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  • HaArD
  • 2023-01-30 04:21:02
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ReScene version pyReScene Auto 0.7 iLEARN File size CRC
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69,437
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181 59D4C092
2,415 B672123F
RAR-files
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Archived files
7. Q and A.mp4 [8e942175fab8a6a3] 31,257,561 775894E2
8. Quiz Solution.mp4 [4e8b1edc6b24491] 48,780,532 4880F677
9. Thanks for your support!!.mp4 [a7d351a39a06ef8e] 26,209,371 43EE3001
10. Arithmetic and Assignment Operators.mp4 [84b81775d2504e82] 81,828,716 8EB2A0A3
11. Comparison, Logical, and Bitwise Operators.mp4 [3022752c279a274d] 65,439,050 8F5D0501
12. Identity and Membership Operators.mp4 [74fbb62cc9fe6df1] 41,126,865 C15398CC
13. Quiz Solution.mp4 [43ae65d4d6b0f966] 35,875,844 F0A916C1
14. String Formatting.mp4 [10bf092c4f22e101] 53,845,267 035F9D5A
15. String Methods.mp4 [427d8cc62365fcb3] 45,390,299 1004E212
16. User Input.mp4 [6b9c010095c2b344] 43,032,519 603CBD37
17. Quiz Solution.mp4 [dea98561cdb9d087] 55,685,505 7A4FD2F9
18. If, elif, and else.mp4 [a4cd8c78dc5a464] 69,106,291 FDDC0CEA
19. For and While.mp4 [5fbf7c46bbdb954] 55,644,563 7E92D879
20. Break and Continue.mp4 [568fbd939cf15d4] 42,701,676 F678F0DF
21. Quiz Solution.mp4 [3d2013f9b5db7b16] 51,424,348 EA46E545
22. Differences between Lists and Tuples.mp4 [55aef82094db486] 51,018,793 9FEBA9AE
23. Operations on Lists.mp4 [2c56e6bd41e5bc96] 46,555,237 A2D704E3
24. Operations on Tuples.mp4 [5fa8c6243c4a6195] 28,769,309 C4B7930A
25. Quiz Solution.mp4 [f14442dee0495cdc] 38,892,134 B1842E69
26. Introduction to Dictionaries.mp4 [e807e987c7fb6851] 70,077,260 00B07134
27. Operations on Dictionaries.mp4 [3eaca8c495547d2d] 39,304,725 165FE39C
28. Nested Dictionaries.mp4 [e8e1a85ddfc2d5e5] 63,490,109 8161B25A
29. Introduction to Sets.mp4 [12aaffddac9b1c19] 79,157,791 7F0B3100
30. Set Operations.mp4 [a7062f774dd8c2fe] 61,433,313 A91C308F
31. Quiz Solution.mp4 [24c3923508c01d9f] 40,128,996 3D0644C4
32. Introduction to Stacks and Queues.mp4 [17a1f68da98094b8] 51,059,785 7FD90ECC
33. Implementing Stacks and Queues using Lists.mp4 [ea471abbe4020d05] 38,271,361 8557EC6C
34. Implementing Stacks and Queues using Deque.mp4 [95e6d2113005a68c] 43,616,313 6C01B86D
35. Quiz Solution.mp4 [dfc450bba264665a] 41,427,770 C432688D
36. Time Complexity.mp4 [7c0ca46724292bbc] 125,967,036 F6F5AC2F
37. Linear Search.mp4 [b7c57204a9ff50b0] 100,158,630 FA0A1924
38. Binary Search.mp4 [34f1268d950d6bb5] 114,863,291 EFB1884C
39. Bubble Sort.mp4 [a330f1b6a3baccde] 79,215,620 17A4100E
40. Insertion and Selection Sort.mp4 [d87536185258bdff] 125,828,520 F81ADB76
41. Merge Sort.mp4 [b7050d20b796d152] 121,042,999 A4CE57C7
42. Quiz Solution.mp4 [4b904564614db72b] 76,794,784 73FD28E9
43. Introduction to Functions.mp4 [ac694cfae882f1d5] 42,176,508 4C23FDCA
44. Default Parameters in Functions.mp4 [d5133b280b5cae2e] 56,578,183 6B42129C
45. Positional Arguments.mp4 [9c672d39aaac024b] 33,666,387 EDBCC505
46. Keyword Arguments.mp4 [7eece44546b369a3] 37,998,461 DAB901C9
47. Python Modules.mp4 [698907018f9c9dfa] 44,774,533 2406F9DB
48. Quiz Solution.mp4 [66e06500998c48a1] 50,011,626 574A6E41
49. Lambda Functions.mp4 [d266fe176cee9dad] 55,724,590 3EBAB1F2
50. Filter, Map, and Zip Functions.mp4 [b19b94729e89db12] 83,749,371 69F7DCC1
51. List, set, and Dictionary Comprehensions.mp4 [8e62854e8a2efb57] 57,233,784 10547D1C
52. Quiz Solution.mp4 [3b1f29924cae49e4] 42,211,436 B7C8C6B5
53. Introduction to Aggregate Functions.mp4 [24ff58c98336cfca] 32,117,630 57F22C6A
54. Introduction to Analytical Functions.mp4 [da2b035d96b85d9d] 36,367,172 2B9F4CF1
55. Quiz Solution.mp4 [fc381a3d7ae74c7b] 40,049,205 166D3316
56. Solving the Factorial Problem using Recursion.mp4 [5194ceb1a0e1299b] 58,073,802 89E54D5B
57. Solving the Fibonacci Problem using Recursion.mp4 [b870fc770060d7ef] 65,720,636 1A84849F
58. Quiz Solution.mp4 [ba81c01fb7238162] 39,913,082 7A273A78
59. Introduction to Classes and Objects.mp4 [aea7d6e93222b1e1] 41,452,793 DF658F70
60. Inheritance.mp4 [ebdca6f51addaf9a] 34,069,030 97A4A6B8
61. Encapsulation.mp4 [871e6204734c41a7] 65,224,050 6D1766E9
62. Polymorphism.mp4 [1866060eaef76721] 48,499,382 986155DE
63. Quiz Solution.mp4 [544b211cf8760923] 42,431,336 529656CE
64. Introduction to datetime.mp4 [236df441fd28896c] 39,312,160 2C64CEE6
65. The date and time class.mp4 [7297c9c5b92ed605] 35,180,613 EF159554
66. The datetime class.mp4 [32f8bfd31f1daef8] 23,666,507 98AF8B8A
67. The timedelta class.mp4 [53d8a45271aa1ca6] 20,301,544 6CFA662B
68. Quiz Solution.mp4 [720cfb3ac1ed5f83] 46,215,361 A89305FF
69. Meta Characters for Regular Expressions.mp4 [67799af0f78b61e5] 77,622,740 C7597748
70. Built-in Functions for Regular Expressions.mp4 [d4a0291e122e0e81] 39,397,414 B48F031C
71. Special Characters for Regular Expressions.mp4 [39cf9d6194a06c] 42,908,506 1AE68303
72. Sets for Regular Expressions.mp4 [3ba428735d7fc723] 58,861,384 44D1C6ED
73. Quiz Solution.mp4 [54c7445094026f0e] 34,414,006 A190209C
74. Array Creation using Numpy.mp4 [332510b5fb12fe50] 53,383,707 EB8EEBAB
75. Mathematical Operations using Numpy.mp4 [db8ed967d291a1e4] 38,209,838 97C86CB8
76. Built-in Functions in Numpy.mp4 [807fa6fc8989da91] 41,929,973 0C7EB070
77. Quiz Solution.mp4 [47bc87fa8f6e051d] 60,400,939 D89DE99E
78. Reading Datasets using Pandas.mp4 [2bfbfeb58445740b] 68,946,606 5AC029D6
79. Plotting Data in Pandas.mp4 [326a7817ee68eefd] 37,480,556 64970E13
80. Indexing, Selecting, and Filtering Data using Pandas.mp4 [4a4f27ddc9e7436] 72,268,554 20F3B41B
81. Merging and Concatenating DataFrames.mp4 [a97de41a9bf7a357] 80,294,053 2C33A7C7
82. Lambda, Map, and Apply Functions.mp4 [35439b11334c98ce] 39,009,360 AA9CB7E4
83. Quiz Solution.mp4 [fa6a915961f184e7] 57,366,846 BC4B2C12
84. Causes and Impact of Missing Values.mp4 [9352cb4398c1c521] 67,501,391 9E9203C7
85. Types of Missing Values.mp4 [a9352777a3a1e3e5] 64,819,501 BAA98DC1
86. When should we delete the Missing values.mp4 [30305e792ae344c5] 83,482,840 2E5EA483
87. Imputing the Missing Values using the Business Logic.mp4 [3873370a56c5fe74] 77,503,805 8EAABCED
88. Imputing Missing Values using MeanMedianMode.mp4 [d3fd63ad187e33e9] 58,674,759 1ACC5910
89. Imputing Missing Values in a real-time scenario.mp4 [903e1ef20aec93a8] 86,563,755 77EABA5D
90. Quiz Solution.mp4 [a53544b493da3520] 51,548,936 31106C8F
91. How Outliers can be harmful for Machine Learning Models.mp4 [3d637a822439d55b] 72,389,541 7C11C602
92. Finding out Outliers from the Data.mp4 [a379ced321045ab2] 66,308,856 8B14ED7B
93. Using Winsorization to deal with Outliers.mp4 [6c544eb86f0ee604] 53,003,269 653D0261
94. Deleting and Capping the Outliers.mp4 [393ea1c831da0439] 63,706,493 AF5B368D
95. Dealing with Outliers in a real-world scenario.mp4 [9899fd47ff2f55d8] 53,370,599 ADD80DE0
96. Quiz Solution.mp4 [de61321cb189a11e] 58,799,631 C3FC28CF
97. Introduction to reindex, set_index, reset_index, and sort_index Functions.mp4 [51f850e0b33eac04] 46,857,525 CF41D8FA
98. Introduction to Replace and Droplevel Function.mp4 [a4981bce87ba4b13] 34,568,065 FB2502D9
99. Introduction to Split and Strip Function.mp4 [564476a060d40872] 39,648,828 BD7F72EA
100. Introduction to Stack, and Unstack Functions.mp4 [5738bb7747abaf8] 26,609,231 B6693424
101. Introduction to Melt, Explode, and Squeeze Functions.mp4 [2132f3f6d5ba23e8] 43,385,073 7D64012F
102. Data Cleaning on Big Mart Dataset.mp4 [7d1579a2e0f0bf8c] 40,153,977 CED03FD4
103. Data Cleaning on Movie Dataset.mp4 [cb648d0988e53355] 39,105,796 58A3AED1
104. Data Cleaning on Melbourne Housing Dataset.mp4 [e2ea5d62fbf92725] 44,177,088 F3740C93
105. Data Cleaning on Naukri Dataset.mp4 [75075590b745616c] 111,396,703 D9CD48A5
106. Univariate Analysis.mp4 [563e05f3d0627ea4] 59,818,099 7555FD9C
107. Bivariate Analysis.mp4 [bdf39e330067cedb] 47,179,548 2AAB4067
108. Multivariate Analysis.mp4 [951e8307f7959c83] 74,283,884 59A2A859
109. Quiz Solution.mp4 [c55e23e260bcbcee] 49,368,882 4F0F3DAE
110. Scatter Plots.mp4 [6a93eb12df6c08a4] 47,344,092 1C5826F2
111. Charts with Colorscale.mp4 [7d08e2f3b63804b3] 33,350,515 661FD1D8
112. Bar, Line, and Area Charts.mp4 [18d1f522bd76ac07] 50,892,539 4FE2E728
113. Facet Grids.mp4 [8bca504a711b8237] 39,759,518 E0429301
114. Statistical Charts.mp4 [983109c15817b3d1] 40,237,804 B025ECB6
115. Polar Charts.mp4 [f68892f1efdf5de5] 30,717,264 89AC6CE0
116. Subplots.mp4 [24522cc17a29a003] 36,483,871 DCF90F03
117. 3D Charts.mp4 [f2f5a1e5f1e3c75c] 25,757,819 A7E8AE57
118. Waffle Charts.mp4 [d2ad66d27d8e713d] 30,774,895 ADBC4680
119. Maps.mp4 [6b8e10f8adee8b68] 32,207,114 951A6E38
120. Quiz Solution.mp4 [4f01854b1db38ed7] 51,203,451 9180639A
121. Animation with Bubbleplot.mp4 [d84ab1aa759e6caf] 50,099,712 33A9B498
122. Animation with Facets.mp4 [d6c673a3c4f5e33f] 28,005,400 611BE337
123. Animation with Scatter Maps.mp4 [bb4df704fecaaf92] 23,752,635 D37FA75D
124. Animation with Choropleth Maps.mp4 [a7ad21616bd74f97] 32,053,825 1141FEDA
125. Quiz Solution.mp4 [80dadb718ed42030] 36,260,866 7874B8CA
126. Introduction to Ipywidgets.mp4 [b5ca6179af933c0e] 40,432,912 5005E631
127. Interactive Univariate Analysis.mp4 [557c44e6d8c2b3f2] 31,330,586 7796DDF6
128. Interactive Bivariate Analysis.mp4 [32a0f911060231a9] 35,506,388 79BB3326
129. Interactive Multivariate Analysis.mp4 [61b0cfba1469185d] 30,591,373 256E9D48
130. Quiz Solution.mp4 [a5b0be5e59450258] 56,430,603 D493F8BE
131. Sunburst Charts.mp4 [f38591287f511f36] 34,753,408 C1CEC28B
132. Parallel Co-ordinate Charts.mp4 [5517e77dc83765f0] 24,077,459 BDB4AA28
133. Funnel Charts.mp4 [e682075e0ed76c89] 41,040,544 CD4AE02B
134. Gantt Charts.mp4 [2cc9c6e502a3a830] 26,299,130 6E49730B
135. Ternary Charts.mp4 [9042e96549bc6c2c] 21,344,716 1F6BFFB1
136. Tree Maps.mp4 [27009c7f9171f888] 22,488,305 0C421EDB
137. Network Charts.mp4 [9830c8ced12b1fdb] 41,666,191 7CAD3033
138. Quiz Solution.mp4 [a19e4bf5a2031dab] 40,379,087 3E345D74
139. Introduction to Feature Engineering.mp4 [e34d88fc3aa5c0be] 62,944,910 8CA8FFC1
140. Removing Unnecessary Columns.mp4 [aca6b94a3f55383b] 59,621,856 25CCA4CF
141. Decomposing Time and Date Features.mp4 [677975e7034788f8] 40,151,106 D99DACFB
142. Decomposing Categorical Features.mp4 [904a0f6dc6fead15] 40,125,071 4C3CF0C1
143. Binning Numerical Features.mp4 [7fd101378ff889b] 62,234,196 F06A0C62
144. Aggregating Features.mp4 [cf7c4d0795661de7] 59,628,442 E47F4F0F
145. Introduction to Feature Engineering on Text Data.mp4 [f79d639f2d5e71dc] 35,462,283 27078C96
146. Reading and Summarizing the Text.mp4 [51b6a0c5aa5079e5] 31,948,588 8C5319CC
147. Finding the Length, Polarity and Subjectivity.mp4 [26df92ef74ff862c] 76,554,643 D1939473
148. Finding the Words, Characters, and Punctuation Count.mp4 [3daffe2d4c8e034] 38,044,396 A0A70658
149. Counting Nouns and Verbs in the Text.mp4 [b577b0de00c3d471] 32,944,119 CDDC2D46
150. Counting Adjectives, Adverb, and Pronouns.mp4 [377b606c7efa9ccd] 24,858,694 AA52CCBA
151. Introduction to Assign and Update Functions.mp4 [80a746ac3c81546d] 37,885,285 431442CB
152. Introduction to at_time and between_time Functions.mp4 [7438058eb98f33cc] 31,692,591 A36D654C
153. Introduction to nlargest and nsmallest Functions.mp4 [7792cd4b09fe45f6] 37,032,024 E4B874F5
154. Introduction to Expanding Function.mp4 [9bafcf29116138] 29,797,512 B6226901
155. Introduction to Cumulative Functions.mp4 [822d6be4dbdee180] 32,614,798 A0D0D2E2
156. Quiz Solution.mp4 [99b73794b157e196] 53,693,890 FC5A1EF1
157. Feature Engineering on Employee Data.mp4 [b795688f81aece0e] 59,914,976 E5AFCAEC
158. Feature Engineering on FIFA Data.mp4 [9966b03a1649a1ec] 46,922,732 518663C6
159. Feature Engineering on Hotel Reviews.mp4 [d000729997aae13d] 36,753,689 6AD196FE
160. Feature Engineering on Marketing Data.mp4 [697edd5a3b567403] 61,425,017 56C1F112
161. Feature Engineering on Titanic Data.mp4 [86944b43d521102e] 52,030,976 1A743D41
162. Quiz Solution.mp4 [694a692ec77ecafa] 67,993,764 D34DEC5A
163. Types of Encoding Techniques.mp4 [4ab869337a0906ea] 63,842,096 89D294AD
164. Label Encoding.mp4 [3632d3c86da9f330] 35,167,157 D84B87B0
165. Feature Mapping for Ordinal Variables.mp4 [d623b4ffa37d1357] 30,422,479 DC5F44BA
166. OneHot Encoding.mp4 [cca14b463a3bc424] 36,250,150 779C655F
167. Binary and BaseN Encoding.mp4 [d428b9a1a53acefd] 34,827,012 B476C38D
168. Mean and Frequency Encoding.mp4 [320147e85970c8b8] 23,939,478 CB0EC917
169. Quiz Solution.mp4 [c274c56f9b3b65e5] 54,405,458 33E76B48
170. Introduction to Skewness and Normal Distribution.mp4 [3265f4fba2fb2336] 39,376,254 70D8DFC6
171. Square and Cube Root Transformation.mp4 [55b4f8432d8f62cf] 41,328,480 B7851B1C
172. Log transformation.mp4 [40e02bd4933525c0] 29,371,022 B4438FB1
173. BoxCox transformation.mp4 [958a891b34c203d2] 34,092,578 927994A4
174. Quiz Solution.mp4 [60f2b9f741c4a04e] 59,415,365 2B51ABBB
175. Train, Test and Validation Split.mp4 [b29215ffbb6fbc91] 46,380,478 406893C6
176. Standardization and Normalization.mp4 [19f42d3b3702bbaa] 41,631,191 3854D0C7
177. Quiz Solution.mp4 [82bad005bf8d3e38] 60,278,045 8A9DFE31
178. Introduction to Linear Regression.mp4 [736093c50163601] 85,153,619 734D937D
179. Implementing Linear Regression using Sklearn.mp4 [25f6eecc07f678f7] 77,007,823 938B5615
180. Feature Selection using RFECV.mp4 [beefd23c3b2312f3] 90,088,133 5A279968
181. Data Transformation with Linear Regression.mp4 [e23963c87b854331] 60,299,944 FFE6A02B
182. Applying Cross Validation.mp4 [efba02a525ad929b] 110,741,324 30D15D4C
183. Analyzing the performance of Regression models.mp4 [d20eb0183a503e02] 114,255,376 5C039FB6
184. R2 score and adjusted R2 score intuition.mp4 [c4bf1c02e50498a6] 112,223,151 2762A042
185. MAE, RMSE, R2 and Adjusted R2 in code.mp4 [a5a569e0f57c3219] 51,373,033 5199A6CC
186. Applying real time prediction on our model.mp4 [dc22a0070f7040fb] 112,823,242 456737DF
187. Industry relevance of linear regression.mp4 [29e5c65461d1daf] 52,290,450 4126222C
188. Quiz Solution.mp4 [8c8e83c10af1a3c4] 79,277,282 8A289C61
189. Introduction to Logistic Regression.mp4 [f79b8de90a41dd79] 111,558,476 8D752B9D
190. Implementing Logistic Regression using Sklearn.mp4 [ced3097e95498617] 91,231,497 26EF7FB3
191. Feature Selection using RFECV.mp4 [9b233b5181cfcb2a] 44,184,666 3A7BCCFC
192. Hyperparameter tuning using Grid search.mp4 [cf9899f4f1f37a2f] 61,597,339 FA91DBDE
193. Applying Cross Validation.mp4 [76db26bfc40eccf7] 59,471,741 E521376B
194. How to analyze performance of a classification model.mp4 [4550273848fc48d1] 153,269,414 B1DE8AD7
195. Using accuracy score to analyze the performance of model.mp4 [71f9a64bdc8b5ba0] 58,222,941 7986F9C9
196. Using ROC-AUC score to analyze the performance of model.mp4 [48b8dabe86f24076] 154,795,999 EF6F27D6
197. Real time prediction using logistic regression.mp4 [db8fe7da3ac0a266] 78,269,246 6A54ABDA
198. Industry Relevance of Logistic Regression.mp4 [eb12b2a9ad0c9374] 62,785,685 51F0D374
199. Quiz Solution.mp4 [818a87797caa603d] 56,332,318 83641E0C
200. Introduction to Support Vector machines.mp4 [7d3982f47fa4feb3] 113,413,633 CC60A8AB
201. The kermel trick for support vector machine.mp4 [daf9d57daa5d252c] 73,787,471 95D764E6
202. Implementing support vector machine using sklearn.mp4 [7730447fdad61de9] 70,697,355 0B13DE80
203. Introduction to K nearest neighbors.mp4 [ff826a5297bad0b8] 109,389,446 B98EE5E9
204. Implementing KNN using Sklearn.mp4 [4ffa977a14f308b1] 34,831,031 CF94540A
205. Introduction to Naive Bayes.mp4 [cfe354e57ce540f7] 183,209,446 00251FD6
206. Implementing Naive Bayes using sklearn.mp4 [e3f511d2106e1308] 64,956,133 5B5FCDF9
207. When should we apply SVM, KNN and Naive bayes.mp4 [bf65497f37be678c] 73,144,177 1C84D478
208. Quiz Solution.mp4 [2570753b7fe3f33] 68,325,894 D0C02D72
209. Intuition for decision trees.mp4 [40252c622e7102f6] 85,963,435 10488CC7
210. Attribute selection method- Gini Index and Entropy.mp4 [5eaf938432fcdb9c] 229,272,773 3A783D1D
211. Advantages and Issues with Decision trees.mp4 [edaca9e1517de3f] 55,955,061 1621F1D3
212. Implementing Decision tree using Sklearn.mp4 [12642e9ab8562740] 37,543,185 FDBE5DF0
213. Understanding the concept of Bagging.mp4 [c9ce5262126c1f68] 69,184,200 91E72DBE
214. Introduction to Random forest.mp4 [2fd303122f1f3090] 71,391,094 3A3E5D79
215. Understanding the parameters of Random forest.mp4 [f63f1c904d9376e3] 56,259,443 37928546
216. Implementing random forest using Sklearn.mp4 [5a619df03cf84f9f] 50,196,523 E31D1914
217. Understading the concept of boosting.mp4 [c257487234a838b2] 59,909,971 0FD159AF
218. Intuition for Adaboost and Gradient Boosting.mp4 [e6ece110c26f9c16] 160,732,425 5F0A9A87
219. Implementing AdaBoost using sklearn.mp4 [49ea1c7a03e2013b] 95,223,417 2DF11FB9
220. Implementing Gradient Boosting using sklearn.mp4 [36f5577eb68c96f9] 70,168,408 9CE4E780
221. Getting High level intuition for XGBoost.mp4 [6fad60b6789d8e72] 43,057,008 EE0BDD3E
222. Implementing XGBoost using sklearn.mp4 [60128d1f84e0f23b] 68,308,081 AF948C5E
223. Introudction to Ensembling techniques.mp4 [ac39067486d2a296] 140,520,618 7B8F84B0
224. Why Imbalanced Data needs extra attention.mp4 [d5948c690ad207ee] 56,210,409 38C255DE
225. Using Resampling Techniques to Balance the Data.mp4 [f6dab98e3187eaff] 73,970,352 FB91952E
226. Solving a Real World Problem.mp4 [3a2d06cb0e1b8728] 59,737,033 6F1CA5B1
227. Preparing the Data for Predictive Modelling.mp4 [f333e76f5f72ed81] 60,729,922 7B4F6A1E
228. Applying Logistic Regression using Sklearn.mp4 [21e3cdf5cad2b8cb] 74,583,563 74277E28
229. Applying Random Forest using Sklearn.mp4 [1acb7e0499e48c2b] 44,721,745 1B3ED4E5
230. Quiz Solution.mp4 [7220f8fd9e73f075] 70,060,062 A2E6EE2A
231. Implementing Random Over Sampling using Imblearn.mp4 [176020bd53393c91] 57,044,651 7C26118F
232. Implementing Random Under Sampling using Imblearn.mp4 [2d8aecff119fc12f] 60,333,749 2CC470A7
233. Implementing Synthetic Sampling using Imblearn.mp4 [fa7931b81b1e2f77] 60,230,039 69918CFD
234. Implementing Neighbors based Sampling using Imblearn.mp4 [83bcce772471d41c] 67,116,778 6B317ED2
235. Combination of Oversampling and Under sampling.mp4 [5a07138fec1f492f] 58,457,615 D4A4A0A1
236. Implementing Ensemble Models for Imbalanced Data.mp4 [60d865ad3f09df12] 57,536,827 BC8C3D22
237. Introduction to XG Boost for Imbalanced Data.mp4 [349c6513d7596e40] 45,642,331 1938D826
238. Comparing the Results.mp4 [86024b844abdd8a7] 43,515,138 6DDE7FF4
239. Quiz Solution on Imbalanced Machine Learning.mp4 [784272af0578e418] 39,688,524 1F799B58
240. Introduction to Clustering.mp4 [2b60dc66eac6bfdc] 60,639,012 141DF085
241. Types of Clustering.mp4 [707111473af98402] 68,340,840 EB48A377
242. Applications of Clustering.mp4 [e5b70db7a508ea01] 58,657,002 85F5B16E
243. Using the Elbow Method for Choosing the Best Value for K.mp4 [4cb263d11a7fdd53] 70,304,037 0E2A97BC
244. Introduction to K Means Clustering.mp4 [2c2b5b75f33ec423] 51,669,420 BA55B961
245. Solving a Real World Problem.mp4 [c9dab8309dd2cf82] 74,452,012 075DA866
246. Implementing K Means on the Mall Dataset.mp4 [24d90179af22b0b2] 75,036,775 75E766DF
247. Using Silhouette Score to analyze the clusters.mp4 [d6e0a0b26e4df1ee] 101,013,987 BA2FF526
248. Clustering Multiple Dimensions.mp4 [37d6f4bf09c79409] 52,428,715 C9FCAC11
249. Introduction to Hierarchical Clustering.mp4 [ed1f5e9e562ea1c5] 92,782,842 500DDF43
250. Introduction to Dendrograms.mp4 [d4a76f64e2e139c1] 43,795,960 5A6803C3
251. Implementing Hierarchical Clustering.mp4 [7cb297b0202a80bb] 54,886,925 3B310125
252. Introduction to DBSCAN Clustering.mp4 [ddbb92003bcaeb57] 79,294,681 AD21BBE4
253. Implementing DBSCAN Clustering.mp4 [f6af96e5ee67a3ab] 50,189,372 95FAE262
254. Why High Dimensional Datasets are a Problem.mp4 [57c12635a15db4ab] 83,061,524 E1F2F0B5
255. Methods to solve the problem of High Dimensionality.mp4 [f50fcc0c131626c1] 59,927,217 FA6CEF8E
256. Solving a Real World Problem.mp4 [36b2cba2878a7676] 103,616,204 F3D8BBFA
257. Introduction to Correlation using Heatmap.mp4 [1f4c6ef30cc6cd23] 74,866,230 72CFF483
258. Removing Highly Correlated Columns using Correlation.mp4 [e5943f902a2dcac3] 51,230,375 C80A5384
259. Introduction to Variance Inflation Filtering.mp4 [e34bc92fb4757829] 51,018,565 1D8BCA55
260. Implementing VIF using statsmodel.mp4 [c79abf91074de424] 50,149,351 C78D6938
261. Introduction to Recursive Feature Selection.mp4 [87f5cbd1b24a5a6e] 59,363,077 F7023DE1
262. Implementing Recursive Feature Selection.mp4 [974f577136d2f6ea] 53,381,137 F895BC6E
263. Introduction the Boruta Algorithm.mp4 [b89d6d2541003947] 55,020,344 45A214AD
264. Implementing the Boruta Algorithm.mp4 [d8ad0f57c14775b6] 45,295,948 2A2721E5
265. Introduction to Principal Component Analysis.mp4 [cb6d76cd75cdb607] 77,364,519 CB6E0E8B
266. Implementing PCA.mp4 [ee577aaa093d8125] 58,209,970 000A9023
267. Introduction to t-SNE.mp4 [34c2e695ee79dfb5] 85,211,804 863D2B40
268. Implementing t-SNE.mp4 [bf16efebac26bd17] 37,852,769 5470BCEA
269. Introduction to Linear Discriminant Analysis.mp4 [339384eb843fed4e] 51,266,078 C6FB1BEC
270. Implementing LDA.mp4 [54144c90377740a7] 38,528,582 85D2E9AD
271. Difference between PCA, t-SNE, and LDA.mp4 [834e17b16ad4148f] 67,926,929 F2EC5F32
272. Introduction to Recommender systems.mp4 [d730a400dcb98c2b] 42,488,167 7679256F
273. What are it's Use Cases.mp4 [8fe6302a4adf636d] 47,242,851 31C4FF82
274. Types of Recommender Systems.mp4 [124fb2775f3d7a47] 59,275,161 2CE5EA31
275. Evaluating Recommender Systems.mp4 [531247bbd93a5dd6] 55,726,801 C2078A5B
276. Introduction to Content Based Filtering.mp4 [39872b62abfd5542] 61,860,589 53E41794
277. Preprocessing the Data for Content Based Filtering.mp4 [d907de4caed8996a] 80,392,036 7E00DDA7
278. Filtering Movies Based on Genres.mp4 [d18cbe5dc3184c2a] 61,587,030 B9B8E840
279. Introduction to Transactional Encoder.mp4 [b6970698275be67f] 66,461,516 A5B024B5
280. Recommending Similar Movies to Watch.mp4 [b26c6bcd4481843b] 58,747,362 524CD8C3
281. Quiz Solution.mp4 [e2a5458cc43cc703] 50,852,855 8CCFBB27
282. Introduction to Collaborative Filtering.mp4 [3a474609bbd022dc] 84,777,178 0290B418
283. Preprocessing the Data for Collaborative Filtering.mp4 [250c67091bee019f] 75,897,971 78755542
284. Implementation of User Based Collaborative Filtering.mp4 [1b7cff603055f623] 65,155,063 92EDFA32
285. Interpreting the Results obtained from User Based Filtering.mp4 [d66126aa02d2a49c] 66,680,052 32740CBA
286. Implementation of Item Based Collaborative Filtering.mp4 [278c0886885d4504] 66,630,541 4663C351
287. Quiz Solution.mp4 [c7c2919e22ec1c25] 58,316,583 ED1488C2
288. Introduction to SVD.mp4 [1b2f906767237f46] 117,464,622 D7951226
289. Implementing SVD using Surprise.mp4 [7a048ea319d5dffa] 42,594,411 AB1901BE
290. Interpreting Results Obtained from SVD.mp4 [464c77183e49bf24] 48,230,103 0C5F0F60
291. Comparing Content, and Collaborative Based Filtering.mp4 [f0865e28762454f1] 64,994,126 FEA66F95
292. Quiz Solution.mp4 [dd504fb8e5835364] 50,261,587 76CE232F
293. Case Study for Netflix.mp4 [411f04870244f6c3] 59,107,796 1FE78996
294. Case Study for Youtube.mp4 [92a18e190f10539e] 60,951,107 A2E567A1
295. What is a Time Series Data.mp4 [63d7545aff55c8fb] 36,596,736 EDAD7003
296. Types of Forecasting.mp4 [83b9ff87a546edb4] 47,496,121 B95B2FBB
297. Regression Vs Time Series.mp4 [f3bf296d3b4431f1] 86,969,133 B2595D6A
298. Applications of Time Series.mp4 [504951aa1edbbbcd] 49,574,765 72C00905
299. Components of Time Series.mp4 [2fc3c17d7fbb9791] 54,470,246 10081E3B
300. Quiz Solution.mp4 [6cc24d19b06ab80b] 61,767,739 EB96B08A
301. Getting Time Series data.mp4 [b35bc27cb3fa682c] 74,525,439 ADC57E41
302. Handling Missing Values.mp4 [cc59052368eb5d1a] 122,120,115 E28659C5
303. Handling Outlier Values.mp4 [3c64d8b622f27574] 67,554,633 95060632
304. Time Series Decomposition.mp4 [b58019ef975ac174] 94,293,411 1109CE01
305. Splitting Time Series Data.mp4 [36e330a67fe9629f] 66,584,470 97BA1427
306. Quiz Solution.mp4 [83fd539f98be8ba9] 61,589,948 AD96A2C2
307. Basic Forecasting Techniques.mp4 [b2d690972fc459dc] 58,174,176 686AE018
308. Metrics for Time series Forecasting.mp4 [ee78b9cda8ea8e77] 82,512,701 18EF81A6
309. Simple Moving Averages.mp4 [3cfdb816bb4a5f1c] 52,548,971 A70082FB
310. Simple Exponential Smoothing.mp4 [322a27b9581fd9b9] 69,857,358 3594C52C
311. Holt and Holt Winter Exponential Smoothing.mp4 [df5b4ef08ef6f12c] 76,678,712 F6D8AB83
312. Quiz Solution.mp4 [7456af06dccaadcd] 45,183,706 56CA30AE
313. Introduction to Auto Regressive Models.mp4 [25c20f6f4571f1b2] 36,381,643 B99FBA69
314. Checking for Stationarity Part 1.mp4 [109ee4c8dcb41581] 68,150,471 3A12A2AC
315. Checking for Stationarity using Statistical Methods Part 2.mp4 [4d35d6f0f5e7aca8] 79,109,269 C9C1A5DD
316. Checking for Stationary Implementation.mp4 [4b2a64e0aa428c93] 39,940,372 63450064
317. Converting Non-Stationary Series into Stationary.mp4 [e2e955d10d52d2c] 50,421,446 294560EE
318. Converting Non-Stationary Series into Stationary Implementation.mp4 [f65e9405acd6545e] 50,501,036 4E7C12AC
319. Auto Correlation and Partial Correlation.mp4 [4ef4e37208200fd4] 80,567,992 CBD160D4
320. Auto Correlation and Partial Correlation Implementation.mp4 [c5e9b645a88fc92c] 40,349,275 3EBDC106
321. The Simple Auto Regressive Model.mp4 [27a2190da9aa94ce] 66,494,345 18140073
322. The Simple Auto Regressive Model Implementation.mp4 [6075a9cefb18b64a] 68,123,145 85F30E93
323. Moving Average Model.mp4 [d60ecb0739c37e17] 37,001,185 22AC14EF
324. Moving Average Model Implementation.mp4 [d431a58d42dd32b5] 24,346,900 2935B1DE
325. Quiz Solution.mp4 [19e8c2ec599f3205] 40,152,239 6A0F22E9
326. Understanding ARMA Model.mp4 [81eb045505e0eb6f] 59,538,023 3CF44E7A
327. Implementing ARMA Model.mp4 [55466cad7a55a2f0] 50,554,548 2F5D48BE
328. Understanding ARIMA Model.mp4 [300c5a0426eb1ffa] 58,577,437 0F9EFDBF
329. Implementing ARIMA Model.mp4 [6caace63a22b0648] 34,797,821 B1A20A2F
330. Understanding SARIMA Model.mp4 [8e7251524a2abe70] 73,335,140 598E7F01
331. Implementing SARIMA Model.mp4 [84764bb7740937d] 39,986,538 73561C5E
332. Quiz Solution.mp4 [57ec589632f067dc] 45,534,455 ACFADCF9
333. Understanding ARIMAX Model.mp4 [e86188fffdfc03bd] 69,726,328 AF642371
334. Implementing ARIMAX Model.mp4 [999098d3fd309817] 46,919,044 74F02471
335. Understanding SARIMAX Model.mp4 [8c6fb464f517a96] 45,970,410 B9BE71A8
336. Implementing SARIMAX Model.mp4 [92331929dde2b99c] 62,877,092 90C7BA0E
337. Quiz Solution.mp4 [b221b87dacb361ef] 35,857,059 6C35A19C
338. How to Choose the Right Model.mp4 [ab0a3e954885b984] 36,840,197 BB4B49B2
339. Choosing the Right for Model Smaller Datasets.mp4 [955fe3b879ce74a1] 54,834,392 817F39B3
340. Choosing the Right Model for Larger Datasets.mp4 [b7ddc8e605376489] 38,064,586 EC7A62DA
341. Best Practices while Choosing a Time series Model..mp4 [cd3ba3fcd2e43e07] 45,108,426 7ADA1AD6
342. Quiz Solution.mp4 [b9445ec14a7cc02b] 49,050,563 B7F3F90A
343. Why do we Evaluate Performance.mp4 [4f6844f00d0611c4] 33,314,304 529551DB
344. Mean Forecast Error.mp4 [93d9b8d7f4e7fa2] 55,484,131 7171A6FE
345. Mean Absolute Error.mp4 [f3ba653c839fd5c0] 37,278,638 832AE54D
346. Mean Absolute Percentage Error.mp4 [7da366f1089d222b] 31,191,683 3BE3D411
347. Root Mean Squared Error.mp4 [8fb344a30900ea02] 30,764,631 1DBBD60B
348. Quiz Solution.mp4 [fbcf72faea6cec9c] 48,246,392 E1647DE1
349. Setting up the Environment.mp4 [2a4ef5f092d3adf9] 43,726,903 66BB8F27
350. Understanding the Dataset.mp4 [4b2fc03f531d9d] 100,523,065 20406762
351. Understanding the Problem Statement.mp4 [5bc9e1f34c92286] 62,669,006 BE4F355C
352. Performing Descriptive Statistics.mp4 [e1885297960fc56b] 64,668,254 BE4EEBFD
353. Missing Values Treatment.mp4 [7250c2ad84c9b797] 40,535,486 36FA6B92
354. Outlier Values Treatment.mp4 [6e8f51a3a4a2019e] 44,541,341 BD0C1DD5
355. Univariate Analysis.mp4 [dd4da97c7e2afcdc] 55,715,088 CD9174D4
356. Bivariate Analysis.mp4 [ef9858073d036291] 38,957,050 ACFBF52B
357. Multivariate Analysis.mp4 [a41c93489d69740d] 41,882,801 7584089B
358. Feature Engineering.mp4 [13bd48f41cc80b35] 52,869,276 F2FEBF90
359. Categorical Encoding.mp4 [d94db905f15aebe6] 39,246,806 27B8D5A4
360. Data Processing.mp4 [a170607d052bf0a1] 70,926,688 BA8ADBAB
361. Feature Scaling.mp4 [d826886a57fb63a2] 44,318,806 18120A81
362. Predictive Modelling.mp4 [7d9141353f435514] 46,809,062 5CEC373E
363. Performance Analysis.mp4 [33bc0245a2ab0f7f] 80,911,365 A6558981
364. Improvements Possible.mp4 [f833b1b52dc4aee5] 43,893,711 CDA701E7
365. Major Takeaways from the Project.mp4 [2077a44d96dce0b6] 30,389,814 8B436762
366. Setting up the Environment.mp4 [867e8c7b4685514d] 52,542,609 C260FFB7
367. Understanding the Dataset.mp4 [c1b834ff384047d5] 109,100,636 E11CA891
368. Understanding the Problem Statement.mp4 [552af8f9677a1497] 64,789,611 ABB86C4C
369. Performing Univariate Analysis.mp4 [58ceb9ab7995a84] 94,100,637 4EBB2864
370. Performing Bivariate Analysis.mp4 [4937f40690a54cd8] 74,920,338 1943CD65
371. Performing Multivariate Analysis.mp4 [651462457f6eac0d] 90,136,437 2CDBE43F
372. Preparing the data for Modelling.mp4 [900ae1adc75bf7c5] 95,277,415 47191020
373. Applying Linear Regression Model.mp4 [fc4c298f5a4b48be] 134,295,396 250957AF
374. Applying Random Forest Model.mp4 [ca1b2a7b03116732] 57,023,777 D8F0DC37
375. Applying Gradient Boosting Model.mp4 [e9ad29bbdbff97f] 73,800,250 891437AF
376. Creating Ensembles of Models.mp4 [19fd7ebadeda1de4] 59,834,194 B1555AEA
377. Comparing Performance of these Models.mp4 [4e791d7693835c11] 38,319,394 60272B21
378. More things to Try.mp4 [7df9361ebdd86a71] 51,039,899 283CA983
379. Major Takeaways from the Project.mp4 [ccb15e97cae88fc3] 60,432,583 16267CC6
380. Understanding the Problem Statement.mp4 [9291fce403c499fb] 47,702,972 2025B78E
381. Setting up the Environment.mp4 [68f441d766ee80ef] 71,921,148 A93E2FC6
382. Understanding the Dataset.mp4 [65a9fb671be4a606] 43,116,341 F69C59E5
383. Performing Descriptive Statistics.mp4 [18d93e58f8ac9ef7] 78,968,093 74CC4A8D
384. Data Cleaning.mp4 [647ec7fdc7ecf818] 70,217,339 F9AD6A75
385. Univariate Data Visualizations.mp4 [8398794fbb325f3a] 68,329,294 CD08C589
386. Bivariate Data Analysis.mp4 [df4c4168adbd3a3a] 73,614,400 6093590B
387. Preparing the Data for Modelling.mp4 [2e5bce3ab3c86cbb] 44,900,939 2B24AA40
388. Applying Resampling.mp4 [d9bbe6f4125c6518] 59,719,569 55A2968E
389. Applying Logistic Regression.mp4 [1db69df8b910ee40] 54,923,222 CE7F849C
390. Applying Gradient Boosting.mp4 [b51d33df5a3e4850] 40,482,202 4D1D1749
391. Summary.mp4 [140ae7b0a9717f] 46,317,936 C33B2D44
392. Setting up the Environment.mp4 [2456687c63b66d7a] 48,687,057 E84D48FE
393. Understanding the Dataset.mp4 [f1d44bdbbb21b6f8] 57,864,348 BAF73203
394. Understanding the Problem Statement.mp4 [83cffe9a01488e1a] 37,106,008 BCD2D8EF
395. Performing Descriptive Statistics.mp4 [a6233e1f8232dac1] 77,145,502 76909D9C
396. Analyzing Agricultural Conditions.mp4 [1a1b47d66f99ec22] 41,068,067 07358A24
397. Clustering Similar Crops.mp4 [5b13ad3cb40f6148] 66,697,620 E2022371
398. Visualizing the Hidden Patterns.mp4 [da0fe999c8673bf] 29,125,491 F3206276
399. Predictive Modelling.mp4 [db41c1c4453d965d] 42,329,245 C9A19C9E
400. Real Time Predictions.mp4 [c51a7535493bb06b] 28,994,458 ADE9FF4C
401. Summarizing the Key-Points.mp4 [1d2da6676e1c4ce7] 42,406,921 425AD5D7
402. Understanding the Problem Statement.mp4 [4f8863e1f85eefe4] 61,608,713 779555EE
403. Setting up the Environment.mp4 [65c6f8038dff9f8d] 34,585,878 CB9FDF11
404. Data Analysis and Visualization.mp4 [1d0fc3ab8610876b] 81,498,243 C6EF98E1
405. KMeans Clustering Analysis.mp4 [61e05977bdd0ddfb] 64,775,068 5119BC64
406. Applying Hierarchical Clustering.mp4 [7f2e505d7a35a4d1] 42,764,273 44FF04D1
407. Using Silhouette Score as Evaluation Metric.mp4 [d277a26f9a533cac] 38,492,113 2CD133EC
409. Major Learnings from the projects.mp4 [480c6bf2fe2765bb] 42,777,512 71B0F94F
410. Conclusion.mp4 [a2cfd4d13d959ab1] 47,658,771 30565E01
1. Why should you learn Python.mp4 [26f42b7dfe868173] 135,238,685 E2F8361F
2. Installing Python and Jupyter Notebook.mp4 [a55a84c8f56d113b] 99,944,238 83E0E524
3. Understanding the Interface of Jupyter Notebook.mp4 [48caf365350275a4] 73,739,706 8007B088
4. Naming Convention for Variables.mp4 [fe8964ee602fc45b] 99,544,360 BE9C07E4
5. Built in Data Types and Type Casting.mp4 [d6b0555128681b73] 144,218,217 3B62D8AC
6. Scope of Variables.mp4 [cb886c831c84302] 69,712,890 F9352D4C
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Execution time: 0.1449 - Memory usage: 1.6MB
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srrDB is a historical record of the warez scene. We do not offer illegal downloads nor links to these works. All meta data gets added by our awesome users. Photographs or pictures can be part of these historical records, deemed fair use (news reporting and research) and are only a part of the complete work, but copyrights are owned by their respective creators or right holders.