首页 磁力链接怎么用

[GigaCourse.Com] Udemy - Python for Machine Learning The Complete Beginner's Course

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2023-4-13 00:26 2024-12-23 05:54 170 684.84 MB 80
二维码链接
[GigaCourse.Com] Udemy - Python for Machine Learning The Complete Beginner's Course的二维码
种子下载(838888不存储任何种子文件)
种子下载线路1(迅雷)--推荐
种子下载线路2(比特彗星)
种子下载线路3(torcache)
3条线路均为国内外知名下载网站种子链接,内容跟本站无关!
文件列表
  1. 1. Introduction to Machine Learning/1. What is Machine Learning.mp47.48MB
  2. 1. Introduction to Machine Learning/2. Applications of Machine Learning.mp46.51MB
  3. 1. Introduction to Machine Learning/3. Machine learning Methods.mp43.7MB
  4. 1. Introduction to Machine Learning/4. What is Supervised learning.mp46.23MB
  5. 1. Introduction to Machine Learning/5. What is Unsupervised learning.mp45.95MB
  6. 1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.mp414.33MB
  7. 1. Introduction to Machine Learning/7.14 u.data1.98MB
  8. 2. Simple Linear Regression/1. Introduction to regression.mp48.97MB
  9. 2. Simple Linear Regression/2. How Does Linear Regression Work.mp47.68MB
  10. 2. Simple Linear Regression/3. Line representation.mp45.45MB
  11. 2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.mp47.55MB
  12. 2. Simple Linear Regression/5. Implementation in python Distribution of the data.mp49.46MB
  13. 2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.mp413.22MB
  14. 3. Multiple Linear Regression/1. Understanding Multiple linear regression.mp46.32MB
  15. 3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.mp413.31MB
  16. 3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.mp428.92MB
  17. 3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.mp48.83MB
  18. 3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.mp48.62MB
  19. 3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.mp417.83MB
  20. 3. Multiple Linear Regression/7. Evaluating the performance of the regression model.mp46.01MB
  21. 3. Multiple Linear Regression/8. Root Mean Squared Error in Python.mp411.83MB
  22. 4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.mp44.67MB
  23. 4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.mp49.67MB
  24. 4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.mp46.05MB
  25. 4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.mp43.48MB
  26. 4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.mp46.14MB
  27. 4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.mp45.11MB
  28. 4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.mp49.29MB
  29. 4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.mp419.69MB
  30. 4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.mp45.73MB
  31. 4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.mp412.51MB
  32. 5. Classification Algorithms Decision Tree/1. Introduction to decision trees.mp46.49MB
  33. 5. Classification Algorithms Decision Tree/2. What is Entropy.mp45.23MB
  34. 5. Classification Algorithms Decision Tree/3. Exploring the dataset.mp45.96MB
  35. 5. Classification Algorithms Decision Tree/4. Decision tree structure.mp46.39MB
  36. 5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.mp44.65MB
  37. 5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.mp416.98MB
  38. 5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.mp44.92MB
  39. 5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.mp410.44MB
  40. 6. Classification Algorithms Logistic regression/1. Introduction.mp46.59MB
  41. 6. Classification Algorithms Logistic regression/2. Implementation steps.mp45.49MB
  42. 6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.mp46.82MB
  43. 6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.mp47.18MB
  44. 6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.mp413.17MB
  45. 6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.mp47.83MB
  46. 6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.mp413.46MB
  47. 6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.mp410.76MB
  48. 7. Clustering/1. Introduction to clustering.mp44.26MB
  49. 7. Clustering/10. Importing the dataset.mp412.78MB
  50. 7. Clustering/11. Visualizing the dataset.mp412.43MB
  51. 7. Clustering/12. Defining the classifier.mp47.66MB
  52. 7. Clustering/13. 3D Visualization of the clusters.mp47.82MB
  53. 7. Clustering/14. 3D Visualization of the predicted values.mp412.84MB
  54. 7. Clustering/15. Number of predicted clusters.mp49.49MB
  55. 7. Clustering/2. Use cases.mp44.05MB
  56. 7. Clustering/3. K-Means Clustering Algorithm.mp46.62MB
  57. 7. Clustering/4. Elbow method.mp47.02MB
  58. 7. Clustering/5. Steps of the Elbow method.mp45.84MB
  59. 7. Clustering/6. Implementation in python.mp419MB
  60. 7. Clustering/7. Hierarchical clustering.mp47.42MB
  61. 7. Clustering/8. Density-based clustering.mp47.79MB
  62. 7. Clustering/9. Implementation of k-means clustering in python.mp43.93MB
  63. 8. Recommender System/1. Introduction.mp47.54MB
  64. 8. Recommender System/10. Data pre-processing.mp410.76MB
  65. 8. Recommender System/11. Sorting the most-rated movies.mp48.88MB
  66. 8. Recommender System/12. Grabbing the ratings for two movies.mp45.47MB
  67. 8. Recommender System/13. Correlation between the most-rated movies.mp413.29MB
  68. 8. Recommender System/14. Sorting the data by correlation.mp46.14MB
  69. 8. Recommender System/15. Filtering out movies.mp44.79MB
  70. 8. Recommender System/16. Sorting values.mp46.84MB
  71. 8. Recommender System/17. Repeating the process for another movie.mp412.66MB
  72. 8. Recommender System/2. Collaborative Filtering in Recommender Systems.mp44.16MB
  73. 8. Recommender System/3. Content-based Recommender System.mp44.88MB
  74. 8. Recommender System/4. Implementation in python Importing libraries & datasets.mp410.26MB
  75. 8. Recommender System/5. Merging datasets into one dataframe.mp44.19MB
  76. 8. Recommender System/6. Sorting by title and rating.mp419.33MB
  77. 8. Recommender System/7. Histogram showing number of ratings.mp45.67MB
  78. 8. Recommender System/8. Frequency distribution.mp46.05MB
  79. 8. Recommender System/9. Jointplot of the ratings and number of ratings.mp47.28MB
  80. 9. Conclusion/1. Conclusion.mp42.8MB
友情提示
不会用的朋友看这里 把磁力链接复制到离线下载,或者bt下载软件里即可下载文件,或者直接复制迅雷链接到迅雷里下载! 亲,你造吗?将网页分享给您的基友,下载的人越多速度越快哦!

违规内容投诉邮箱:[email protected]

概述 838888磁力搜索是一个磁力链接搜索引擎,是学术研究的副产品,用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络,实时的自动采集数据,仅存储文件的标题、大小、文件列表、文件标识符(磁力链接)等基础信息 838888磁力搜索不下载任何真实资源,无法判断资源的合法性及真实性,使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源,不提供Tracker服务,不提供种子文件的下载,这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统