|
RAR-files |
| ilearn-lrirs.rar |
50,000,000 |
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| ilearn-lrirs.r00 |
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| ilearn-lrirs.r01 |
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| ilearn-lrirs.r02 |
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| ilearn-lrirs.r03 |
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| ilearn-lrirs.r04 |
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| ilearn-lrirs.r05 |
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| ilearn-lrirs.r07 |
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| ilearn-lrirs.r08 |
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| ilearn-lrirs.r09 |
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| ilearn-lrirs.r10 |
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| ilearn-lrirs.r12 |
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| ilearn-lrirs.r15 |
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| ilearn-lrirs.r16 |
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| ilearn-lrirs.r17 |
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| ilearn-lrirs.r21 |
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| ilearn-lrirs.r22 |
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| ilearn-lrirs.r23 |
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| ilearn-lrirs.r24 |
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| ilearn-lrirs.r25 |
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| ilearn-lrirs.r30 |
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| ilearn-lrirs.r31 |
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| ilearn-lrirs.r32 |
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| ilearn-lrirs.r34 |
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| ilearn-lrirs.r38 |
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| ilearn-lrirs.r39 |
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| ilearn-lrirs.r40 |
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| ilearn-lrirs.r41 |
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| ilearn-lrirs.r42 |
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| ilearn-lrirs.r43 |
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| ilearn-lrirs.r44 |
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| ilearn-lrirs.r45 |
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| ilearn-lrirs.r46 |
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| ilearn-lrirs.r47 |
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| ilearn-lrirs.r48 |
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| ilearn-lrirs.r49 |
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| ilearn-lrirs.r50 |
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| ilearn-lrirs.r51 |
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| ilearn-lrirs.r52 |
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| Total size: |
2,747,348,405 |
|
|
| Archived
files |
|
4. This is a milestone!.mp4
[cd0ae45dee9a0604]
|
21,702,879 |
70831DAA |
|
5. Types of Statistics.mp4
[ef88a5d98ecd6cc0]
|
11,456,626 |
A3E9166B |
|
6. Describing data Graphically.mp4
[7b000e4a446e4eb1]
|
68,526,723 |
E86C08DC |
|
7. Measures of Centers.mp4
[aaa6d6386acfa5d3]
|
40,432,233 |
7220E148 |
|
9. Measures of Dispersion.mp4
[40c2d06cdb47ac99]
|
23,969,192 |
A30CB5CF |
|
11. Installing R and R studio.mp4
[c7b307c53a4cd3bd]
|
42,818,995 |
F87C583D |
|
12. Basics of R and R studio.mp4
[8f77d1d4b677c9d]
|
50,528,660 |
E03D83D2 |
|
13. Packages in R.mp4
[e661411920f398a2]
|
103,490,384 |
DCD7CAE1 |
|
14. Inputting data part 1 Inbuilt datasets of R.mp4
[96ffa87eb74bfb34]
|
48,409,790 |
C5878952 |
|
15. Inputting data part 2 Manual data entry.mp4
[f2050ed9ca99ad31]
|
32,343,050 |
D98EE860 |
|
16. Inputting data part 3 Importing from CSV or Text files.mp4
[cbd7daaa880d9165]
|
72,512,143 |
916EBEC4 |
|
17. Creating Barplots in R.mp4
[298d0d870762f0b9]
|
123,235,988 |
C58DAC74 |
|
18. Creating Histograms in R.mp4
[ac5dd013a8c338c6]
|
54,004,943 |
DC59C8CD |
|
19. Introduction to Machine Learning.mp4
[618511db3145c518]
|
129,903,340 |
CA47CA6C |
|
20. Building a Machine Learning model.mp4
[9849b5821307df6c]
|
47,468,973 |
5AC9FDC3 |
|
21. Gathering Business Knowledge.mp4
[d071b66d0b750927]
|
15,244,831 |
F63697AB |
|
22. Data Exploration.mp4
[cbb9d3864f888dc7]
|
21,101,346 |
BE1CC6E3 |
|
23. The Data and the Data Dictionary.mp4
[47425ec4cb4f516f]
|
91,971,445 |
88D80610 |
|
24. Importing the dataset into R.mp4
[fd066a855a18c7a5]
|
17,157,390 |
20E83A20 |
|
26. Univariate analysis and EDD.mp4
[b8fdb2e2c191c719]
|
28,623,178 |
80E541C5 |
|
27. EDD in R.mp4
[37c00508728deedd]
|
81,925,876 |
6E54CC67 |
|
29. Outlier Treatment.mp4
[9fb937df6323c8c6]
|
29,119,049 |
F1606E6C |
|
30. Outlier Treatment in R.mp4
[f9884c697de73219]
|
32,853,940 |
2027A404 |
|
32. Missing Value Imputation.mp4
[61d4a9663f0f0f41]
|
28,900,961 |
EF36044D |
|
33. Missing Value imputation in R.mp4
[c060df6e2bdb7b94]
|
24,604,275 |
01ED17E0 |
|
35. Seasonality in Data.mp4
[9caff16c9ee49d76]
|
21,872,877 |
067666E4 |
|
36. Variable transformation in R.mp4
[9e3eb01296df5e81]
|
48,831,962 |
D26D2B1D |
|
38. Dummy variable creation Handling qualitative data.mp4
[9612bd0058c78f11]
|
42,577,345 |
6577DDEA |
|
39. Dummy variable creation in R.mp4
[ab2324d026283dfd]
|
55,099,258 |
66FD56B8 |
|
41. Three Classifiers and the problem statement.mp4
[589fb2dd66f79971]
|
24,020,840 |
6E145823 |
|
42. Why can't we use Linear Regression.mp4
[747af88b3b8389a5]
|
21,386,196 |
55910FF4 |
|
43. Logistic Regression.mp4
[a3a71effcb335584]
|
41,002,505 |
1CCE1B94 |
|
44. Training a Simple Logistic model in R.mp4
[83a28a3eebab9778]
|
32,595,731 |
F0E63F90 |
|
46. Results of Simple Logistic Regression.mp4
[522283e4cdcf855b]
|
32,667,043 |
0AE4E0D5 |
|
47. Logistic with multiple predictors.mp4
[1d819ee489c5af1e]
|
10,480,981 |
CA6B2FB7 |
|
48. Training multiple predictor Logistic model in R.mp4
[2432220494ee536f]
|
19,248,574 |
73670C68 |
|
50. Confusion Matrix.mp4
[516a05a1b0bff972]
|
27,954,020 |
D4B4DFB1 |
|
51. Evaluating Model performance.mp4
[ce560b2ad65102d5]
|
44,883,594 |
99677E15 |
|
52. Predicting probabilities, assigning classes and making Confusion Matrix.mp4
[7af1180ebf78484e]
|
69,405,393 |
FF29E01A |
|
54. Linear Discriminant Analysis.mp4
[dc57726f0730cc31]
|
51,088,968 |
EDBA6FA7 |
|
55. Linear Discriminant Analysis in R.mp4
[a6fac6ed8696e9f4]
|
93,916,822 |
A88EF801 |
|
57. Test-Train Split.mp4
[331fd49250803cd4]
|
47,912,604 |
713ACE70 |
|
59. Test-Train Split in R.mp4
[3ab65c8712e6e928]
|
94,685,211 |
56B14ABE |
|
61. K-Nearest Neighbors classifier.mp4
[6366f79b006de2fc]
|
87,589,730 |
BBD3B5F8 |
|
62. K-Nearest Neighbors in R.mp4
[36163aa9fb921032]
|
83,709,243 |
FBD40ABD |
|
64. Understanding the results of classification models.mp4
[a387e98d0d732a]
|
48,186,356 |
967E1FEC |
|
65. Summary of the three models.mp4
[537fea463139d301]
|
26,483,472 |
C7E15252 |
|
67. The problem statement.mp4
[649474625b279bfd]
|
9,815,084 |
5F5AED54 |
|
68. Basic equations and Ordinary Least Squared (OLS) method.mp4
[c8087e77e0a97d10]
|
45,470,361 |
1B573ABA |
|
69. Assessing Accuracy of predicted coefficients.mp4
[8812e7bcbd10cf61]
|
96,591,176 |
97B2ACCA |
|
70. Assessing Model Accuracy - RSE and R squared.mp4
[539c528e5bd06968]
|
45,751,631 |
7BD8B3DF |
|
71. Simple Linear Regression in R.mp4
[ee3a9f38449d0bc0]
|
42,814,120 |
077A2DC8 |
|
72. Multiple Linear Regression.mp4
[442798f9095f9eb9]
|
36,005,423 |
646A780D |
|
73. The F - statistic.mp4
[b220074561cf98d]
|
58,708,090 |
3151F7AF |
|
74. Interpreting result for categorical Variable.mp4
[34b3b9a7d4876475]
|
23,592,417 |
828DE8CB |
|
75. Multiple Linear Regression in R.mp4
[c84e95152cbba6e2]
|
65,454,724 |
B253E5E0 |
|
76. The final milestone!.mp4
[8ffa2516cd9eebbc]
|
12,443,476 |
DD180D73 |
|
1. Welcome to the course!.mp4
[b07ffdeb4abcd7d6]
|
19,973,660 |
970FDF7D |
|
3. Types of Data.mp4
[a31770856f4396ee]
|
22,812,709 |
B31466C2 |
|
| Total size: |
2,747,337,806 |
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|
