|
RAR-files |
| ilearn-lralrip.rar |
50,000,000 |
76D9D7D1 |
| ilearn-lralrip.r00 |
50,000,000 |
315B7118 |
| ilearn-lralrip.r01 |
50,000,000 |
5101B1B3 |
| ilearn-lralrip.r02 |
50,000,000 |
3E81DA12 |
| ilearn-lralrip.r03 |
50,000,000 |
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| ilearn-lralrip.r04 |
50,000,000 |
26367987 |
| ilearn-lralrip.r05 |
50,000,000 |
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| ilearn-lralrip.r06 |
50,000,000 |
AEEAD8AD |
| ilearn-lralrip.r07 |
50,000,000 |
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| ilearn-lralrip.r08 |
50,000,000 |
CE70F1A3 |
| ilearn-lralrip.r09 |
50,000,000 |
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| ilearn-lralrip.r10 |
50,000,000 |
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| ilearn-lralrip.r11 |
50,000,000 |
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| ilearn-lralrip.r12 |
50,000,000 |
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| ilearn-lralrip.r13 |
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| ilearn-lralrip.r14 |
50,000,000 |
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| ilearn-lralrip.r15 |
50,000,000 |
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| ilearn-lralrip.r16 |
50,000,000 |
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| ilearn-lralrip.r17 |
50,000,000 |
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| ilearn-lralrip.r18 |
50,000,000 |
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| ilearn-lralrip.r19 |
50,000,000 |
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| ilearn-lralrip.r20 |
50,000,000 |
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| ilearn-lralrip.r21 |
50,000,000 |
80A91C12 |
| ilearn-lralrip.r22 |
50,000,000 |
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| ilearn-lralrip.r23 |
50,000,000 |
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| ilearn-lralrip.r24 |
50,000,000 |
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| ilearn-lralrip.r25 |
50,000,000 |
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| ilearn-lralrip.r26 |
50,000,000 |
034392BD |
| ilearn-lralrip.r27 |
50,000,000 |
38654AB5 |
| ilearn-lralrip.r28 |
50,000,000 |
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| ilearn-lralrip.r29 |
50,000,000 |
CEE9C010 |
| ilearn-lralrip.r30 |
50,000,000 |
3E6DF7B6 |
| ilearn-lralrip.r31 |
50,000,000 |
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| ilearn-lralrip.r32 |
50,000,000 |
1AE7EC71 |
| ilearn-lralrip.r33 |
50,000,000 |
3831D27B |
| ilearn-lralrip.r34 |
50,000,000 |
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| ilearn-lralrip.r35 |
50,000,000 |
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| ilearn-lralrip.r36 |
50,000,000 |
694C5256 |
| ilearn-lralrip.r37 |
50,000,000 |
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| ilearn-lralrip.r38 |
50,000,000 |
0657B1F6 |
| ilearn-lralrip.r39 |
50,000,000 |
1F02C275 |
| ilearn-lralrip.r40 |
50,000,000 |
AE9B8588 |
| ilearn-lralrip.r41 |
50,000,000 |
585100EA |
| ilearn-lralrip.r42 |
50,000,000 |
DEDDC41F |
| ilearn-lralrip.r43 |
50,000,000 |
29AA1E68 |
| ilearn-lralrip.r44 |
50,000,000 |
FB09FC90 |
| ilearn-lralrip.r45 |
50,000,000 |
E6221ABF |
| ilearn-lralrip.r46 |
50,000,000 |
C52AE1D7 |
| ilearn-lralrip.r47 |
50,000,000 |
58C37870 |
| ilearn-lralrip.r48 |
50,000,000 |
E04ADBFE |
| ilearn-lralrip.r49 |
50,000,000 |
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| ilearn-lralrip.r50 |
50,000,000 |
337A21C7 |
| ilearn-lralrip.r51 |
50,000,000 |
CCD2E6FB |
| ilearn-lralrip.r52 |
12,945,737 |
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|
| Total size: |
2,662,945,737 |
|
|
| Archived
files |
|
7. Arithmetic operators in Python Python Basics.mp4
[b1a17424bd4a4d5c]
|
13,359,011 |
BE4AF270 |
|
8. Strings in Python Python Basics.mp4
[f1091ed9ee16dd7b]
|
67,559,382 |
6A894D6B |
|
9. Lists, Tuples and Directories Python Basics.mp4
[dd890be2bf24ef2b]
|
63,251,119 |
02826D43 |
|
10. Working with Numpy Library of Python.mp4
[7e900c7a6fe9a82c]
|
46,002,436 |
7C301CFA |
|
11. Working with Pandas Library of Python.mp4
[a9eab21d96c82705]
|
49,159,965 |
E5F41324 |
|
12. Working with Seaborn Library of Python.mp4
[a9d51b9aaaa5a41a]
|
42,313,253 |
5559D36B |
|
13. Types of Data.mp4
[ec3caec104e33923]
|
22,812,335 |
5BE13041 |
|
14. Types of Statistics.mp4
[bc3c674747814c4c]
|
11,456,984 |
B846066A |
|
15. Describing data Graphically.mp4
[4e975114bd50d19f]
|
68,524,085 |
E7DDEFE9 |
|
16. Measures of Centers.mp4
[87ab1d47e8607e4f]
|
40,450,351 |
05274DB4 |
|
17. Measures of Dispersion.mp4
[313ed56b25a77409]
|
23,970,839 |
54B2AAEC |
|
18. Gathering Business Knowledge.mp4
[c2e1085e30b9c32d]
|
15,236,113 |
043812FC |
|
19. Data Exploration.mp4
[a91048b549a2d092]
|
21,110,825 |
DC0375B8 |
|
20. The Dataset and the Data Dictionary.mp4
[f40832eed35bd779]
|
72,738,076 |
0705D16B |
|
21. Importing Data in Python.mp4
[e00778e6f80b8a2a]
|
29,166,590 |
3712974B |
|
22. Univariate analysis and EDD.mp4
[40233ef4aa86079]
|
25,389,565 |
A5D9295C |
|
23. EDD in Python.mp4
[d5d7c738683058d6]
|
64,793,937 |
8E4CFF19 |
|
24. Outlier Treatment.mp4
[688c73f751eec70f]
|
25,695,253 |
2BD7BACB |
|
25. Outlier Treatment in Python.mp4
[8fbcc2dd517f3bc0]
|
73,651,268 |
1E607D39 |
|
26. Missing Value Imputation.mp4
[a1f643e1acaa9157]
|
26,208,851 |
2CA656BF |
|
27. Missing Value Imputation in Python.mp4
[95fd778789bbf1fb]
|
24,548,934 |
92728E78 |
|
28. Seasonality in Data.mp4
[beef05021697486b]
|
17,853,060 |
854F51C1 |
|
29. Bi-variate analysis and Variable transformation.mp4
[859e2661642214cb]
|
105,293,980 |
D13C263E |
|
30. Variable transformation and deletion in Python.mp4
[f68244f347e533d9]
|
46,238,650 |
6CC89071 |
|
31. Non-usable variables.mp4
[267672b50e1cfa6b]
|
21,225,736 |
0725599D |
|
32. Dummy variable creation Handling qualitative data.mp4
[e6eb91a1110c2c68]
|
38,599,149 |
68C2DD84 |
|
33. Dummy variable creation in Python.mp4
[7c297f2f69f080c1]
|
27,823,246 |
D26FF241 |
|
34. Correlation Analysis.mp4
[f1eabfd2c139c01e]
|
75,058,834 |
BC2E3680 |
|
35. Correlation Analysis in Python.mp4
[1ae16e43aa893407]
|
57,993,264 |
3F6BA9CF |
|
36. The Problem Statement.mp4
[5166975dd42b6763]
|
9,828,731 |
1F93C8B8 |
|
37. Basic Equations and Ordinary Least Squares (OLS) method.mp4
[2ffaba177ea6ad25]
|
45,465,506 |
7B1E4EE2 |
|
38. Assessing accuracy of predicted coefficients.mp4
[b99e439391c3aac5]
|
96,586,352 |
3080240B |
|
39. Assessing Model Accuracy RSE and R squared.mp4
[b4ddabc63a8e4920]
|
45,704,303 |
C8E51795 |
|
40. Simple Linear Regression in Python.mp4
[2e50ff9539c0205b]
|
66,487,669 |
8ECE438B |
|
41. Multiple Linear Regression.mp4
[a604641f0a347b30]
|
35,971,414 |
8DB65E77 |
|
42. The F - statistic.mp4
[13e76b672eea412f]
|
58,744,018 |
E502AF0A |
|
43. Interpreting results of Categorical variables.mp4
[33a59fd968a6f031]
|
23,585,103 |
89F01120 |
|
44. Multiple Linear Regression in Python.mp4
[cdc15c39185ce3e5]
|
73,124,636 |
9241D24A |
|
45. Test-train split.mp4
[d7ee7ab83510eebe]
|
43,897,881 |
CC5738BC |
|
46. Bias Variance trade-off.mp4
[b79f31ab9fa3f0c3]
|
26,313,530 |
6760843F |
|
48. Test train split in Python.mp4
[8128b779a7b47529]
|
47,060,581 |
6EB545AE |
|
49. The Dataset and the Data Dictionary.mp4
[bf76dd7909acd2fe]
|
83,044,263 |
68AF087B |
|
50. Data Import in Python.mp4
[58d4c9e788565294]
|
23,134,190 |
15A2B0FF |
|
51. EDD in Python.mp4
[5c707e6a97b97271]
|
81,343,966 |
13CB01BB |
|
52. Outlier Treatment in Python.mp4
[ab29cda3495e54b6]
|
49,643,320 |
623C2D1C |
|
53. Missing Value Imputation in Python.mp4
[f99cd49f1d5c8b11]
|
23,645,798 |
C46D58DF |
|
54. Variable transformation and Deletion in Python.mp4
[faf11633b28f34a1]
|
30,653,320 |
F9438467 |
|
55. Dummy variable creation in Python.mp4
[25041590277b4818]
|
27,657,464 |
46B7AF82 |
|
56. Why can't we use Linear Regression.mp4
[f723efc8d3d27fbf]
|
17,754,209 |
0144926F |
|
57. Logistic Regression.mp4
[8a29f20f04b53ee5]
|
34,518,884 |
F4903E45 |
|
58. Training a Simple Logistic Model in Python.mp4
[9f2fb9d632480d1a]
|
50,187,314 |
88CC9426 |
|
59. Result of Simple Logistic Regression.mp4
[ab781190e3c83ff8]
|
28,239,795 |
5F1DDBD3 |
|
60. Logistic with multiple predictors.mp4
[4e0a7cd8aadece1d]
|
8,941,268 |
76809532 |
|
61. Training multiple predictor Logistic model in Python.mp4
[852dbb72cda66a5a]
|
27,500,797 |
3F93145B |
|
62. Confusion Matrix.mp4
[a45e5e3a0f35d50c]
|
22,130,550 |
7AA969FD |
|
63. Creating Confusion Matrix in Python.mp4
[b4931038e5a466cb]
|
53,766,635 |
643A0913 |
|
64. Evaluating performance of model.mp4
[12b5fcbc01dab400]
|
36,862,976 |
D3FC99F2 |
|
65. Evaluating model performance in Python.mp4
[6960e211be419ba9]
|
9,443,638 |
87DDA06B |
|
66. Test-Train Split.mp4
[1e94c1ca4ba11bca]
|
41,190,102 |
BEA2D142 |
|
67. Test-Train Split in Python.mp4
[ddbd5267055fd0eb]
|
34,701,493 |
742791E0 |
|
68. The final milestone!.mp4
[9f84cc01d54724a5]
|
12,426,068 |
8613D6A6 |
|
1. Introduction.mp4
[a684567c7256e38e]
|
25,943,612 |
D0296834 |
|
3. Installing Python and Anaconda.mp4
[61024e5abc3238da]
|
17,054,835 |
F1DF1B8D |
|
4. This is a milestone!.mp4
[9e82e82dc03ed824]
|
21,636,614 |
AFCB4E88 |
|
5. Opening Jupyter Notebook.mp4
[ba04c73ed0cbf218]
|
68,361,225 |
617B6578 |
|
6. Introduction to Jupyter.mp4
[643999c01e2d3a46]
|
42,897,546 |
B9C9982D |
|
| Total size: |
2,662,934,697 |
|
|
