|
RAR-files |
| ilearn-sfdsup.rar |
50,000,000 |
6AF4F63A |
| ilearn-sfdsup.r00 |
50,000,000 |
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| ilearn-sfdsup.r01 |
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| ilearn-sfdsup.r02 |
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| ilearn-sfdsup.r07 |
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| ilearn-sfdsup.r08 |
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| ilearn-sfdsup.r09 |
50,000,000 |
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| ilearn-sfdsup.r10 |
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| ilearn-sfdsup.r11 |
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| ilearn-sfdsup.r12 |
50,000,000 |
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| ilearn-sfdsup.r13 |
50,000,000 |
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| ilearn-sfdsup.r14 |
50,000,000 |
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| ilearn-sfdsup.r15 |
50,000,000 |
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| ilearn-sfdsup.r16 |
50,000,000 |
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| ilearn-sfdsup.r17 |
50,000,000 |
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| ilearn-sfdsup.r18 |
50,000,000 |
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| ilearn-sfdsup.r19 |
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| ilearn-sfdsup.r20 |
50,000,000 |
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| ilearn-sfdsup.r21 |
50,000,000 |
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| ilearn-sfdsup.r22 |
50,000,000 |
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| ilearn-sfdsup.r23 |
50,000,000 |
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| ilearn-sfdsup.r24 |
50,000,000 |
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| ilearn-sfdsup.r25 |
50,000,000 |
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| ilearn-sfdsup.r26 |
50,000,000 |
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| ilearn-sfdsup.r27 |
50,000,000 |
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| ilearn-sfdsup.r28 |
50,000,000 |
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| ilearn-sfdsup.r29 |
50,000,000 |
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| ilearn-sfdsup.r30 |
50,000,000 |
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| ilearn-sfdsup.r31 |
50,000,000 |
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| ilearn-sfdsup.r32 |
50,000,000 |
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| ilearn-sfdsup.r33 |
50,000,000 |
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| ilearn-sfdsup.r34 |
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| ilearn-sfdsup.r35 |
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| Total size: |
1,830,828,148 |
|
|
| Archived
files |
|
4. Use of Statistics in Data Science.mp4
[501c22ce4a8e5211]
|
28,613,975 |
CD4E0A5B |
|
5. What is Python & need of Python in Data Science!.mp4
[1c849e3e47c13c3]
|
24,905,313 |
F545C852 |
|
6. How Python works.mp4
[d8069f9f38ddd275]
|
23,980,112 |
A9BF88A1 |
|
7. Installation of Anaconda Navigator.mp4
[48c3cfae54165437]
|
64,810,245 |
A8ADC006 |
|
8. Random Variable, Population and Sample Statistics.mp4
[6998616762fe6ce7]
|
19,944,383 |
840D127A |
|
9. Types of Statistics.mp4
[e00f79e0dc8917a9]
|
28,535,264 |
2DB4E098 |
|
10. What are Outliers & Measures of Central Tendancy(Mean,Median,Mode) .mp4
[21e6225b78ee874e]
|
38,354,561 |
C3FF8AF0 |
|
11. Mean,Mode Median implementation using Python.mp4
[5f76ad6adca2fe7e]
|
30,145,229 |
DD7F334D |
|
12. Measures of Spread (Variance,Standard Dev. ,Range,Inter-Quantile Range).mp4
[45646e4ed78208d1]
|
60,734,646 |
5107A4FF |
|
13. Outliers Detection and Removal using Python.mp4
[e8ecd6d9b02a9050]
|
77,572,471 |
2E6732EC |
|
14. Skewness in Data.mp4
[ab8dbe613609bb46]
|
46,389,742 |
D5915C71 |
|
15. Frequency Tables & Histogram.mp4
[cd9da85cf0936665]
|
13,673,512 |
5ABD5E17 |
|
16. Frequency Tables & Histogram in Python.mp4
[59dbc792fba912f1]
|
54,944,370 |
BD5457CC |
|
17. Types of Analysis.mp4
[30751130cb34246]
|
48,174,011 |
F8E7A0DA |
|
18. Types of Analysis in Python.mp4
[3ed5a61a2edeb550]
|
72,292,410 |
F78C2D32 |
|
19. Covariance and Co-relation.mp4
[6e51a80c9c4a02ec]
|
30,498,046 |
573683EB |
|
20. Co-relation using Python.mp4
[e520d8ad4f00b6aa]
|
36,045,121 |
86CA02CA |
|
21. Intro to Probability.mp4
[e5bd6b5f2614d7c0]
|
24,618,713 |
AD51AD8A |
|
22. What is Prob. Density Function(PDF) & Cumalative Density Function(CDF) .mp4
[47f43a81dfa86432]
|
37,521,231 |
8A2A709A |
|
23. Bayes Theorem.mp4
[f4b5029d757863b1]
|
30,180,108 |
4AE83CC0 |
|
24. What is a Distribution and why we use it.mp4
[daf9089c719a5800]
|
12,568,467 |
5559B654 |
|
25. Bionomial Distribution.mp4
[635cfcddf1483839]
|
24,347,421 |
0DA1E389 |
|
26. Binomial Dist. in Python.mp4
[dc7f7a8535af2b92]
|
26,550,350 |
49F7B8C5 |
|
27. Poisson's Distribution.mp4
[8eba039d9768a285]
|
16,456,704 |
605C256F |
|
28. Poisson Distribution in Python.mp4
[53201756d45792b5]
|
15,319,561 |
DC43FA4F |
|
29. Normal Distribution.mp4
[f79268de6e3adaae]
|
42,152,054 |
15249F22 |
|
30. Normal Distribution in Python.mp4
[befdb3aef25746e0]
|
15,437,834 |
F77E18ED |
|
31. Implementation of Z-score(Standarization) in python.mp4
[a4a3e4fd2b6724cb]
|
75,683,623 |
4A0B0C61 |
|
32. Log-Normal Distribution and Heavy-Tailed Distribution.mp4
[e0625612c4fef337]
|
18,779,971 |
6BC18A5F |
|
33. Log-Normal and Heavy Tailed Dist.. in Python.mp4
[7d44641010533a4e]
|
29,948,889 |
DEC5316F |
|
34. Q-Q Plot.mp4
[4388bc0a7aaa57e9]
|
10,593,426 |
C3CAF790 |
|
35. Central Limit Theorem.mp4
[1dfcad767880d20]
|
14,458,286 |
8DFF43EE |
|
36. Central Limit Theorem implemntation in Python.mp4
[7c4ec3a1fec71128]
|
82,399,169 |
6FCD4539 |
|
37. Chebyshew's Inequality.mp4
[cb2416f8959fd772]
|
31,358,892 |
C12BE9B8 |
|
38. Estimation Problem based on Z-stats.mp4
[78087d7c56d69c4e]
|
54,392,281 |
C11EC892 |
|
39. Hypothesis testing.mp4
[25e2a8145aea0d4e]
|
22,297,370 |
0FFE0E35 |
|
40. 1-tailed and 2-Tailed Test.mp4
[d143a4a02d05ff48]
|
22,159,197 |
71A646F9 |
|
41. Critical_Region.mp4
[2b6fe42845822def]
|
46,171,025 |
401ED1D6 |
|
42. Hypothesis Table.mp4
[720407075b09cd79]
|
61,256,849 |
A0B877C8 |
|
43. Level of Significance.mp4
[1d42642552932501]
|
13,361,345 |
A1559927 |
|
44. P-value.mp4
[3abf0ab0649f9e24]
|
14,218,009 |
F7283118 |
|
45. T-test and various types of T-test.mp4
[ed9dd5df59773e6a]
|
27,868,153 |
D561784E |
|
46. T-test in Python.mp4
[9afe13c072301125]
|
122,321,692 |
DF79299A |
|
47. Chi-Square Test.mp4
[733856c6dd0a055e]
|
58,752,276 |
6DE714E9 |
|
48. Anova Test.mp4
[a84660800bd79209]
|
138,254,789 |
1351F5FD |
|
1. Why to Learn Statistics.mp4
[8a18a02e15022f74]
|
6,813,449 |
62EB05F2 |
|
2. Utilize this GOLDEN oppurtunity , QnA section !!.mp4
[76f69cc12d6b560]
|
9,830,849 |
F28CF9D0 |
|
3. What Is Statistics.mp4
[eda4deb72ab5d8bd]
|
25,134,873 |
32730022 |
|
| Total size: |
1,830,820,267 |
|
|
