UDEMY Machine Learning and Data Science A-Z Hands-on Python 2022 BOOKWARE-iLEARN

iLEARN
source: https://www.udemy.com/course/data-science-machine-learning-a-z-hands-on-python/
looking for locations in:
10 GBit .SE
10 GBit .NO
and other european locations

18M	1. Course Content.mp4
57M	10. NumPy2.mp4
85M	11. NumPy3.mp4
57M	12. NumPy4.mp4
153M	13. NumPy5.mp4
135M	14. NumPy6.mp4
96M	15. Pandas1.mp4
117M	16. Pandas2.mp4
118M	17. Pandas3.mp4
204M	18. Pandas4.mp4
100M	19. Visualization with Matplotlib1.mp4
26M	2. What is Machine Learning Some Basic Terms.mp4
206M	20. Visualization with Matplotlib2.mp4
189M	21. Visualization with Matplotlib3.mp4
143M	22. Visualization with Matplotlib4.mp4
130M	23. Visualization with Matplotlib5.mp4
155M	24. Reading and Modifying a Dataset.mp4
35M	25. Statistics1.mp4
208M	26. Statistics2.mp4
108M	27. Statistics3 - Covariance.mp4
130M	28. Missing Values1.mp4
220M	29. Missing Values2.mp4
74M	30. Outlier Detection1.mp4
131M	31. Outlier Detection2.mp4
32M	32. Outlier Detection3.mp4
66M	33. Concatenation.mp4
58M	34. Dummy Variable.mp4
187M	35. Normalization.mp4
46M	36. Learning Types.mp4
234M	37. Supervised Learning Models - Introduction and Understanding the Data.mp4
49M	38. k-NN Concepts.mp4
141M	39. k-NN Model Development.mp4
7.6M	4. Python IDE.mp4
229M	40. k-NN Training-Set and Test-Set Creation.mp4
26M	41. Decision Tree Concepts.mp4
67M	42. Decision Tree Model Development.mp4
55M	43. Decision Tree - Cross Validation.mp4
60M	44. Naive Bayes Concepts.mp4
59M	45. Naive Bayes Model Development.mp4
11M	46. Logistic Regression Concepts.mp4
113M	47. Logistic Regression Model Development.mp4
84M	48. Model Evaluation Concepts.mp4
175M	49. Model Evaluation - Calculating with Python.mp4
23M	5. IDE Installation.mp4
213M	50. Simple and Multiple Linear Regression Concepts.mp4
76M	51. Multiple Linear Regression - Model Development.mp4
50M	52. Evaluation Metrics - Concepts.mp4
160M	53. Evaluation Metrics - Implementation.mp4
27M	54. Polynomial Linear Regression Concepts.mp4
220M	55. Polynomial Linear Regression Model Development.mp4
31M	56. Random Forest Concepts.mp4
247M	57. Random Forest Model Development.mp4
27M	58. Support Vector Regression Concepts.mp4
122M	59. Support Vector Regression Model Development.mp4
71M	6. Installation of Required Libraries.mp4
39M	60. Introduction.mp4
45M	61. K-means Concepts1.mp4
22M	62. K-means Concepts2.mp4
36M	63. K-means Model Development1.mp4
104M	64. K-means Model Development2.mp4
103M	65. K-means - Model Evaluation.mp4
27M	66. DBSCAN Concepts.mp4
87M	67. DBSCAN Model Development.mp4
25M	68. Hierarchical Clustering Concepts.mp4
146M	69. Hierarchical Clustering Model Development.mp4
47M	7. Spyder Interface.mp4
18M	70. Introduction.mp4
126M	71. Support Vector Regression - Model Tuning.mp4
16M	72. K-Means - Model Tuning.mp4
134M	73. k-NN - Model Tuning.mp4
73M	74. Overfitting and Underfitting.mp4
38M	9. NumPy1.mp4
6.8G	total