)
--------6isotonic_regression.py(1KB)
--------4ransac_regression.py(1KB)
--------3ridge_lasso_elasticnet.py(2KB)
--------2multiple_linear_regression.py(1KB)
--------1twoD_linear_regression.py(1KB)
----Chapter11()
--------5als_spark.py(1KB)
--------4model_based_cf.py(903B)
--------2content-based.py(1KB)
--------1user_based.py(1KB)
--------3memory_based_cf.py(1KB)
----Chapter12()
--------7reuters_text_classifier.py(2KB)
--------6vectorizing.py(2KB)
--------1corpora.py(487B)
--------5stemming.py(923B)
--------3stopwords_removal.py(600B)
--------4language_detection.py(328B)
--------2tokenizing.py(1KB)
----Chapter10()
--------2agglomerative_clustering.py(2KB)
--------1dendrogram.py(979B)
--------3connectivity_constraints.py(2KB)
----README.md(3KB)
----Chapter13()
--------7vader_sentiment_analysis.py(293B)
--------5lda.py(2KB)
--------4plsa.py(3KB)
--------3lsa_2.py(1KB)
--------6sentiment_analysis.py(2KB)
--------2lsa_1.py(1KB)
--------1lsa.py(2KB)