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365 Data Science - Customer Analytics in Python [CoursesGhar]

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文件列表

  1. 11. Deep Learning/4. Balancing the Dataset.mp4 45MB
  2. 1. A Brief Marketing Introduction/4. Price Elasticity.mp4 32MB
  3. 11. Deep Learning/5. Preprocessing the Data for Deep Learning.mp4 31MB
  4. 2. Segmentation Data/2. Importing and Exploring Segmentation Data.mp4 30MB
  5. 10. Modeling Purchase Quantity/2. Preparing the Data and Fitting the Model.mp4 30MB
  6. 1. A Brief Marketing Introduction/2. Marketing Mix.mp4 28MB
  7. 1. A Brief Marketing Introduction/1. Segmentation, Targeting, Positioning.mp4 28MB
  8. 11. Deep Learning/7. Training the Deep Learning Model.mp4 27MB
  9. 5. K-Means Clustering based on Principal Component Analysis/5. K-Means Clustering with Principal Components - Results.mp4 25MB
  10. 1. A Brief Marketing Introduction/3. Physical and Online Retailers - Similarities and Differences..mp4 23MB
  11. 11. Deep Learning/2. Exploring the Dataset.mp4 23MB
  12. 6. Purchase Data/2. Getting to know the Purchase Dataset.mp4 20MB
  13. 4. K-Means Clustering/3. K-Means Clustering - Results.mp4 20MB
  14. 11. Deep Learning/11. Predicting on New Data.mp4 19MB
  15. 8. Modeling Purchase Incidence/6. Purchase Probability by Segments.mp4 18MB
  16. 11. Deep Learning/9. Obtaining the Probability of a Customer to Convert.mp4 17MB
  17. 3. Hierarchical Clustering/2. Hierarchical Clustering - Implementation and Results.mp4 16MB
  18. 9. Modeling Brand Choice/7. Own and Cross-Price Elasticity by Segment - Comparison.mp4 16MB
  19. 9. Modeling Brand Choice/6. Own and Cross-Price Elasticity by Segment.mp4 16MB
  20. 11. Deep Learning/8. Testing the Model.mp4 16MB
  21. 7. Descriptive Analyses by Segments/4. Dissecting the revenue by segment.mp4 15MB
  22. 8. Modeling Purchase Incidence/4. Calculating Price Elasticity of Purchase Probability.mp4 14MB
  23. 9. Modeling Brand Choice/5. Cross Price Brand Choice Elasticity.mp4 14MB
  24. 7. Descriptive Analyses by Segments/1. Purchase Analytics Descriptive Statistics - Segment Proportions.mp4 14MB
  25. 8. Modeling Purchase Incidence/5. Price Elasticity of Purchase Probability - Results.mp4 13MB
  26. 2. Segmentation Data/1. Getting to know the Segmentation Dataset.mp4 12MB
  27. 7. Descriptive Analyses by Segments/3. Brand Choice.mp4 12MB
  28. 9. Modeling Brand Choice/4. Own Price Brand Choice Elasticity.mp4 11MB
  29. 11. Deep Learning/1. Introduction to Deep Learning for Customer Analytics.mp4 11MB
  30. 4. K-Means Clustering/2. K-Means Clustering - Application.mp4 11MB
  31. 10. Modeling Purchase Quantity/3. Calculating Price Elasticity of Purchase Quantity.mp4 11MB
  32. 5. K-Means Clustering based on Principal Component Analysis/2. Principal Component Analysis - Application.mp4 11MB
  33. 5. K-Means Clustering based on Principal Component Analysis/3. Principal Component Analysis - Results.mp4 10MB
  34. 7. Descriptive Analyses by Segments/2. Purchase Analytics Descriptive Statistics - Purchase occasion and purchase Incidence.mp4 9MB
  35. 6. Purchase Data/4. Applying the Segmentation Model.mp4 9MB
  36. 8. Modeling Purchase Incidence/3. Model Estimation.mp4 9MB
  37. 2. Segmentation Data/3. Standardizing Segmentation Data.mp4 9MB
  38. 5. K-Means Clustering based on Principal Component Analysis/6. Saving the Models.mp4 9MB
  39. 3. Hierarchical Clustering/1. Hierarchical Clustering - Background.mp4 9MB
  40. 11. Deep Learning/6. Outlining the Deep Learning Model.mp4 9MB
  41. 10. Modeling Purchase Quantity/1. Purchase Quantity Models. The Model - Linear Regression.mp4 8MB
  42. 8. Modeling Purchase Incidence/9. Comparing Price Elasticities with and without Promotion.mp4 7MB
  43. 4. K-Means Clustering/1. K-Means Clustering - Background.mp4 7MB
  44. 5. K-Means Clustering based on Principal Component Analysis/4. K-Means Clustering with Principal Components - Application.mp4 7MB
  45. 9. Modeling Brand Choice/1. Brand Choice Models. The Model - Multinomial Logistic Regression.mp4 7MB
  46. 9. Modeling Brand Choice/3. Interpreting the Coefficients.mp4 7MB
  47. 10. Modeling Purchase Quantity/4. Price Elasticity of Purchase Quantity - Results.mp4 7MB
  48. 8. Modeling Purchase Incidence/1. Purchase Incidence Models. The Model - Binomial Logistic Regression.mp4 6MB
  49. 8. Modeling Purchase Incidence/7. Purchase Probability Model with Promotion.mp4 6MB
  50. 9. Modeling Brand Choice/2. Prepare Data and Fit the Model.mp4 5MB
  51. 8. Modeling Purchase Incidence/8. Calculating Price Elasticities with Promotion.mp4 5MB
  52. 11. Deep Learning/10. Saving the Model and Preparing for Deployment.mp4 4MB
  53. 6. Purchase Data/3. Importing and Exploring Purchase Data.mp4 4MB
  54. 5. K-Means Clustering based on Principal Component Analysis/1. Principal Component Analysis - Background.mp4 4MB
  55. 6. Purchase Data/1. Purchase Analytics - Introduction.mp4 3MB
  56. 11. Deep Learning/3. How Are We Going to Tackle the Business Case.mp4 3MB
  57. 8. Modeling Purchase Incidence/2. Prepare the Dataset for Logistic Regression.mp4 3MB
  58. Uploaded by [Coursesghar.com].txt 1KB
  59. !! IMPORTANT Note !!.txt 298B
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  61. Join Our Telegram Group For More Updates !!!.url 138B
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