|
RAR-files |
| ilearn-tsaafup.rar |
50,000,000 |
4030F5B9 |
| ilearn-tsaafup.r00 |
50,000,000 |
5A295DA6 |
| ilearn-tsaafup.r01 |
50,000,000 |
85127B39 |
| ilearn-tsaafup.r02 |
50,000,000 |
BF8A6701 |
| ilearn-tsaafup.r03 |
50,000,000 |
F2F77F8A |
| ilearn-tsaafup.r04 |
50,000,000 |
CDE2BF4C |
| ilearn-tsaafup.r05 |
50,000,000 |
DDE71ED3 |
| ilearn-tsaafup.r06 |
50,000,000 |
168D30FB |
| ilearn-tsaafup.r07 |
50,000,000 |
96947E7C |
| ilearn-tsaafup.r08 |
50,000,000 |
CA75ED73 |
| ilearn-tsaafup.r09 |
50,000,000 |
3714BEDE |
| ilearn-tsaafup.r10 |
50,000,000 |
632C80AD |
| ilearn-tsaafup.r11 |
50,000,000 |
56DF8724 |
| ilearn-tsaafup.r12 |
50,000,000 |
D4413DF6 |
| ilearn-tsaafup.r13 |
50,000,000 |
032947BC |
| ilearn-tsaafup.r14 |
50,000,000 |
882113F5 |
| ilearn-tsaafup.r15 |
50,000,000 |
F868E923 |
| ilearn-tsaafup.r16 |
50,000,000 |
2348AE91 |
| ilearn-tsaafup.r17 |
50,000,000 |
9BFDE851 |
| ilearn-tsaafup.r18 |
50,000,000 |
A0762992 |
| ilearn-tsaafup.r19 |
50,000,000 |
99823060 |
| ilearn-tsaafup.r20 |
50,000,000 |
24A52ECC |
| ilearn-tsaafup.r21 |
50,000,000 |
6610508C |
| ilearn-tsaafup.r22 |
50,000,000 |
6081877E |
| ilearn-tsaafup.r23 |
50,000,000 |
5F59CD74 |
| ilearn-tsaafup.r24 |
50,000,000 |
F08DE3FA |
| ilearn-tsaafup.r25 |
50,000,000 |
66AEBE2E |
| ilearn-tsaafup.r26 |
50,000,000 |
7655D2F3 |
| ilearn-tsaafup.r27 |
50,000,000 |
4D76FA70 |
| ilearn-tsaafup.r28 |
50,000,000 |
D72BEEE3 |
| ilearn-tsaafup.r29 |
50,000,000 |
BD5CA167 |
| ilearn-tsaafup.r30 |
50,000,000 |
E6047B5F |
| ilearn-tsaafup.r31 |
50,000,000 |
2A3AEC43 |
| ilearn-tsaafup.r32 |
50,000,000 |
04DE401F |
| ilearn-tsaafup.r33 |
50,000,000 |
28584918 |
| ilearn-tsaafup.r34 |
50,000,000 |
2B7BB0F9 |
| ilearn-tsaafup.r35 |
50,000,000 |
30BFEBD0 |
| ilearn-tsaafup.r36 |
50,000,000 |
0C6B0725 |
| ilearn-tsaafup.r37 |
50,000,000 |
1E3B1672 |
| ilearn-tsaafup.r38 |
50,000,000 |
B1F27F0B |
| ilearn-tsaafup.r39 |
50,000,000 |
C6181EAD |
| ilearn-tsaafup.r40 |
50,000,000 |
73F9A05D |
| ilearn-tsaafup.r41 |
50,000,000 |
AD76B046 |
| ilearn-tsaafup.r42 |
50,000,000 |
C9264326 |
| ilearn-tsaafup.r43 |
50,000,000 |
E4980B5E |
| ilearn-tsaafup.r44 |
50,000,000 |
C8ABBE3F |
| ilearn-tsaafup.r45 |
50,000,000 |
79C7C40E |
| ilearn-tsaafup.r46 |
50,000,000 |
FE9DB94D |
| ilearn-tsaafup.r47 |
50,000,000 |
A2513053 |
| ilearn-tsaafup.r48 |
50,000,000 |
9ECDA1E7 |
| ilearn-tsaafup.r49 |
50,000,000 |
A7E2D938 |
| ilearn-tsaafup.r50 |
50,000,000 |
47DED024 |
| ilearn-tsaafup.r51 |
50,000,000 |
6391CEE1 |
| ilearn-tsaafup.r52 |
50,000,000 |
9CDE3279 |
| ilearn-tsaafup.r53 |
50,000,000 |
216F3B67 |
| ilearn-tsaafup.r54 |
50,000,000 |
EB337361 |
| ilearn-tsaafup.r55 |
50,000,000 |
7E3832CA |
| ilearn-tsaafup.r56 |
50,000,000 |
581BB43D |
| ilearn-tsaafup.r57 |
50,000,000 |
EF677723 |
| ilearn-tsaafup.r58 |
50,000,000 |
14035723 |
| ilearn-tsaafup.r59 |
50,000,000 |
F30F841A |
| ilearn-tsaafup.r60 |
50,000,000 |
3ECF6C03 |
| ilearn-tsaafup.r61 |
50,000,000 |
264692DB |
| ilearn-tsaafup.r62 |
50,000,000 |
1C7EF5AF |
| ilearn-tsaafup.r63 |
50,000,000 |
840539BC |
| ilearn-tsaafup.r64 |
50,000,000 |
113D0A9B |
| ilearn-tsaafup.r65 |
50,000,000 |
E184FED2 |
| ilearn-tsaafup.r66 |
50,000,000 |
3A57396F |
| ilearn-tsaafup.r67 |
50,000,000 |
7438FBD1 |
| ilearn-tsaafup.r68 |
50,000,000 |
5F2687E0 |
| ilearn-tsaafup.r69 |
50,000,000 |
2A805B09 |
| ilearn-tsaafup.r70 |
50,000,000 |
FD495F92 |
| ilearn-tsaafup.r71 |
50,000,000 |
9B781D09 |
| ilearn-tsaafup.r72 |
50,000,000 |
16636524 |
| ilearn-tsaafup.r73 |
50,000,000 |
0399A86D |
| ilearn-tsaafup.r74 |
50,000,000 |
38DD54A7 |
| ilearn-tsaafup.r75 |
50,000,000 |
0D64C734 |
| ilearn-tsaafup.r76 |
50,000,000 |
79C5DDD6 |
| ilearn-tsaafup.r77 |
50,000,000 |
D52BE316 |
| ilearn-tsaafup.r78 |
50,000,000 |
895E33CB |
| ilearn-tsaafup.r79 |
50,000,000 |
00C32682 |
| ilearn-tsaafup.r80 |
50,000,000 |
60A06141 |
| ilearn-tsaafup.r81 |
50,000,000 |
FFBCBB03 |
| ilearn-tsaafup.r82 |
50,000,000 |
C7220C49 |
| ilearn-tsaafup.r83 |
50,000,000 |
8D87F435 |
| ilearn-tsaafup.r84 |
50,000,000 |
6D0282F3 |
| ilearn-tsaafup.r85 |
50,000,000 |
C3C4F731 |
| ilearn-tsaafup.r86 |
50,000,000 |
9A5A6425 |
| ilearn-tsaafup.r87 |
50,000,000 |
F50ED8F9 |
| ilearn-tsaafup.r88 |
50,000,000 |
9B1AE63B |
| ilearn-tsaafup.r89 |
50,000,000 |
2F1A6843 |
| ilearn-tsaafup.r90 |
50,000,000 |
440A835F |
| ilearn-tsaafup.r91 |
50,000,000 |
09044F2F |
| ilearn-tsaafup.r92 |
12,162,237 |
7C5C2608 |
|
| Total size: |
4,662,162,237 |
|
|
| Archived
files |
|
6. Forecasting model creation - Steps.mp4
[71cc1143b4949574]
|
10,605,796 |
2B55443C |
|
7. Forecasting model creation - Steps 1 (Goal).mp4
[874e878282788c0d]
|
36,159,642 |
DE3D1AF5 |
|
8. Time Series - Basic Notations.mp4
[4ac276ed732d4c12]
|
65,478,370 |
18D91475 |
|
9. Installing Python and Anaconda.mp4
[96b93a2674f3ab2b]
|
17,055,939 |
39A2B077 |
|
11. Opening Jupyter Notebook.mp4
[ba04c73ed0cbb31d]
|
68,345,102 |
AA5DD6AE |
|
12. Introduction to Jupyter.mp4
[59454c68dd80f376]
|
42,900,064 |
2B65E013 |
|
13. Arithmetic operators in Python Python Basics.mp4
[b1967424bdcb47d2]
|
13,358,361 |
75D2F32E |
|
14. Strings in Python Python Basics.mp4
[f1091ed9ee16cdc4]
|
67,555,359 |
8C4DE661 |
|
15. Lists, Tuples and Directories Python Basics.mp4
[dd890be2bf24f2c1]
|
63,252,037 |
DA090779 |
|
16. Working with Numpy Library of Python.mp4
[7af39b78559a0c69]
|
45,994,570 |
04795F3F |
|
17. Working with Pandas Library of Python.mp4
[8047e8c9dbc622ca]
|
49,167,900 |
8D78556B |
|
18. Working with Seaborn Library of Python.mp4
[14cb51eaebd411b8]
|
42,318,236 |
F8BA725E |
|
19. Data Loading in Python.mp4
[5b335028cf52d070]
|
114,166,608 |
7076B87A |
|
20. Time Series - Feature Engineering Basics.mp4
[453c900fd1d608a5]
|
62,354,242 |
61A20696 |
|
21. Time Series - Feature Engineering in Python.mp4
[cd85c7803bd824e3]
|
118,152,629 |
1031812B |
|
22. Time Series - Upsampling and Downsampling.mp4
[c8dadc749dd35816]
|
17,775,644 |
60A44F0B |
|
23. Time Series - Upsampling and Downsampling in Python.mp4
[e4a1648ff447105]
|
105,563,047 |
C618C5F0 |
|
24. Time Series - Visualization Basics.mp4
[3368c6f4653068cc]
|
66,799,401 |
ECE3C0BB |
|
25. Time Series - Visualization in Python.mp4
[3c79ed8898850581]
|
173,237,937 |
0A516CB2 |
|
26. Time Series - Power Transformation.mp4
[2dbad367ca86a299]
|
15,558,359 |
A5F482DC |
|
27. Moving Average.mp4
[f497a2daac67c295]
|
40,600,265 |
A31EC746 |
|
28. Exponential Smoothing.mp4
[4f92963ba854cc0f]
|
8,788,354 |
E9A3F86A |
|
29. White Noise.mp4
[25192773bf8f8941]
|
11,924,362 |
474869B3 |
|
30. Random Walk.mp4
[40f3961c567a4430]
|
22,190,620 |
8F122868 |
|
31. Decomposing Time Series in Python.mp4
[74f291a9445ff84d]
|
62,722,627 |
D1E68D00 |
|
32. Differencing.mp4
[50108551d0a00636]
|
33,917,438 |
01131DDD |
|
33. Differencing in Python.mp4
[b414659c40ceeb5d]
|
118,467,783 |
5853669D |
|
34. Test Train Split in Python.mp4
[a5edf6695972756d]
|
60,197,220 |
854CB3D7 |
|
35. Naive (Persistence) model in Python.mp4
[e5fe1a6b982f37a1]
|
45,472,576 |
C54CCB4D |
|
36. Auto Regression Model - Basics.mp4
[1c7f3ce0ff001292]
|
17,708,366 |
E3FE4C67 |
|
37. Auto Regression Model creation in Python.mp4
[84b3470c4a8ab87]
|
56,093,192 |
470903A9 |
|
38. Auto Regression with Walk Forward validation in Python.mp4
[2eb5fb5301698f34]
|
52,045,116 |
893FB2C0 |
|
39. Moving Average model -Basics.mp4
[aa5f61e9a411265b]
|
25,242,481 |
7520DD3E |
|
40. Moving Average model in Python.mp4
[4a39897b06ae3ddf]
|
59,423,957 |
1F14E948 |
|
41. ACF and PACF.mp4
[d8a93a29e2b48a90]
|
43,215,621 |
AEC28AEC |
|
42. ARIMA model - Basics.mp4
[6c764c2c0f39784b]
|
22,404,761 |
44BFBC2E |
|
43. ARIMA model in Python.mp4
[57045e087320984a]
|
78,034,525 |
99ADDF24 |
|
44. ARIMA model with Walk Forward Validation in Python.mp4
[9afbecab453a5111]
|
33,710,837 |
16483BDE |
|
45. SARIMA model.mp4
[d4bcd85b96562e0]
|
40,938,967 |
5689C415 |
|
46. SARIMA model in Python.mp4
[eb71f35c5c65c737]
|
69,445,765 |
5CE50850 |
|
47. Stationary Time Series.mp4
[18ff66ae0b0ea15b]
|
5,853,132 |
897C72E2 |
|
48. Introduction.mp4
[48146fd279121a91]
|
9,699,308 |
AE7F285C |
|
50. Gathering Business Knowledge.mp4
[8d219a7da911c4e4]
|
15,240,987 |
5FCF1EA1 |
|
51. Data Exploration.mp4
[9f24b95a3614f15e]
|
21,104,470 |
73E2AEF3 |
|
52. The Dataset and the Data Dictionary.mp4
[d81a7be8b596f0bd]
|
72,661,387 |
4C4F37D5 |
|
53. Importing Data in Python.mp4
[6550e6b33e1584fa]
|
29,180,117 |
35156AC0 |
|
54. Univariate analysis and EDD.mp4
[b6e1d39ec49a93fd]
|
25,387,622 |
5554A6E1 |
|
55. EDD in Python.mp4
[d5d7c73868307798]
|
64,801,811 |
A1FC8FD5 |
|
56. Outlier Treatment.mp4
[11a4a590fe721139]
|
25,656,818 |
E461FB18 |
|
57. Outlier Treatment in Python.mp4
[c9609bbee6507aa5]
|
73,667,497 |
9AA64E37 |
|
58. Missing Value Imputation.mp4
[f05167570d4f8514]
|
26,210,342 |
BD2A4298 |
|
59. Missing Value Imputation in Python.mp4
[f354ab5e460c81d5]
|
24,557,943 |
E0525625 |
|
60. Seasonality in Data.mp4
[6339c2a040b480b7]
|
17,853,845 |
FC7AD852 |
|
61. Bi-variate analysis and Variable transformation.mp4
[859e26616423e1d5]
|
105,412,006 |
EED3C6B0 |
|
62. Variable transformation and deletion in Python.mp4
[7b248ce1c5383da7]
|
46,256,384 |
4C6D4A74 |
|
63. Non-usable variables.mp4
[267672b50e1cf5d4]
|
21,224,561 |
3CBF8967 |
|
64. Dummy variable creation Handling qualitative data.mp4
[f871771859b102e4]
|
38,585,297 |
75206B3A |
|
65. Dummy variable creation in Python.mp4
[7c297f2f69f08a33]
|
27,825,664 |
D7480237 |
|
66. Correlation Analysis.mp4
[f07ca1e40c643db3]
|
75,073,894 |
D479FDE1 |
|
67. Correlation Analysis in Python.mp4
[d7deeced3a14b82a]
|
57,997,031 |
8436BC85 |
|
68. The Problem Statement.mp4
[1c0636ba05fe4d20]
|
9,823,822 |
13B3E237 |
|
69. Basic Equations and Ordinary Least Squares (OLS) method.mp4
[8c8e0b86d0c77cf6]
|
45,479,348 |
5054A3E3 |
|
70. Assessing accuracy of predicted coefficients.mp4
[d7e4f3cecfdc5c9c]
|
96,561,090 |
0BBEC748 |
|
71. Assessing Model Accuracy RSE and R squared.mp4
[6407415d92c0a868]
|
45,758,332 |
691B7E30 |
|
72. Simple Linear Regression in Python.mp4
[5cd96ae158cb27db]
|
66,507,845 |
5532141F |
|
73. Multiple Linear Regression.mp4
[e9c5b499e288540d]
|
35,975,958 |
B701AEB4 |
|
74. The F - statistic.mp4
[ef6fe082965edd9f]
|
58,703,089 |
AF509C90 |
|
75. Interpreting results of Categorical variables.mp4
[9ec3929d78d329d4]
|
23,587,865 |
A0640DA5 |
|
76. Multiple Linear Regression in Python.mp4
[c74c2ccc71cec16f]
|
73,118,460 |
4EBE2FD5 |
|
77. Test-train split.mp4
[40a101b16bf06ab5]
|
43,897,256 |
A2979A1B |
|
78. Bias Variance trade-off.mp4
[85cafb9abe8dcfef]
|
26,315,306 |
760B366C |
|
80. Test train split in Python.mp4
[5437d6612fb723ec]
|
47,030,146 |
1521E8EF |
|
81. Introduction to Neural Networks and Course flow.mp4
[ff5c997443b3e2fa]
|
30,469,500 |
CD0C4B09 |
|
82. Perceptron.mp4
[3452386ca2ff5e4e]
|
46,934,419 |
EEC6C6E6 |
|
83. Activation Functions.mp4
[92b529df940883f2]
|
36,297,624 |
89131263 |
|
84. Python - Creating Perceptron model.mp4
[36ef8d3502c5f862]
|
90,764,626 |
E2C64771 |
|
85. Basic Terminologies.mp4
[4b3e294ee45caa8f]
|
42,378,448 |
AFC9358B |
|
86. Gradient Descent.mp4
[60c3aca66d617c0a]
|
63,250,416 |
798A5E1E |
|
87. Back Propagation.mp4
[42eed854a1424a78]
|
128,116,340 |
4461A9C9 |
|
88. Some Important Concepts.mp4
[3015928029d9eef7]
|
65,183,725 |
B90180FD |
|
89. Hyperparameters.mp4
[bd1647c37c8f68e]
|
47,524,270 |
F73B8CAE |
|
90. Keras and Tensorflow.mp4
[a41440d8f3422c37]
|
15,633,576 |
8724810F |
|
91. Installing Tensorflow and Keras.mp4
[8c51706e2362406a]
|
21,031,629 |
2447D2E5 |
|
92. Dataset for classification.mp4
[5329867cec26fbd3]
|
58,854,798 |
C5914449 |
|
93. Normalization and Test-Train split.mp4
[759f5f7ffc60e465]
|
46,323,275 |
5B38C8D9 |
|
94. Different ways to create ANN using Keras.mp4
[a46fc0464fa0c852]
|
11,333,684 |
4B39CC11 |
|
95. Building the Neural Network using Keras.mp4
[e1a403b927cdaaef]
|
82,957,823 |
A32E2411 |
|
96. Compiling and Training the Neural Network model.mp4
[52be30d63996cc4c]
|
85,647,544 |
A030C003 |
|
97. Evaluating performance and Predicting using Keras.mp4
[8e5f3721dad042dc]
|
73,281,914 |
74FC7FD7 |
|
98. Building Neural Network for Regression Problem.mp4
[41427799a9809105]
|
163,415,952 |
D89D77A1 |
|
99. The final milestone!.mp4
[9e348698d0a8110b]
|
12,436,770 |
36590B54 |
|
1. Welcome to the course.mp4
[a97d12c4ef61fdad]
|
23,286,599 |
2228B571 |
|
2. What is Time Series Forecasting.mp4
[c3853c4d9965c79]
|
12,853,015 |
D1E58851 |
|
4. This is a milestone!.mp4
[85ab9222e9149b3e]
|
21,660,571 |
16FF99FB |
|
5. Time Series Forecasting - Use cases.mp4
[9b10b8a374ab7531]
|
27,155,121 |
78E0EAE2 |
|
| Total size: |
4,662,144,348 |
|
|
