[FreeCourseSite.com] Udemy - Business Data Analytics & Intelligence with Python 2023

磁力链接/BT种子名称

[FreeCourseSite.com] Udemy - Business Data Analytics & Intelligence with Python 2023

磁力链接/BT种子简介

种子哈希：77dfc00bbffc33eada14739f7460a05051e85adc
文件大小： 6.18G
已经下载：3291次
下载速度：极快
收录时间：2024-01-13
最近下载：2025-07-20

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:77DFC00BBFFC33EADA14739F7460A05051E85ADC
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 世界之窗 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 极乐禁地 91短视频 TikTok成人版 PornHub 草榴社区 哆哔涩漫 呦乐园 萝莉岛

最近搜索

文件列表

  • 9. Google Causal Impact (Econometrics and Causal Inference)/22. CHALLENGE Solutions.mp4 172.4 MB
  • 13. Gaussian Mixture/14. CHALLENGE Solutions.mp4 168.4 MB
  • 16. Facebook Prophet/33. CHALLENGE Solutions (Part 3).mp4 134.0 MB
  • 16. Facebook Prophet/32. CHALLENGE Solutions (Part 2).mp4 117.0 MB
  • 6. Multilinear Regression/22. CHALLENGE Solutions.mp4 116.0 MB
  • 10. Matching/24. CHALLENGE Solutions.mp4 112.7 MB
  • 7. Logistic Regression/20. CHALLENGE Solutions.mp4 95.0 MB
  • 13. Gaussian Mixture/12. Python - Interpretation.mp4 82.5 MB
  • 1. Introduction/3. Join Our Online Classroom!.mp4 79.0 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/16. Python - Correlation Matrix and Heatmap.mp4 77.9 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/18. CHALLENGE Solutions.mp4 76.2 MB
  • 16. Facebook Prophet/31. CHALLENGE Solutions (Part 1).mp4 72.6 MB
  • 10. Matching/13. Python - Transforming Race Variable.mp4 69.4 MB
  • 4. Intermediary Statistics/20. Python - T-test.mp4 68.7 MB
  • 16. Facebook Prophet/17. Python - Facebook Prophet.mp4 63.9 MB
  • 16. Facebook Prophet/28. Python - Parameter Tuning.mp4 63.8 MB
  • 16. Facebook Prophet/20. Python - Event Assessment.mp4 63.2 MB
  • 3. Basic Statistics/5. Python - Mean.mp4 62.6 MB
  • 15. Random Forest/21. CHALLENGE Solutions (Part 2).mp4 62.0 MB
  • 1. Introduction/5. Setting up the Course Material.mp4 60.8 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/15. Python - Stationarity.mp4 60.7 MB
  • 16. Facebook Prophet/19. Python - Forecasting.mp4 60.5 MB
  • 7. Logistic Regression/13. Python - Function to Read Coefficients.mp4 59.4 MB
  • 10. Matching/21. Python - Matching Robustness Repeated Samples.mp4 58.9 MB
  • 16. Facebook Prophet/24. Python - Cross-Validation.mp4 58.7 MB
  • 15. Random Forest/20. CHALLENGE Solutions (Part 1).mp4 58.6 MB
  • 3. Basic Statistics/13. Python - Correlation.mp4 56.7 MB
  • 1. Introduction/1. Python for Business Analytics & Intelligence.mp4 56.2 MB
  • 3. Basic Statistics/4. Python - Directory, Libraries and Data.mp4 53.8 MB
  • 4. Intermediary Statistics/9. Python - Shapiro-Wilks Test.mp4 53.6 MB
  • 4. Intermediary Statistics/23. Python - Chi-square test.mp4 52.9 MB
  • 10. Matching/19. Python - Matching Model.mp4 52.7 MB
  • 7. Logistic Regression/6. Python - Histogram and Outlier Removal.mp4 50.6 MB
  • 15. Random Forest/18. Python - Parameter Tuning.mp4 50.5 MB
  • 16. Facebook Prophet/22. Python - Visualization.mp4 50.2 MB
  • 10. Matching/9. Python - T-Test Loop.mp4 49.6 MB
  • 16. Facebook Prophet/25. Python - Cross-Validation Results and Visualization.mp4 48.5 MB
  • 4. Intermediary Statistics/16. Python - Confidence Interval.mp4 48.0 MB
  • 1. Introduction/7. ZTM Resources.mp4 46.7 MB
  • 4. Intermediary Statistics/15. Confidence interval.mp4 45.0 MB
  • 6. Multilinear Regression/17. Python - Multilinear Regression.mp4 44.8 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/20. Python - Causal Impact Results.mp4 43.9 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/12. Python - Quartiles.mp4 43.9 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/9. Python - Load Bitcoin Price Data.mp4 43.1 MB
  • 4. Intermediary Statistics/5. Python - Normal Distribution Visualization.mp4 42.9 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/21. CHALLENGE Introduction.mp4 42.5 MB
  • 6. Multilinear Regression/7. Python - Plotting Continuous Variables.mp4 41.3 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/23. EXERCISE Imposter Syndrome.mp4 41.1 MB
  • 13. Gaussian Mixture/9. Python - Optimal Number of Clusters.mp4 40.6 MB
  • 7. Logistic Regression/17. Python - Manual Accuracy Assessment.mp4 40.3 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/12. Python - Data Preparation.mp4 40.2 MB
  • 10. Matching/14. Python - Transforming Education Variable.mp4 40.0 MB
  • 6. Multilinear Regression/20. Python - Accuracy Assessment.mp4 39.4 MB
  • 16. Facebook Prophet/11. Python - Black Friday Holiday.mp4 39.3 MB
  • 16. Facebook Prophet/29. Python - Parameter Tuning Results.mp4 38.6 MB
  • 5. Linear Regression/12. EXERCISE Python - Linear Regression.mp4 37.8 MB
  • 10. Matching/8. Python - T-Test.mp4 37.1 MB
  • 10. Matching/23. CHALLENGE Introduction.mp4 36.0 MB
  • 16. Facebook Prophet/27. Python - Parameter Grid.mp4 35.8 MB
  • 5. Linear Regression/7. Linear Regression Output.mp4 35.1 MB
  • 10. Matching/18. Python - Plotting Common Support Region.mp4 34.7 MB
  • 13. Gaussian Mixture/13. CHALLENGE Introduction.mp4 34.5 MB
  • 6. Multilinear Regression/4. Python - Preparing Script and Loading Data.mp4 34.4 MB
  • 16. Facebook Prophet/6. Python - Loading and Inspecting the Data.mp4 34.2 MB
  • 4. Intermediary Statistics/21. EXERCISE Python - T-test.mp4 33.5 MB
  • 15. Random Forest/11. Python - Training and Test Set.mp4 33.2 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/18. Python - Google Causal Impact.mp4 32.6 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/9. Python - Customer Level Aggregation.mp4 31.9 MB
  • 7. Logistic Regression/16. Python - Confusion Matrix.mp4 31.1 MB
  • 10. Matching/16. Common Support Region.mp4 30.9 MB
  • 3. Basic Statistics/12. Correlation.mp4 30.8 MB
  • 16. Facebook Prophet/18. Python - Regressor Coefficients.mp4 30.7 MB
  • 16. Facebook Prophet/21. Python - Accuracy Assessment.mp4 30.7 MB
  • 7. Logistic Regression/19. CHALLENGE Introduction.mp4 30.7 MB
  • 15. Random Forest/19. CHALLENGE Introduction.mp4 30.6 MB
  • 10. Matching/11. Python - Chi-square Loop.mp4 30.4 MB
  • 4. Intermediary Statistics/12. Python - Standard Error.mp4 30.2 MB
  • 4. Intermediary Statistics/4. Python - Preparing Script and Loading Data.mp4 30.0 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/8. Python - Dates.mp4 29.8 MB
  • 4. Intermediary Statistics/6. EXERCISE Python - Normal Distribution.mp4 29.7 MB
  • 6. Multilinear Regression/10. Python - For Loop.mp4 29.6 MB
  • 7. Logistic Regression/5. Python - Summary Statistics.mp4 29.3 MB
  • 7. Logistic Regression/4. Python - Preparing Script and Loading Data.mp4 29.2 MB
  • 6. Multilinear Regression/21. CHALLENGE Introduction.mp4 29.0 MB
  • 5. Linear Regression/4. Python - Preparing Script and Loading Data.mp4 28.8 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/16. Python - Results Summary.mp4 28.6 MB
  • 4. Intermediary Statistics/7. P-value.mp4 28.6 MB
  • 10. Matching/5. Python - Loading Data.mp4 28.6 MB
  • 6. Multilinear Regression/9. Python - Categorical Variables.mp4 27.8 MB
  • 10. Matching/17. Python - Logistic Regression for Common Support Region.mp4 27.6 MB
  • 5. Linear Regression/9. Python - Plotting Regression.mp4 27.4 MB
  • 15. Random Forest/15. Python - Feature Importance.mp4 27.3 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/19. Interpreting the Causal Impact Plots.mp4 26.8 MB
  • 3. Basic Statistics/8. Python - Median.mp4 26.6 MB
  • 15. Random Forest/14. Python - Classification Report and F1 score.mp4 25.9 MB
  • 7. Logistic Regression/15. Confusion Matrix.mp4 25.7 MB
  • 3. Basic Statistics/14. EXERCISE Python - Correlation.mp4 25.3 MB
  • 15. Random Forest/8. Python - Summary Statistics.mp4 24.7 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/6. Python - Loading Data.mp4 24.6 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/11. Python - Loading More Stock Data.mp4 24.4 MB
  • 10. Matching/10. Python - Chi-square Test.mp4 24.3 MB
  • 10. Matching/4. Python - Libraries and Directory.mp4 23.9 MB
  • 4. Intermediary Statistics/24. EXERCISE Python - Chi-square.mp4 23.8 MB
  • 16. Facebook Prophet/10. Python - Easter Holiday.mp4 23.5 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/14. Python - RFM Function.mp4 23.5 MB
  • 5. Linear Regression/8. Python - Linear Regression model and summary.mp4 23.0 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/8. Python - Date Variable.mp4 22.9 MB
  • 7. Logistic Regression/10. Python - Training and Test Set.mp4 22.7 MB
  • 7. Logistic Regression/14. Python - Predictions.mp4 22.3 MB
  • 6. Multilinear Regression/8. Python - Correlation Matrix.mp4 22.1 MB
  • 6. Multilinear Regression/12. Python - Isolate X and Y.mp4 21.7 MB
  • 3. Basic Statistics/9. EXERCISE Python - Median.mp4 21.3 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/7. Python - Libraries and Dates.mp4 21.1 MB
  • 15. Random Forest/6. Python - Loading Data.mp4 20.7 MB
  • 10. Matching/22. Python - Removing 1 Confounder.mp4 20.6 MB
  • 7. Logistic Regression/18. Python - Classification Report.mp4 20.5 MB
  • 1. Introduction/6. The Modern Day Business Analyst.mp4 20.5 MB
  • 3. Basic Statistics/10. Python - Mode.mp4 19.7 MB
  • 6. Multilinear Regression/5. Python - Summary Statistics.mp4 19.7 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/11. Python - Tidying up Dataframe.mp4 19.6 MB
  • 7. Logistic Regression/12. Python - Logistic Regression.mp4 19.5 MB
  • 10. Matching/2. Matching.mp4 19.5 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/1. Why Econometrics and Causal Inference.mp4 19.2 MB
  • 16. Facebook Prophet/7. Python - Transforming Date Variable.mp4 18.9 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/17. CHALLENGE Introduction.mp4 18.8 MB
  • 5. Linear Regression/11. Python - Dummy Variable.mp4 18.7 MB
  • 15. Random Forest/12. Python - Random Forest Model.mp4 18.7 MB
  • 15. Random Forest/17. Python - Parameter Grid.mp4 18.6 MB
  • 3. Basic Statistics/16. Python - Standard Deviation.mp4 18.5 MB
  • 6. Multilinear Regression/11. Python - Creating Dummy Variables.mp4 18.4 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/14. Correlation Recap and Stationarity.mp4 18.2 MB
  • 7. Logistic Regression/8. Python - Transforming Dependent Variable.mp4 18.2 MB
  • 15. Random Forest/3. How Decision Trees Work.mp4 18.1 MB
  • 10. Matching/15. Python - Cleaning and Preparing Dataframe.mp4 18.0 MB
  • 17. Where To Go From Here/1. Thank You!.mp4 17.7 MB
  • 10. Matching/7. Python - Comparing Means per Group.mp4 17.5 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/10. Assumptions.mp4 17.5 MB
  • 13. Gaussian Mixture/6. Python - Load Data.mp4 17.4 MB
  • 16. Facebook Prophet/30. CHALLENGE Introduction - Demand in NYC.mp4 17.4 MB
  • 3. Basic Statistics/6. EXERCISE Python - Mean.mp4 17.3 MB
  • 5. Linear Regression/3. Linear Regression.mp4 17.0 MB
  • 4. Intermediary Statistics/17. EXERCISE Python - Confidence Interval.mp4 16.9 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/7. Python - Creating Sales Variable.mp4 16.8 MB
  • 4. Intermediary Statistics/25. Powerposing and p-hacking.mp4 16.5 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/15. Python - Applying RFM Function.mp4 16.5 MB
  • 16. Facebook Prophet/34. Forecasting at Uber.mp4 16.4 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/13. Python - Training Dataframe.mp4 16.3 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/17. Python - Google Causal Impact Setup.mp4 15.9 MB
  • 16. Facebook Prophet/5. Python - Directory and Libraries.mp4 15.8 MB
  • 7. Logistic Regression/7. Python - Correlation Matrix.mp4 15.8 MB
  • 4. Intermediary Statistics/10. EXERCISE Python - Shapiro-Wilks.mp4 15.8 MB
  • 13. Gaussian Mixture/5. Python - Directory and Data.mp4 15.4 MB
  • 6. Multilinear Regression/18. Accuracy KPIs (Key Performance Indicators).mp4 15.3 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/3. RFM Model.mp4 15.1 MB
  • 13. Gaussian Mixture/11. Python - Cluster Prediction and Assignment.mp4 15.0 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/4. CASE STUDY Bitcoin and PayPal (Briefing).mp4 15.0 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/5. Python - Directory and Libraries.mp4 14.9 MB
  • 15. Random Forest/5. Python - Directory and Libraries.mp4 14.6 MB
  • 6. Multilinear Regression/16. Python - Train and Test Split.mp4 14.6 MB
  • 13. Gaussian Mixture/3. Gaussian Mixture Model.mp4 14.5 MB
  • 15. Random Forest/7. Python - Transform Object into Numerical Variables.mp4 14.2 MB
  • 15. Random Forest/10. Python - Isolate X and Y.mp4 13.7 MB
  • 13. Gaussian Mixture/15. My Experience with Segmentation.mp4 13.6 MB
  • 4. Intermediary Statistics/2. Normal Distribution.mp4 13.4 MB
  • 3. Basic Statistics/11. EXERCISE Python - Mode.mp4 13.2 MB
  • 4. Intermediary Statistics/13. EXERCISE Python - Standard Error.mp4 13.2 MB
  • 3. Basic Statistics/18. CASE STUDY Moneyball.mp4 13.1 MB
  • 10. Matching/6. Unconfoundedness.mp4 12.8 MB
  • 5. Linear Regression/10. Dummy Variable Trap.mp4 12.7 MB
  • 16. Facebook Prophet/3. Facebook Prophet.mp4 12.7 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/13. Python - RFM Score.mp4 12.6 MB
  • 13. Gaussian Mixture/7. Python - Transform Character variables.mp4 12.6 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/5. Difference-in-Differences Framework.mp4 12.2 MB
  • 7. Logistic Regression/9. Python - Prepare X and Y.mp4 12.1 MB
  • 5. Linear Regression/6. Python - Adding Constant.mp4 12.0 MB
  • 5. Linear Regression/5. Python - Isolate X and Y.mp4 11.6 MB
  • 7. Logistic Regression/11. How to Read Logistic Regression Coefficients.mp4 11.4 MB
  • 16. Facebook Prophet/15. Facebook Prophet Model.mp4 11.4 MB
  • 16. Facebook Prophet/14. Python - Training and Test Set.mp4 11.2 MB
  • 13. Gaussian Mixture/8. AIC and BIC.mp4 10.6 MB
  • 16. Facebook Prophet/16. Additive vs. Multiplicative Seasonality.mp4 10.6 MB
  • 7. Logistic Regression/3. Logistic Regression.mp4 10.6 MB
  • 4. Intermediary Statistics/3. CASE STUDY Wine Quality (Briefing).mp4 10.3 MB
  • 6. Multilinear Regression/19. Python - Model Predictions.mp4 10.2 MB
  • 10. Matching/12. The Curse of Dimensionality.mp4 9.8 MB
  • 3. Basic Statistics/2. Arithmetic Mean.mp4 9.5 MB
  • 15. Random Forest/2. Ensemble Learning and Random Forest.mp4 9.5 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/2. Value Based Segmentation.mp4 9.4 MB
  • 16. Facebook Prophet/8. Python - Renaming Variables.mp4 9.4 MB
  • 10. Matching/1. Matching - Game Plan.mp4 9.3 MB
  • 6. Multilinear Regression/6. Outliers.mp4 9.2 MB
  • 4. Intermediary Statistics/22. Chi-square test.mp4 9.2 MB
  • 13. Gaussian Mixture/10. Python - Gaussian Mixture Model.mp4 9.0 MB
  • 16. Facebook Prophet/9. Dynamic Holidays.mp4 8.9 MB
  • 4. Intermediary Statistics/14. Z-Score.mp4 8.9 MB
  • 10. Matching/3. CASE STUDY Catholic Schools & Standardized Tests (Briefing).mp4 8.9 MB
  • 3. Basic Statistics/7. Median and Mode.mp4 8.8 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/10. Python - Monetary Variable.mp4 8.7 MB
  • 16. Facebook Prophet/12. Python - Finishing Holiday Preparation.mp4 8.7 MB
  • 16. Facebook Prophet/26. Parameters to Tune.mp4 8.7 MB
  • 16. Facebook Prophet/2. Structural Time Series.mp4 8.6 MB
  • 4. Intermediary Statistics/11. Standard Error of the Mean.mp4 8.5 MB
  • 15. Random Forest/13. Python - Predictions.mp4 8.2 MB
  • 10. Matching/25. My Experience with Matching.mp4 8.1 MB
  • 15. Random Forest/16. Parameter Tuning.mp4 8.0 MB
  • 1. Introduction/2. Introduction.mp4 7.6 MB
  • 3. Basic Statistics/15. Standard Deviation.mp4 7.6 MB
  • 6. Multilinear Regression/13. Python - Adding Constant.mp4 7.4 MB
  • 15. Random Forest/9. Random Forest Quirks.mp4 7.0 MB
  • 10. Matching/20. Matching Robustness Check.mp4 7.0 MB
  • 4. Intermediary Statistics/18. T-test.mp4 6.9 MB
  • 3. Basic Statistics/17. EXERCISE Python - Standard Deviation.mp4 6.8 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/3. Time Series Data.mp4 6.7 MB
  • 13. Gaussian Mixture/2. Clustering.mp4 6.5 MB
  • 4. Intermediary Statistics/8. Shapiro-Wilks Test.mp4 6.4 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/6. Causal Impact Step-by-Step Guide.mp4 6.2 MB
  • 16. Facebook Prophet/13. Training and Test Set in Time Series.mp4 6.1 MB
  • 6. Multilinear Regression/2. The Concept of Multilinear Regression.mp4 5.6 MB
  • 6. Multilinear Regression/14. Under and Over Fitting.mp4 5.3 MB
  • 16. Facebook Prophet/1. Facebook Prophet - Game Plan.mp4 5.3 MB
  • 6. Multilinear Regression/1. Multilinear Regression - Game Plan.mp4 4.4 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/2. Google Causal Impact - Game Plan.mp4 4.2 MB
  • 16. Facebook Prophet/23. Cross-Validation.mp4 3.8 MB
  • 5. Linear Regression/1. Linear Regression - Game Plan.mp4 3.8 MB
  • 16. Facebook Prophet/4. CASE STUDY Wikipedia (Briefing).mp4 3.7 MB
  • 5. Linear Regression/2. CASE STUDY Diamonds (Briefing).mp4 3.5 MB
  • 15. Random Forest/1. Random Forest - Game Plan.mp4 3.5 MB
  • 7. Logistic Regression/1. Logistic Regression - Game Plan.mp4 3.3 MB
  • 6. Multilinear Regression/3. CASE STUDY Professors' Salary (Briefing).mp4 3.2 MB
  • 13. Gaussian Mixture/4. CASE STUDY Credit Cards #1 (Briefing).mp4 3.1 MB
  • 7. Logistic Regression/2. CASE STUDY Spam Emails (Briefing).mp4 3.1 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/4. CASE STUDY Online Shopping (Briefing).mp4 3.1 MB
  • 3. Basic Statistics/1. Basic Statistics - Game Plan.mp4 3.1 MB
  • 6. Multilinear Regression/15. Training and Test Set.mp4 2.9 MB
  • 13. Gaussian Mixture/1. Gaussian Mixture - Game Plan.mp4 2.8 MB
  • 4. Intermediary Statistics/19. CASE STUDY Remote Work Predictions (Briefing).mp4 2.6 MB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/1. RFM - Game Plan.mp4 2.4 MB
  • 15. Random Forest/4. CASE STUDY Credit Cards #2 (Briefing).mp4 2.3 MB
  • 3. Basic Statistics/3. CASE STUDY Moneyball (Briefing).mp4 2.3 MB
  • 4. Intermediary Statistics/1. Intermediary Statistics - Game Plan.mp4 1.9 MB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/22. CHALLENGE Solutions.srt 22.2 kB
  • 13. Gaussian Mixture/14. CHALLENGE Solutions.srt 20.2 kB
  • 6. Multilinear Regression/22. CHALLENGE Solutions.srt 20.0 kB
  • 16. Facebook Prophet/33. CHALLENGE Solutions (Part 3).srt 17.3 kB
  • 16. Facebook Prophet/32. CHALLENGE Solutions (Part 2).srt 16.7 kB
  • 10. Matching/24. CHALLENGE Solutions.srt 16.4 kB
  • 7. Logistic Regression/20. CHALLENGE Solutions.srt 15.6 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/18. CHALLENGE Solutions.srt 13.1 kB
  • 4. Intermediary Statistics/20. Python - T-test.srt 12.4 kB
  • 16. Facebook Prophet/31. CHALLENGE Solutions (Part 1).srt 12.1 kB
  • 3. Basic Statistics/13. Python - Correlation.srt 10.6 kB
  • 3. Basic Statistics/5. Python - Mean.srt 10.6 kB
  • 15. Random Forest/21. CHALLENGE Solutions (Part 2).srt 10.5 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/16. Python - Correlation Matrix and Heatmap.srt 10.5 kB
  • 7. Logistic Regression/13. Python - Function to Read Coefficients.srt 10.3 kB
  • 1. Introduction/5. Setting up the Course Material.srt 10.0 kB
  • 10. Matching/13. Python - Transforming Race Variable.srt 9.8 kB
  • 3. Basic Statistics/4. Python - Directory, Libraries and Data.srt 9.7 kB
  • 15. Random Forest/20. CHALLENGE Solutions (Part 1).srt 9.5 kB
  • 10. Matching/21. Python - Matching Robustness Repeated Samples.srt 9.4 kB
  • 4. Intermediary Statistics/5. Python - Normal Distribution Visualization.srt 9.3 kB
  • 13. Gaussian Mixture/12. Python - Interpretation.srt 9.2 kB
  • 4. Intermediary Statistics/9. Python - Shapiro-Wilks Test.srt 9.1 kB
  • 4. Intermediary Statistics/23. Python - Chi-square test.srt 8.9 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/15. Python - Stationarity.srt 8.9 kB
  • 16. Facebook Prophet/20. Python - Event Assessment.srt 8.4 kB
  • 16. Facebook Prophet/28. Python - Parameter Tuning.srt 8.4 kB
  • 16. Facebook Prophet/19. Python - Forecasting.srt 8.1 kB
  • 16. Facebook Prophet/22. Python - Visualization.srt 8.1 kB
  • 7. Logistic Regression/6. Python - Histogram and Outlier Removal.srt 8.1 kB
  • 10. Matching/19. Python - Matching Model.srt 7.9 kB
  • 4. Intermediary Statistics/16. Python - Confidence Interval.srt 7.9 kB
  • 15. Random Forest/18. Python - Parameter Tuning.srt 7.8 kB
  • 7. Logistic Regression/17. Python - Manual Accuracy Assessment.srt 7.6 kB
  • 7. Logistic Regression/15. Confusion Matrix.srt 7.5 kB
  • 16. Facebook Prophet/25. Python - Cross-Validation Results and Visualization.srt 7.4 kB
  • 16. Facebook Prophet/24. Python - Cross-Validation.srt 7.4 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/12. Python - Quartiles.srt 7.3 kB
  • 16. Facebook Prophet/17. Python - Facebook Prophet.srt 7.2 kB
  • 4. Intermediary Statistics/6. EXERCISE Python - Normal Distribution.srt 7.0 kB
  • 4. Intermediary Statistics/7. P-value.srt 6.8 kB
  • 13. Gaussian Mixture/9. Python - Optimal Number of Clusters.srt 6.7 kB
  • 6. Multilinear Regression/20. Python - Accuracy Assessment.srt 6.7 kB
  • 5. Linear Regression/12. EXERCISE Python - Linear Regression.srt 6.5 kB
  • 1. Introduction/7. ZTM Resources.srt 6.5 kB
  • 10. Matching/23. CHALLENGE Introduction.srt 6.5 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/21. CHALLENGE Introduction.srt 6.4 kB
  • 4. Intermediary Statistics/15. Confidence interval.srt 6.4 kB
  • 16. Facebook Prophet/11. Python - Black Friday Holiday.srt 6.4 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/20. Python - Causal Impact Results.srt 6.3 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/3. RFM Model.srt 6.2 kB
  • 16. Facebook Prophet/6. Python - Loading and Inspecting the Data.srt 6.2 kB
  • 16. Facebook Prophet/10. Python - Easter Holiday.srt 6.2 kB
  • 4. Intermediary Statistics/21. EXERCISE Python - T-test.srt 6.1 kB
  • 1. Introduction/3. Join Our Online Classroom!.srt 6.1 kB
  • 10. Matching/9. Python - T-Test Loop.srt 6.0 kB
  • 6. Multilinear Regression/4. Python - Preparing Script and Loading Data.srt 6.0 kB
  • 4. Intermediary Statistics/4. Python - Preparing Script and Loading Data.srt 6.0 kB
  • 5. Linear Regression/4. Python - Preparing Script and Loading Data.srt 5.7 kB
  • 16. Facebook Prophet/27. Python - Parameter Grid.srt 5.7 kB
  • 5. Linear Regression/3. Linear Regression.srt 5.7 kB
  • 1. Introduction/6. The Modern Day Business Analyst.srt 5.6 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/12. Python - Data Preparation.srt 5.6 kB
  • 10. Matching/18. Python - Plotting Common Support Region.srt 5.6 kB
  • 7. Logistic Regression/16. Python - Confusion Matrix.srt 5.6 kB
  • 10. Matching/14. Python - Transforming Education Variable.srt 5.6 kB
  • 6. Multilinear Regression/7. Python - Plotting Continuous Variables.srt 5.6 kB
  • 3. Basic Statistics/12. Correlation.srt 5.5 kB
  • 16. Facebook Prophet/21. Python - Accuracy Assessment.srt 5.5 kB
  • 16. Facebook Prophet/34. Forecasting at Uber.srt 5.5 kB
  • 6. Multilinear Regression/21. CHALLENGE Introduction.srt 5.5 kB
  • 6. Multilinear Regression/17. Python - Multilinear Regression.srt 5.4 kB
  • 3. Basic Statistics/8. Python - Median.srt 5.2 kB
  • 13. Gaussian Mixture/13. CHALLENGE Introduction.srt 5.2 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/16. Python - Results Summary.srt 5.1 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/9. Python - Load Bitcoin Price Data.srt 5.1 kB
  • 10. Matching/17. Python - Logistic Regression for Common Support Region.srt 5.1 kB
  • 6. Multilinear Regression/10. Python - For Loop.srt 5.0 kB
  • 15. Random Forest/15. Python - Feature Importance.srt 5.0 kB
  • 6. Multilinear Regression/9. Python - Categorical Variables.srt 5.0 kB
  • 7. Logistic Regression/19. CHALLENGE Introduction.srt 5.0 kB
  • 10. Matching/8. Python - T-Test.srt 5.0 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/8. Python - Dates.srt 4.9 kB
  • 10. Matching/16. Common Support Region.srt 4.9 kB
  • 7. Logistic Regression/4. Python - Preparing Script and Loading Data.srt 4.8 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/14. Python - RFM Function.srt 4.8 kB
  • 15. Random Forest/19. CHALLENGE Introduction.srt 4.8 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/14. Correlation Recap and Stationarity.srt 4.7 kB
  • 4. Intermediary Statistics/12. Python - Standard Error.srt 4.7 kB
  • 15. Random Forest/3. How Decision Trees Work.srt 4.6 kB
  • 5. Linear Regression/9. Python - Plotting Regression.srt 4.6 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/23. EXERCISE Imposter Syndrome.srt 4.6 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/1. Why Econometrics and Causal Inference.srt 4.6 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/19. Interpreting the Causal Impact Plots.srt 4.6 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/11. Python - Loading More Stock Data.srt 4.5 kB
  • 13. Gaussian Mixture/3. Gaussian Mixture Model.srt 4.4 kB
  • 16. Facebook Prophet/3. Facebook Prophet.srt 4.3 kB
  • 6. Multilinear Regression/12. Python - Isolate X and Y.srt 4.3 kB
  • 3. Basic Statistics/18. CASE STUDY Moneyball.srt 4.3 kB
  • 3. Basic Statistics/14. EXERCISE Python - Correlation.srt 4.3 kB
  • 16. Facebook Prophet/29. Python - Parameter Tuning Results.srt 4.2 kB
  • 5. Linear Regression/11. Python - Dummy Variable.srt 4.1 kB
  • 10. Matching/11. Python - Chi-square Loop.srt 4.1 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/9. Python - Customer Level Aggregation.srt 4.1 kB
  • 16. Facebook Prophet/7. Python - Transforming Date Variable.srt 4.1 kB
  • 4. Intermediary Statistics/25. Powerposing and p-hacking.srt 4.0 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/17. CHALLENGE Introduction.srt 4.0 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/10. Assumptions.srt 4.0 kB
  • 10. Matching/2. Matching.srt 4.0 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/8. Python - Date Variable.srt 3.9 kB
  • 5. Linear Regression/10. Dummy Variable Trap.srt 3.9 kB
  • 13. Gaussian Mixture/15. My Experience with Segmentation.srt 3.9 kB
  • 5. Linear Regression/7. Linear Regression Output.srt 3.9 kB
  • 10. Matching/10. Python - Chi-square Test.srt 3.8 kB
  • 1. Introduction/4. Exercise Meet Your Classmates + Instructor.html 3.8 kB
  • 3. Basic Statistics/9. EXERCISE Python - Median.srt 3.8 kB
  • 15. Random Forest/14. Python - Classification Report and F1 score.srt 3.8 kB
  • 4. Intermediary Statistics/24. EXERCISE Python - Chi-square.srt 3.8 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/7. Python - Libraries and Dates.srt 3.8 kB
  • 5. Linear Regression/8. Python - Linear Regression model and summary.srt 3.7 kB
  • 16. Facebook Prophet/18. Python - Regressor Coefficients.srt 3.6 kB
  • 7. Logistic Regression/14. Python - Predictions.srt 3.6 kB
  • 15. Random Forest/17. Python - Parameter Grid.srt 3.6 kB
  • 6. Multilinear Regression/11. Python - Creating Dummy Variables.srt 3.6 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/5. Difference-in-Differences Framework.srt 3.6 kB
  • 1. Introduction/1. Python for Business Analytics & Intelligence.srt 3.5 kB
  • 6. Multilinear Regression/18. Accuracy KPIs (Key Performance Indicators).srt 3.5 kB
  • 4. Intermediary Statistics/2. Normal Distribution.srt 3.5 kB
  • 15. Random Forest/11. Python - Training and Test Set.srt 3.5 kB
  • 6. Multilinear Regression/5. Python - Summary Statistics.srt 3.4 kB
  • 10. Matching/15. Python - Cleaning and Preparing Dataframe.srt 3.4 kB
  • 3. Basic Statistics/10. Python - Mode.srt 3.4 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/2. Value Based Segmentation.srt 3.4 kB
  • 6. Multilinear Regression/8. Python - Correlation Matrix.srt 3.4 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/18. Python - Google Causal Impact.srt 3.4 kB
  • 7. Logistic Regression/5. Python - Summary Statistics.srt 3.4 kB
  • 10. Matching/4. Python - Libraries and Directory.srt 3.4 kB
  • 6. Multilinear Regression/6. Outliers.srt 3.4 kB
  • 3. Basic Statistics/7. Median and Mode.srt 3.4 kB
  • 5. Linear Regression/6. Python - Adding Constant.srt 3.4 kB
  • 4. Intermediary Statistics/10. EXERCISE Python - Shapiro-Wilks.srt 3.3 kB
  • 15. Random Forest/12. Python - Random Forest Model.srt 3.3 kB
  • 10. Matching/25. My Experience with Matching.srt 3.2 kB
  • 4. Intermediary Statistics/11. Standard Error of the Mean.srt 3.2 kB
  • 4. Intermediary Statistics/3. CASE STUDY Wine Quality (Briefing).srt 3.2 kB
  • 13. Gaussian Mixture/11. Python - Cluster Prediction and Assignment.srt 3.1 kB
  • 10. Matching/1. Matching - Game Plan.srt 3.1 kB
  • 4. Intermediary Statistics/13. EXERCISE Python - Standard Error.srt 3.1 kB
  • 10. Matching/22. Python - Removing 1 Confounder.srt 3.1 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/6. Python - Loading Data.srt 3.0 kB
  • 15. Random Forest/16. Parameter Tuning.srt 3.0 kB
  • 4. Intermediary Statistics/14. Z-Score.srt 3.0 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/11. Python - Tidying up Dataframe.srt 3.0 kB
  • 7. Logistic Regression/8. Python - Transforming Dependent Variable.srt 3.0 kB
  • 10. Matching/7. Python - Comparing Means per Group.srt 3.0 kB
  • 16. Facebook Prophet/5. Python - Directory and Libraries.srt 3.0 kB
  • 7. Logistic Regression/10. Python - Training and Test Set.srt 3.0 kB
  • 16. Facebook Prophet/15. Facebook Prophet Model.srt 2.9 kB
  • 10. Matching/6. Unconfoundedness.srt 2.9 kB
  • 4. Intermediary Statistics/22. Chi-square test.srt 2.9 kB
  • 7. Logistic Regression/18. Python - Classification Report.srt 2.9 kB
  • 7. Logistic Regression/11. How to Read Logistic Regression Coefficients.srt 2.9 kB
  • 16. Facebook Prophet/2. Structural Time Series.srt 2.8 kB
  • 15. Random Forest/9. Random Forest Quirks.srt 2.8 kB
  • 16. Facebook Prophet/9. Dynamic Holidays.srt 2.8 kB
  • 16. Facebook Prophet/16. Additive vs. Multiplicative Seasonality.srt 2.8 kB
  • 10. Matching/5. Python - Loading Data.srt 2.8 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/4. CASE STUDY Bitcoin and PayPal (Briefing).srt 2.7 kB
  • 3. Basic Statistics/15. Standard Deviation.srt 2.7 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/15. Python - Applying RFM Function.srt 2.7 kB
  • 3. Basic Statistics/16. Python - Standard Deviation.srt 2.7 kB
  • 4. Intermediary Statistics/17. EXERCISE Python - Confidence Interval.srt 2.7 kB
  • 3. Basic Statistics/2. Arithmetic Mean.srt 2.7 kB
  • 6. Multilinear Regression/16. Python - Train and Test Split.srt 2.7 kB
  • 7. Logistic Regression/12. Python - Logistic Regression.srt 2.6 kB
  • 7. Logistic Regression/7. Python - Correlation Matrix.srt 2.6 kB
  • 4. Intermediary Statistics/18. T-test.srt 2.6 kB
  • 15. Random Forest/8. Python - Summary Statistics.srt 2.6 kB
  • 7. Logistic Regression/9. Python - Prepare X and Y.srt 2.6 kB
  • 15. Random Forest/2. Ensemble Learning and Random Forest.srt 2.6 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/17. Python - Google Causal Impact Setup.srt 2.5 kB
  • 13. Gaussian Mixture/2. Clustering.srt 2.5 kB
  • 15. Random Forest/5. Python - Directory and Libraries.srt 2.5 kB
  • 16. Facebook Prophet/30. CHALLENGE Introduction - Demand in NYC.srt 2.5 kB
  • 3. Basic Statistics/6. EXERCISE Python - Mean.srt 2.5 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/6. Causal Impact Step-by-Step Guide.srt 2.5 kB
  • 13. Gaussian Mixture/8. AIC and BIC.srt 2.5 kB
  • 16. Facebook Prophet/26. Parameters to Tune.srt 2.5 kB
  • 1. Introduction/2. Introduction.srt 2.4 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/5. Python - Directory and Libraries.srt 2.4 kB
  • 10. Matching/20. Matching Robustness Check.srt 2.3 kB
  • 16. Facebook Prophet/13. Training and Test Set in Time Series.srt 2.3 kB
  • 16. Facebook Prophet/14. Python - Training and Test Set.srt 2.3 kB
  • 6. Multilinear Regression/2. The Concept of Multilinear Regression.srt 2.2 kB
  • 7. Logistic Regression/3. Logistic Regression.srt 2.2 kB
  • 13. Gaussian Mixture/5. Python - Directory and Data.srt 2.2 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/13. Python - Training Dataframe.srt 2.2 kB
  • 4. Intermediary Statistics/8. Shapiro-Wilks Test.srt 2.1 kB
  • 10. Matching/12. The Curse of Dimensionality.srt 2.1 kB
  • 13. Gaussian Mixture/6. Python - Load Data.srt 2.1 kB
  • 15. Random Forest/6. Python - Loading Data.srt 2.1 kB
  • 11. PART C SEGMENTATION/1. What is Segmentation and why is it important.html 2.1 kB
  • 16. Facebook Prophet/1. Facebook Prophet - Game Plan.srt 2.0 kB
  • 5. Linear Regression/5. Python - Isolate X and Y.srt 2.0 kB
  • 15. Random Forest/7. Python - Transform Object into Numerical Variables.srt 2.0 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/13. Python - RFM Score.srt 2.0 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/7. Python - Creating Sales Variable.srt 2.0 kB
  • 3. Basic Statistics/11. EXERCISE Python - Mode.srt 2.0 kB
  • 16. Facebook Prophet/8. Python - Renaming Variables.srt 1.9 kB
  • 17. Where To Go From Here/1. Thank You!.srt 1.9 kB
  • 10. Matching/3. CASE STUDY Catholic Schools & Standardized Tests (Briefing).srt 1.9 kB
  • 6. Multilinear Regression/1. Multilinear Regression - Game Plan.srt 1.8 kB
  • 15. Random Forest/10. Python - Isolate X and Y.srt 1.8 kB
  • 8. PART B ECONOMETRICS & CAUSAL INFERENCE/1. What are Econometrics & Causal Inference, and why are they important.html 1.8 kB
  • 6. Multilinear Regression/19. Python - Model Predictions.srt 1.8 kB
  • 5. Linear Regression/1. Linear Regression - Game Plan.srt 1.7 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/2. Google Causal Impact - Game Plan.srt 1.7 kB
  • 6. Multilinear Regression/14. Under and Over Fitting.srt 1.7 kB
  • 14. PART D PREDICTIVE ANALYTICS/1. What are Predictive Analytics and why are they important.html 1.7 kB
  • 6. Multilinear Regression/13. Python - Adding Constant.srt 1.7 kB
  • 9. Google Causal Impact (Econometrics and Causal Inference)/3. Time Series Data.srt 1.7 kB
  • 1. Introduction/8. Monthly Coding Challenges, Free Resources and Guides.html 1.6 kB
  • 2. PART A STATISTICS/1. What are Statistics and why are they important.html 1.6 kB
  • 15. Random Forest/13. Python - Predictions.srt 1.5 kB
  • 16. Facebook Prophet/12. Python - Finishing Holiday Preparation.srt 1.5 kB
  • 13. Gaussian Mixture/1. Gaussian Mixture - Game Plan.srt 1.4 kB
  • 7. Logistic Regression/1. Logistic Regression - Game Plan.srt 1.4 kB
  • 17. Where To Go From Here/3. Endorsements On LinkedIn.html 1.4 kB
  • 13. Gaussian Mixture/10. Python - Gaussian Mixture Model.srt 1.4 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/10. Python - Monetary Variable.srt 1.4 kB
  • 13. Gaussian Mixture/7. Python - Transform Character variables.srt 1.4 kB
  • 3. Basic Statistics/17. EXERCISE Python - Standard Deviation.srt 1.3 kB
  • 3. Basic Statistics/1. Basic Statistics - Game Plan.srt 1.3 kB
  • 3. Basic Statistics/3. CASE STUDY Moneyball (Briefing).srt 1.3 kB
  • 16. Facebook Prophet/23. Cross-Validation.srt 1.3 kB
  • 18. BONUS Section/1. Special Bonus Lecture.html 1.3 kB
  • 15. Random Forest/1. Random Forest - Game Plan.srt 1.2 kB
  • 6. Multilinear Regression/15. Training and Test Set.srt 1.2 kB
  • 16. Facebook Prophet/4. CASE STUDY Wikipedia (Briefing).srt 1.1 kB
  • 5. Linear Regression/2. CASE STUDY Diamonds (Briefing).srt 1.1 kB
  • 7. Logistic Regression/2. CASE STUDY Spam Emails (Briefing).srt 1.1 kB
  • 12. RFM (Recency, Frequency, Monetary) Analysis/4. CASE STUDY Online Shopping (Briefing).srt 1.1 kB
  • 13. Gaussian Mixture/4. CASE STUDY Credit Cards #1 (Briefing).srt 1.1 kB
  • 6. Multilinear Regression/3. CASE STUDY Professors' Salary (Briefing).srt 1.0 kB
  • 4. Intermediary Statistics/1. Intermediary Statistics - Game Plan.srt 971 Bytes
  • 17. Where To Go From Here/2. Become An Alumni.html 921 Bytes
  • 4. Intermediary Statistics/19. CASE STUDY Remote Work Predictions (Briefing).srt 875 Bytes
  • 15. Random Forest/4. CASE STUDY Credit Cards #2 (Briefing).srt 860 Bytes
  • 17. Where To Go From Here/5. Coding Challenges.html 860 Bytes
  • 12. RFM (Recency, Frequency, Monetary) Analysis/1. RFM - Game Plan.srt 849 Bytes
  • 17. Where To Go From Here/4. Learning Guideline.html 353 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 1. Introduction/7.1 LinkedIn Group.html 102 Bytes
  • 1. Introduction/5.1 Course Materials.html 99 Bytes
  • 1. Introduction/7.3 ZTM Youtube.html 99 Bytes
  • 1. Introduction/7.2 zerotomastery.io.html 86 Bytes
  • 1. Introduction/5.2 Sign up for your free Google Drive account here..html 85 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes

随机展示

相关说明

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！