Packt.Step.by.Step.Machine.Learning.with.Python

磁力链接/BT种子名称

Packt.Step.by.Step.Machine.Learning.with.Python

磁力链接/BT种子简介

种子哈希：cc6469702160d18e078735731ab059a59bdc3dd7
文件大小：953.6M
已经下载：6288次
下载速度：极快
收录时间：2020-01-27
最近下载：2025-10-14

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磁力链接

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文件列表

  • 6 - Click-Through Prediction with Logistic Regression#/Click-Through Prediction with Logistic Regression by Gradient Descent.mp4 79.0 MB
  • 3 - Spam Email Detection with Naïve Bayes#/The Naïve Bayes Implementation.mp4 60.1 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Touring Powerful NLP Libraries in Python.mp4 42.2 MB
  • 6 - Click-Through Prediction with Logistic Regression#/Logistic Regression Classifier.mp4 39.2 MB
  • 3 - Spam Email Detection with Naïve Bayes#/Classifier Performance Evaluation.mp4 38.8 MB
  • 5 - Click-Through Prediction with Tree-Based Algorithms/Decision Tree Classifier.mp4 38.5 MB
  • 4 - News Topic Classification with Support Vector Machine/News topic Classification with Support Vector Machine.mp4 38.1 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Stock Price Prediction with Regression Algorithms.mp4 35.9 MB
  • 8 - Best Practices/Best Practices in Data Preparation Stage.mp4 33.4 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Linear Regression.mp4 31.8 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Decision Tree Regression.mp4 28.8 MB
  • 5 - Click-Through Prediction with Tree-Based Algorithms/Click-Through Prediction with Decision Tree.mp4 26.2 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Predicting Stock Price with Regression Algorithms.mp4 25.6 MB
  • 5 - Click-Through Prediction with Tree-Based Algorithms/The Implementations of Decision Tree.mp4 23.9 MB
  • 1 - Getting Started with Python and Machine Learning/Installing Software and Setting Up.mp4 23.1 MB
  • 4 - News Topic Classification with Support Vector Machine/Fetal State Classification with SVM.mp4 22.9 MB
  • 6 - Click-Through Prediction with Logistic Regression#/One-Hot Encoding - Converting Categorical Features to Numerical.mp4 22.5 MB
  • 8 - Best Practices/Best Practices in the Training Sets Generation Stage.mp4 21.5 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Thinking about Features.mp4 21.3 MB
  • 4 - News Topic Classification with Support Vector Machine/The Implementations of SVM.mp4 20.6 MB
  • 5 - Click-Through Prediction with Tree-Based Algorithms/Random Forest - Feature Bagging of Decision Tree.mp4 19.2 MB
  • 3 - Spam Email Detection with Naïve Bayes#/Model Tuning and cross-validation.mp4 19.1 MB
  • 1 - Getting Started with Python and Machine Learning/The Course Overview.mp4 18.0 MB
  • 4 - News Topic Classification with Support Vector Machine/Recap and Inverse Document Frequency.mp4 17.4 MB
  • 6 - Click-Through Prediction with Logistic Regression#/Feature Selection via Random Forest.mp4 16.8 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Understanding NLP.mp4 16.7 MB
  • 4 - News Topic Classification with Support Vector Machine/Choosing Between the Linear and the RBF Kernel.mp4 14.9 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Getting the Newsgroups Data.mp4 14.8 MB
  • 8 - Best Practices/Best Practices in the Deployment and Monitoring Stage.mp4 14.6 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Topic Modeling.mp4 13.7 MB
  • 1 - Getting Started with Python and Machine Learning/Introduction to Machine Learning.mp4 13.7 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Regression Performance Evaluation.mp4 13.3 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Data Acquisition and Feature Generation.mp4 12.9 MB
  • 4 - News Topic Classification with Support Vector Machine/The Kernels of SVM.mp4 12.4 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Visualization.mp4 12.1 MB
  • 5 - Click-Through Prediction with Tree-Based Algorithms/Brief Overview of Advertising Click-Through Prediction.mp4 11.5 MB
  • 8 - Best Practices/Best Practices in the Model Training, Evaluation, and Selection Stage.mp4 11.4 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Clustering.mp4 10.9 MB
  • 4 - News Topic Classification with Support Vector Machine/The Mechanics of SVM.mp4 9.6 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Data Preprocessing.mp4 9.6 MB
  • 3 - Spam Email Detection with Naïve Bayes#/Getting Started with Classification.mp4 9.2 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Support Vector Regression.mp4 8.5 MB
  • 3 - Spam Email Detection with Naïve Bayes#/The Mechanics of Naïve Bayes.mp4 7.7 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Brief Overview of the Stock Market And Stock Price.mp4 7.4 MB
  • 3 - Spam Email Detection with Naïve Bayes#/Exploring Naïve Bayes.mp4 5.4 MB
  • V09050_Code/V09050_Code/Section 04/CTG.xls 1.7 MB
  • 2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Machine Learning with Python.mp4 1.7 MB
  • 7 - Stock Price Prediction with Regression Algorithms/Machine Learning with Python.mp4 1.7 MB
  • V09050_Code/V09050_Code/Section 07/198810101_20151231.csv 556.8 kB
  • V09050_Code/V09050_Code/Section 03/email_spam.py 10.5 kB
  • V09050_Code/V09050_Code/Section 05/1decision_tree_submit.py 10.0 kB
  • V09050_Code/V09050_Code/Section 06/3logistic_regression_from_scratch.py 7.8 kB
  • V09050_Code/V09050_Code/Section 07/1stock_price_prediction.py 7.7 kB
  • V09050_Code/V09050_Code/Section 07/3decision_tree_regression.py 7.2 kB
  • V09050_Code/V09050_Code/Section 03/.DS_Store 6.1 kB
  • V09050_Code/V09050_Code/Section 04/.DS_Store 6.1 kB
  • V09050_Code/V09050_Code/Section 05/.DS_Store 6.1 kB
  • V09050_Code/V09050_Code/Section 06/.DS_Store 6.1 kB
  • V09050_Code/V09050_Code/Section 07/.DS_Store 6.1 kB
  • V09050_Code/V09050_Code/Section 08/.DS_Store 6.1 kB
  • V09050_Code/V09050_Code/Section 06/5scikit_logistic_regression.py 5.4 kB
  • V09050_Code/V09050_Code/Section 04/2topic_categorization.py 5.2 kB
  • V09050_Code/V09050_Code/Section 07/2linear_regression.py 4.8 kB
  • V09050_Code/V09050_Code/Section 02/.ropeproject/config.py 3.5 kB
  • V09050_Code/V09050_Code/Section 08/1imputation.py 3.3 kB
  • V09050_Code/V09050_Code/Section 04/1email_spam_tfidf_submit.py 2.7 kB
  • V09050_Code/V09050_Code/Section 06/1one_hot_encode.py 2.5 kB
  • V09050_Code/V09050_Code/Section 02/.ropeproject/objectdb 2.3 kB
  • V09050_Code/V09050_Code/Section 05/2avazu_ctr.py 2.1 kB
  • V09050_Code/V09050_Code/Section 06/4random_forest_feature_selection.py 1.7 kB
  • V09050_Code/V09050_Code/Section 04/4ctg.py 1.2 kB
  • V09050_Code/V09050_Code/Section 04/3plot_rbf_kernels.py 1.2 kB
  • V09050_Code/V09050_Code/Section 08/2feature_selection.py 1.1 kB
  • V09050_Code/V09050_Code/Section 08/5save_reuse_monitor_model.py 1.0 kB
  • V09050_Code/V09050_Code/Section 02/.ropeproject/globalnames 1.0 kB
  • V09050_Code/V09050_Code/Section 02/4topic_model.py 998 Bytes
  • V09050_Code/V09050_Code/Section 02/3post_clustering.py 919 Bytes
  • V09050_Code/V09050_Code/Section 06/2logistic_function.py 833 Bytes
  • V09050_Code/V09050_Code/Section 02/2clean_words.py 723 Bytes
  • V09050_Code/V09050_Code/Section 07/20051201_20151210.csv 644 Bytes
  • V09050_Code/V09050_Code/Section 08/3dimensionality_reduction.py 635 Bytes
  • V09050_Code/V09050_Code/Section 02/1histogram.py 529 Bytes
  • V09050_Code/V09050_Code/Section 07/4support_vector_regression.py 439 Bytes
  • V09050_Code/V09050_Code/Section 08/4generic_feature_engineering.py 344 Bytes
  • V09050_Code/V09050_Code/Section 02/0_getting.py 303 Bytes
  • V09050_Code/V09050_Code/Section 02/.ropeproject/history 14 Bytes

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