|
RAR-files |
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| Total size: |
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|
| Archived
files |
|
5. Opening Jupyter Notebook.mp4
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|
68,363,598 |
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|
6. Introduction to Jupyter - part 1.mp4
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|
30,206,159 |
93E64B04 |
|
7. Introduction to Jupyter - part 2.mp4
[ac6c06c502096401]
|
13,133,843 |
D9E6F5AF |
|
8. Arithmetic operators in Python Python Basics.mp4
[b1a17424bd4a4d5c]
|
13,359,011 |
BE4AF270 |
|
9. Strings in Python Python Basics.mp4
[f1091ed9ee16db89]
|
67,558,884 |
049F41EA |
|
10. Lists, Tuples and Directories Python Basics.mp4
[dd890be2bf24e2b1]
|
63,247,925 |
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|
11. Working with Numpy Library of Python.mp4
[979b7321ca23f300]
|
46,008,556 |
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|
12. Working with Pandas Library of Python.mp4
[a9eab21d96c844d5]
|
49,167,597 |
EC95A03D |
|
13. Working with Seaborn Library of Python.mp4
[411720d799b036c7]
|
42,309,097 |
F59AA762 |
|
14. Installing R and R studio.mp4
[4514061b57d82d0e]
|
37,418,720 |
7D69E9B5 |
|
15. Basics of R and R studio.mp4
[576869b30be9f555]
|
40,731,699 |
FD8FE796 |
|
16. Packages in R.mp4
[a224904be171104]
|
86,974,817 |
577C2DAF |
|
17. Inputting data part 1 Inbuilt datasets of R.mp4
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|
42,694,196 |
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|
18. Inputting data part 2 Manual data entry.mp4
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|
26,745,989 |
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|
19. Inputting data part 3 Importing from CSV or Text files.mp4
[9152a1daf92dfc3]
|
62,978,423 |
51DCDAC2 |
|
20. Creating Barplots in R.mp4
[2368480826f2b09a]
|
101,455,713 |
F95854E9 |
|
21. Creating Histograms in R.mp4
[fcf67398118074ba]
|
44,042,557 |
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|
22. Perceptron.mp4
[3452386ca2ff39b4]
|
46,925,049 |
DF69ABB4 |
|
23. Activation Functions.mp4
[92b529df94088100]
|
36,296,870 |
6C666BCF |
|
24. Python - Creating Perceptron model.mp4
[212ffb96a9825c48]
|
90,787,644 |
958D492B |
|
25. Basic Terminologies.mp4
[db07a899dace6ce3]
|
42,396,242 |
7B947A40 |
|
26. Gradient Descent.mp4
[60c3aca66d61a756]
|
63,261,500 |
E8332B94 |
|
27. Back Propagation.mp4
[42eed854a142619f]
|
128,122,267 |
4321A8F3 |
|
28. Some Important Concepts.mp4
[298d4009593d7c29]
|
65,192,741 |
3A695021 |
|
29. Hyperparameters.mp4
[331610ae4cf6a5a9]
|
47,557,747 |
ADA5614B |
|
30. Keras and Tensorflow.mp4
[75096fe7e65e27e6]
|
15,636,637 |
FFBC66ED |
|
31. Installing Tensorflow and Keras in Python.mp4
[8ae1706e233a4ccb]
|
21,034,798 |
18E3EA03 |
|
32. Installing TensorFlow and Keras in R.mp4
[e1c4acef63f7f7de]
|
23,923,725 |
D5EAD95B |
|
33. Python - Dataset for classification problem.mp4
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|
58,893,546 |
CE8F7B36 |
|
34. Python - Normalization and Test-Train split.mp4
[274ba830c34069dd]
|
46,349,574 |
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|
35. R - Dataset, Normalization and Test-Train set.mp4
[4749a0726b88cd5e]
|
117,233,187 |
21A44E30 |
|
37. Different ways to create ANN using Keras.mp4
[b8482a9b9c10e64]
|
11,330,025 |
E224664C |
|
38. Building the Neural Network using Keras.mp4
[de23920b3882a107]
|
82,979,680 |
A17CC1C6 |
|
39. Compiling and Training the Neural Network model.mp4
[52be30d6399648b3]
|
85,613,855 |
18EAA41F |
|
40. Evaluating performance and Predicting using Keras.mp4
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|
73,250,521 |
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41. Building,Compiling and Training.mp4
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|
137,072,294 |
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|
42. Evaluating and Predicting.mp4
[891ea2727d743671]
|
104,073,919 |
9DF875CA |
|
43. Building Neural Network for Regression Problem.mp4
[fac68aa2efcdfe5b]
|
163,428,718 |
4649FC78 |
|
44. Using Functional API for complex architectures.mp4
[391513e702987570]
|
96,607,239 |
B34045A5 |
|
45. Building Regression Model with Functional AP.mp4
[ff201e26895ca7fe]
|
137,494,820 |
AF808B49 |
|
46. Complex Architectures using Functional API.mp4
[b09f45fb214f3fee]
|
83,435,861 |
1B085E90 |
|
47. Saving - Restoring Models and Using Callbacks.mp4
[d616a85c2f72cb49]
|
158,989,445 |
A5DC49B6 |
|
48. Saving - Restoring Models and Using Callbacks.mp4
[9bd17eef9677a343]
|
226,582,500 |
3EBE595B |
|
49. Hyperparameter Tuning.mp4
[c3fe7668c4f89782]
|
63,578,823 |
491675E5 |
|
50. Hyperparameter Tuning.mp4
[b2c35c3d743a7a2c]
|
63,570,096 |
66E4890E |
|
51. Gathering Business Knowledge.mp4
[e765242657186265]
|
23,361,832 |
E6CD7388 |
|
52. Data Exploration.mp4
[b3bf89711fddb6e]
|
21,510,068 |
03311A91 |
|
53. The Data and the Data Dictionary.mp4
[f86f95dbe236eba]
|
72,701,909 |
C181D0E6 |
|
54. Importing Data in Python.mp4
[d0c28b3ca39ea7e0]
|
29,177,221 |
FB95887D |
|
55. Importing the dataset into R.mp4
[e2dc3388421befcc]
|
13,738,969 |
3A7BC043 |
|
56. Univariate Analysis and EDD.mp4
[db9f66bd56052b65]
|
25,372,833 |
35DB50D1 |
|
57. EDD in Python.mp4
[d5d7c73868302676]
|
64,781,041 |
8743730C |
|
58. EDD in R.mp4
[2958eb22ae418cde]
|
101,685,212 |
2B386E2A |
|
59. Outlier Treatment.mp4
[12d634d5e00c2c86]
|
25,664,355 |
70BBD086 |
|
60. Outlier Treatment in Python.mp4
[e0f7c74e33751adf]
|
73,653,223 |
C9F4DA05 |
|
61. Outlier Treatment in R.mp4
[deceb08947248e7f]
|
32,231,619 |
4E288B2B |
|
62. Missing Value imputation.mp4
[e9579354aef93efd]
|
26,192,445 |
B523D799 |
|
63. Missing Value Imputation in Python.mp4
[fec43066c1914068]
|
24,546,985 |
75F95C14 |
|
64. Missing Value imputation in R.mp4
[3fa8d64a55636b17]
|
27,254,791 |
B97E7F3B |
|
65. Seasonality in Data.mp4
[c3cc010223415219]
|
17,858,098 |
72DD1B36 |
|
66. Bi-variate Analysis and Variable Transformation.mp4
[859e26616422d496]
|
105,343,079 |
EFF3F284 |
|
67. Variable transformation and deletion in Python.mp4
[1ea66a45ceb597e]
|
46,254,760 |
4B861B5C |
|
68. Variable transformation in R.mp4
[63ba50c04c69e601]
|
58,109,745 |
A96E4163 |
|
69. Non Usable Variables.mp4
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|
21,221,846 |
0C30D0B4 |
|
70. Dummy variable creation Handling qualitative data.mp4
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|
38,620,520 |
AC820784 |
|
71. Dummy variable creation in Python.mp4
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|
27,815,081 |
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|
72. Dummy variable creation in R.mp4
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|
46,108,238 |
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|
73. Test-train split.mp4
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|
43,900,814 |
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74. Bias Variance trade-off.mp4
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|
26,312,783 |
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75. Test train split in Python.mp4
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|
47,040,598 |
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|
76. Test train split in R.mp4
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|
79,281,102 |
77D07F88 |
|
77. The final milestone!.mp4
[93cd1baac942ab6b]
|
12,436,849 |
3CED8720 |
|
1. Introduction.mp4
[ff5c997443b45b7d]
|
30,500,351 |
FAB6C978 |
|
3. Installing Python and Anaconda.mp4
[aaa932ae3c0691cf]
|
17,055,799 |
264C4068 |
|
4. This is a milestone!.mp4
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|
21,663,196 |
408D07A0 |
|
| Total size: |
4,329,411,469 |
|
|
