UDEMY Artificial Neural Networks for Business Managers in R Studio BOOKWARE-iLEARN

UDEMY Artificial Neural Networks for Business Managers in R Studio BOOKWARE-iLEARN | Apps-Tutorials | MP4 | 3.11 GiB

991 kb/s 1280×720 | AAC 128 kb/s 2 CH

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iLEARN
source: https://www.udemy.com/course/neural-network-understanding-and-building-an-ann-in-r/
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10 GBit .SE
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File List (Click to Show)

22M	1. Welcome to the course.mp4
61M	10. Inputting data part 3 Importing from CSV or Text files.mp4
97M	11. Creating Barplots in R.mp4
43M	12. Creating Histograms in R.mp4
45M	13. Perceptron.mp4
35M	14. Activation Functions.mp4
41M	15. Basic Terminologies.mp4
61M	16. Gradient Descent.mp4
123M	17. Back Propagation.mp4
63M	18. Some Important Concepts.mp4
46M	19. Hyperparameters.mp4
30M	2. Introduction to Neural Networks and Course flow.mp4
15M	20. Keras and Tensorflow.mp4
23M	21. Installing Keras and Tensorflow.mp4
112M	22. Data Normalization and Test-Train Split.mp4
131M	24. Building,Compiling and Training.mp4
100M	25. Evaluating and Predicting.mp4
85M	26. ANN with NeuralNets Package.mp4
132M	27. Building Regression Model with Functional AP.mp4
80M	28. Complex Architectures using Functional API.mp4
217M	29. Saving - Restoring Models and Using Callbacks.mp4
61M	30. Hyperparameter Tuning.mp4
15M	31. Gathering Business Knowledge.mp4
21M	32. Data Exploration.mp4
70M	33. The Data and the Data Dictionary.mp4
14M	34. Importing the dataset into R.mp4
25M	35. Univariate Analysis and EDD.mp4
97M	36. EDD in R.mp4
25M	37. Outlier Treatment.mp4
31M	38. Outlier Treatment in R.mp4
25M	39. Missing Value imputation.mp4
21M	4. This is a milestone!.mp4
26M	40. Missing Value imputation in R.mp4
18M	41. Seasonality in Data.mp4
101M	42. Bi-variate Analysis and Variable Transformation.mp4
56M	43. Variable transformation in R.mp4
21M	44. Non Usable Variables.mp4
37M	45. Dummy variable creation Handling qualitative data.mp4
44M	46. Dummy variable creation in R.mp4
72M	47. Correlation Matrix and cause-effect relationship.mp4
84M	48. Correlation Matrix in R.mp4
9.4M	49. The problem statement.mp4
36M	5. Installing R and R studio.mp4
44M	50. Basic equations and Ordinary Least Squared (OLS) method.mp4
93M	51. Assessing Accuracy of predicted coefficients.mp4
44M	52. Assessing Model Accuracy - RSE and R squared.mp4
41M	53. Simple Linear Regression in R.mp4
35M	54. Multiple Linear Regression.mp4
56M	55. The F - statistic.mp4
23M	56. Interpreting result for categorical Variable.mp4
63M	57. Multiple Linear Regression in R.mp4
42M	58. Test-Train split.mp4
26M	59. Bias Variance trade-off.mp4
39M	6. Basics of R and R studio.mp4
76M	60. Test-Train Split in R.mp4
12M	61. The final milestone!.mp4
83M	7. Packages in R.mp4
41M	8. Inputting data part 1 Inbuilt datasets of R.mp4
26M	9. Inputting data part 2 Manual data entry.mp4
3.2G	total

File: 1. Welcome to the course.mp4
Size: 22235861 bytes (21.21 MiB), duration: 00:02:59, avg.bitrate: 994 kb/s
Audio: aac, 44100 Hz, stereo (und)
Video: h264, yuv420p, 1280x720, 30.00 fps(r) => 1279x720 (und)


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Keywords: UDEMY, Artificial, Neural, Networks, for, Business, Managers, Studio, BOOKWARE, iLEARN
Apps-Tutorials
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