Archived
files |
272-ANN_in_R-Step_2.mkv
[f438764a0a2b9941]
|
45,812,987 |
8E574D0E |
283-Step_4-Full_Connection.mkv
[c9829c69b5ddff1d]
|
103,862,028 |
10EB2987 |
278-What_are_convolutional_neural_networks.mkv
[738265db4bca5d44]
|
109,107,391 |
DF1D1C62 |
279-Step_1-Convolution_Operation.mkv
[842a854abfc0b1e1]
|
96,264,873 |
4D1FF4FD |
112-Logistic_Regression_in_Python-Step_2.mkv
[27c1278d1b0d5e46]
|
74,883,220 |
F0492C65 |
255-What_is_Deep_Learning.mkv
[bab830253d311b3d]
|
186,995,469 |
8BA206FB |
318-XGBoost_in_R.mkv
[ef2aef9f42bf9473]
|
127,090,902 |
00176622 |
176-K-Means_Clustering_in_R.mkv
[d8aac01404926d26]
|
76,839,597 |
4202DD5C |
117-Logistic_Regression_in_Python-Step_7.mkv
[e578e9185823e903]
|
149,223,519 |
EC844417 |
274-ANN_in_R-Step_4_(Last_step).mkv
[988cd7859c634514]
|
100,823,517 |
25652DB2 |
290-CNN_in_Python-Step_4.mkv
[1e26f4c3e315d6e]
|
36,638,386 |
E3D5587A |
058-Multiple_Linear_Regression_in_Python-Step_3.mkv
[3d265c5eeb478310]
|
46,837,703 |
F0A6833E |
039-Simple_Linear_Regression_in_Python-Step_1.mkv
[498ec74f07ccf78c]
|
59,500,970 |
27227E0E |
232-Classical_vs_Deep_Learning_Models.mkv
[6474ce8d52856db2]
|
48,149,405 |
1C3156B7 |
246-Natural_Language_Processing_in_R-Step_4.mkv
[b9545d5b3a4552e9]
|
15,802,081 |
B1204E49 |
115-Logistic_Regression_in_Python-Step_5.mkv
[b21da1c3ccd7689a]
|
24,295,051 |
C24554FB |
250-Natural_Language_Processing_in_R-Step_8.mkv
[5fe766e21df71814]
|
29,776,785 |
B6E65E02 |
295-Principal_Component_Analysis_(PCA)_Intuition.mkv
[267e31f4735311ad]
|
29,182,601 |
5149D019 |
066-Multiple_Linear_Regression_in_R-Backward_Elimination-Homework_Solution.mkv
[46daab7508a8e864]
|
59,352,424 |
7E219917 |
023-Splitting_the_dataset_into_the_Training_set_and_Test_set.mkv
[71a6e9ed3988ca44]
|
49,159,713 |
A51F0EC9 |
085-SVR_in_Python-Step_4.mkv
[e7f0ef3259b7222f]
|
31,797,689 |
FB962A42 |
281-Step_2-Pooling.mkv
[8b2fdcf93b9a3be0]
|
146,442,461 |
C62A2CB1 |
257-The_Neuron.mkv
[7518ff3d8f9763bf]
|
67,115,699 |
7992926B |
118-Logistic_Regression_in_R-Step_1.mkv
[70bd1465b539494a]
|
34,164,419 |
EE9F32F3 |
078-R_Regression_Template.mkv
[a8ff0a73b8476fe1]
|
49,316,262 |
355DF4CE |
289-CNN_in_Python-Step_3.mkv
[842a05a160373470]
|
106,623,711 |
5724AE3C |
266-ANN_in_Python-Step_1.mkv
[ab77487b50ac10ee]
|
85,128,109 |
8B549D33 |
136-Mapping_to_a_higher_dimension.mkv
[9620364c4dff6ed1]
|
23,584,488 |
A5EFC135 |
070-Polynomial_Regression_in_Python-Step_1.mkv
[39524cc2e0708d4f]
|
48,546,954 |
11F4DC1F |
144-Naive_Bayes_Intuition.mkv
[6351ebb6f8c95a01]
|
34,269,043 |
A826E8A6 |
238-Natural_Language_Processing_in_Python-Step_4.mkv
[4928d7d2449e2fb7]
|
57,988,625 |
7859D1E3 |
292-CNN_in_Python-FINAL_DEMO.mkv
[a67b8446bd826f84]
|
195,417,846 |
BA40E604 |
048-Dataset_and_Business_Problem_Description.mkv
[d25e0c657d41fc5c]
|
25,190,376 |
9BF09BFA |
203-Eclat_in_R.mkv
[5fc4c0c119b13b56]
|
115,632,863 |
446227CD |
131-SVM_Intuition.mkv
[c3bd58e142a24d40]
|
28,124,311 |
6B091FB3 |
280-Step_1(b)-ReLU_Layer.mkv
[a4a8fc56d7163ca0]
|
32,651,975 |
91D4D905 |
273-ANN_in_R-Step_3.mkv
[905fce79fc07cd0]
|
212,741,669 |
E35A922E |
305-LDA_in_R.mkv
[36d33843c263edd]
|
173,592,376 |
F28E4558 |
268-ANN_in_Python-Step_3.mkv
[4ac6a43df4ec46a8]
|
62,175,047 |
D28BC217 |
087-SVR_in_R.mkv
[3a1f2122249cd58b]
|
52,918,440 |
F88E438F |
235-Natural_Language_Processing_in_Python-Step_1.mkv
[fce0649e889cf0ae]
|
23,604,724 |
F4A9000F |
033-Feature_Scaling.mkv
[873bd6247fb57e2f]
|
93,186,818 |
12C7441B |
162-Accuracy_Paradox.mkv
[2ee1c4a0bc180226]
|
6,211,814 |
066A3E01 |
213-Upper_Confidence_Bound_in_Python-Step_6.mkv
[d5c61df4627761ac]
|
31,298,012 |
9022795D |
127-K-Nearest_Neighbor_Intuition.mkv
[7c32e74f7e17944f]
|
14,132,797 |
3F512313 |
270-ANN_in_Python-Step_5.mkv
[4a8940cddb8ee05c]
|
125,726,636 |
0BD5D71B |
156-Random_Forest_Classification_in_Python.mkv
[ac7e6e29b9f0ba2f]
|
121,786,269 |
197E91E6 |
233-Bag-Of-Words_Model.mkv
[3d2a322c1cadf2ed]
|
62,460,414 |
B52ADBF9 |
141-Kernel_SVM_in_Python.mkv
[bde424ceae32b60f]
|
77,254,436 |
BBA6BD1D |
018-Importing_the_Libraries.mkv
[c7c8e906792cefd6]
|
11,668,062 |
ECBDD21F |
099-R-Squared_Intuition.mkv
[b35d326e110cfca5]
|
14,294,534 |
0AC2767F |
188-Hierarchical_Clustering_in_R-Step_5.mkv
[52682b8ea5e10158]
|
27,006,450 |
AE2388BE |
080-Heads-up_on_non-linear_SVR.mkv
[1ade896deb5ca85f]
|
15,084,658 |
8BF4C805 |
195-Apriori_in_Python-Step_3.mkv
[2bf6038ab183905b]
|
61,628,591 |
6BB5F0B4 |
261-Gradient_Descent.mkv
[3d2626183eac4bf4]
|
40,278,671 |
FF187535 |
288-CNN_in_Python-Step_2.mkv
[437e240b0ea334ea]
|
183,140,505 |
B77854A5 |
050-Multiple_Linear_Regression_Intuition-Step_2.mkv
[e0f305882645c6c4]
|
2,639,706 |
767CC17A |
032-Splitting_the_dataset_into_the_Training_set_and_Test_set.mkv
[880c70bab3802faf]
|
106,828,689 |
E0BCA53B |
103-THE_ULTIMATE_DEMO_OF_THE_POWERFUL_REGRESSION_CODE_TEMPLATES_IN_ACTION.mkv
[7bfdfab1cbb4554c]
|
35,067,307 |
865685F9 |
068-Polynomial_Regression_Intuition.mkv
[91b3f1fef33e5584]
|
13,592,979 |
047F2F7F |
260-How_do_Neural_Networks_learn.mkv
[2b831fc8a7089dd5]
|
69,897,312 |
1BD4AE48 |
137-The_Kernel_Trick.mkv
[21247848b0a2033a]
|
55,415,149 |
993D3AA3 |
302-Linear_Discriminant_Analysis_(LDA)_Intuition.mkv
[6e200273187f477d]
|
24,774,248 |
3F8CFFDD |
042-Simple_Linear_Regression_in_Python-Step_4.mkv
[5aadac57ab25b317]
|
59,433,465 |
7595B04F |
159-THE_ULTIMATE_DEMO_OF_THE_POWERFUL_CLASSIFICATION_CODE_TEMPLATES_IN_ACTION.mkv
[817e333c782a2eda]
|
169,427,640 |
84419DC0 |
113-Logistic_Regression_in_Python-Step_3.mkv
[b8498b79b38f32ea]
|
38,714,758 |
04BDBA16 |
167-K-Means_Clustering_Intuition.mkv
[391e183558b3ca91]
|
42,663,158 |
D3B7F81D |
319-THANK_YOU_Additional_Video.mkv
[f30e9fccd0139352]
|
111,407,770 |
C045034C |
199-Apriori_in_R-Step_3.mkv
[653a4fe4b597591a]
|
282,378,851 |
7BA93E28 |
211-Upper_Confidence_Bound_in_Python-Step_4.mkv
[c33c59fdd77d955b]
|
67,130,565 |
57B2067E |
053-Understanding_the_P-Value.mkv
[c4fd0443ab9d3a0b]
|
35,867,278 |
5E790D6E |
300-PCA_in_R-Step_2.mkv
[b2f4bac272960e3a]
|
86,574,180 |
448A8013 |
299-PCA_in_R-Step_1.mkv
[fd2f6b99f5ea7d00]
|
184,515,961 |
F1F076D6 |
222-Thompson_Sampling_in_Python-Step_1.mkv
[412e6ddbd07e9a69]
|
20,512,232 |
00E50FCC |
074-Polynomial_Regression_in_R-Step_1.mkv
[c568396e0222f29e]
|
44,928,128 |
1B8356AE |
034-Data_Preprocessing_Template.mkv
[7ed33278edc47825]
|
39,760,149 |
17441F9E |
153-Decision_Tree_Classification_in_R.mkv
[52bfdb86928bab7c]
|
346,004,797 |
2579DC84 |
237-Natural_Language_Processing_in_Python-Step_3.mkv
[aa86ad01bf570d0e]
|
44,713,823 |
28415C85 |
028-Dataset_Description.mkv
[8c7f9d30e6680c2d]
|
10,081,113 |
4B21CE7E |
148-Naive_Bayes_in_Python.mkv
[4acffe56e9d7bca2]
|
74,926,424 |
696CD69B |
024-Feature_Scaling.mkv
[36016415d5a6638]
|
83,158,475 |
08B91C0C |
215-Upper_Confidence_Bound_in_R-Step_1.mkv
[64ffa45e4422ce7e]
|
59,577,438 |
61DCF58D |
029-Importing_the_Dataset.mkv
[e48a8a05a8db0c2b]
|
11,859,825 |
3FE80458 |
304-LDA_in_Python.mkv
[65017d0e7aacebf8]
|
128,950,691 |
9204E0AD |
010-Presentation_of_the_ML_A-Z_folder_Colaboratory_Jupyter_Notebook_and_Spyder.mkv
[4397fd34d83f928]
|
87,973,551 |
80778A81 |
054-Multiple_Linear_Regression_Intuition-Step_5.mkv
[58a9a9e14148e32e]
|
50,714,414 |
BC3B090E |
277-Plan_of_attack.mkv
[de2985e57d0bdf7b]
|
9,422,825 |
ED1E5B15 |
217-Upper_Confidence_Bound_in_R-Step_3.mkv
[f32a25674d170895]
|
172,958,277 |
DFECA25B |
094-Decision_Tree_Regression_in_R.mkv
[82831e0698593e0a]
|
108,226,348 |
42696104 |
282-Step_3-Flattening.mkv
[a82aea879d5946bc]
|
4,475,138 |
DE2860B8 |
179-Hierarchical_Clustering_Using_Dendrograms.mkv
[f7e4a97abab0d7c6]
|
43,074,384 |
1DF4AAF2 |
072-Polynomial_Regression_in_Python-Step_3.mkv
[615be9133e74380b]
|
62,629,962 |
28905281 |
114-Logistic_Regression_in_Python-Step_4.mkv
[d4d636e659a60411]
|
41,412,334 |
322AC778 |
307-Kernel_PCA_in_Python.mkv
[be726ef8ed738baa]
|
97,910,899 |
D39B412A |
098-Random_Forest_Regression_in_R.mkv
[474b6dafa8d1de49]
|
109,090,994 |
F7A6118A |
298-PCA_in_Python-Step_2.mkv
[822a3f9124628471]
|
34,923,702 |
7A8B2204 |
064-Multiple_Linear_Regression_in_R-Step_3.mkv
[e35ea69a5cf29e9]
|
25,922,446 |
274A4261 |
116-Logistic_Regression_in_Python-Step_6.mkv
[c81107797ec0250d]
|
47,527,468 |
BD66EA15 |
082-SVR_in_Python-Step_1.mkv
[6241673d3ce977ac]
|
30,222,732 |
AF98A9CD |
130-K-NN_in_R.mkv
[341c6ce00f2b60f4]
|
136,439,891 |
B6AA1C22 |
031-Encoding_Categorical_Data.mkv
[eac3ecdcf90a3af8]
|
39,089,241 |
AD8454CD |
146-Naive_Bayes_Intuition_(Extras).mkv
[f64cdffd2b7f5373]
|
25,321,950 |
56D490E3 |
312-Grid_Search_in_Python.mkv
[9ed64cf21258e8fa]
|
197,075,080 |
30211A04 |
084-SVR_in_Python-Step_3.mkv
[faa015a2025bdaa6]
|
31,194,810 |
0C937071 |
supplemental_assets\Section 40 - Convolutional Neural Networks (CNN).zip |
234,921,658 |
9FA2C7F7 |
supplemental_assets\Eclat.zip |
49,709 |
305A97AE |
supplemental_assets\Machine Learning A-Z (Codes and Datasets).zip |
5,521,672 |
F4608F88 |
supplemental_assets\Machine_Learning_A_Z_Q_A.pdf |
2,367,210 |
EB4EA81B |
supplemental_assets\Machine Learning A-Z (Model Selection).zip |
163,850 |
C9B34B37 |
supplemental_assets\Clustering-Pros-Cons.pdf |
26,379 |
FE6166BC |
supplemental_assets\SVM.zip |
8,464 |
73C0411E |
supplemental_assets\Classification_Pros_Cons.pdf |
29,953 |
AB067410 |
supplemental_assets\Regression_Bonus.zip |
373,237 |
A3A12A55 |
150-Decision_Tree_Classification_Intuition.mkv
[37efbefd2623bfce]
|
24,932,064 |
5D59AEFF |
200-Eclat_Intuition.mkv
[9d07311a38ae4e9b]
|
37,085,166 |
1348D7DF |
149-Naive_Bayes_in_R.mkv
[835285126cb97860]
|
131,540,413 |
78DAAED7 |
219-Thompson_Sampling_Intuition.mkv
[d551ad8e1c141547]
|
72,752,582 |
990C7CBD |
316-XGBoost_in_Python.mkv
[38662fc13b40a688]
|
149,666,222 |
588E6701 |
076-Polynomial_Regression_in_R-Step_3.mkv
[962ba65e74885680]
|
111,173,380 |
4B1E6053 |
120-Logistic_Regression_in_R-Step_3.mkv
[2d768d4bb89bb177]
|
51,332,844 |
FC8E8A97 |
216-Upper_Confidence_Bound_in_R-Step_2.mkv
[f0d2aed21d77a107]
|
127,761,106 |
0752F14C |
313-k-Fold_Cross_Validation_in_R.mkv
[4703d23b1157d54c]
|
95,001,564 |
E2A2417A |
124-R_Classification_Template.mkv
[e2020c477e24bf60]
|
52,953,459 |
32FECEAD |
135-Kernel_SVM_Intuition.mkv
[a688c1c6e1077e4f]
|
9,618,713 |
2BB5C00D |
227-Thompson_Sampling_in_R-Step_1.mkv
[13ee0f02b6851913]
|
103,845,567 |
6905034D |
267-ANN_in_Python-Step_2.mkv
[165ac4beefbbdc14]
|
140,869,818 |
20F173A5 |
063-Multiple_Linear_Regression_in_R-Step_2.mkv
[527e4050265c3fe2]
|
96,080,809 |
B2A6F1E2 |
314-Grid_Search_in_R.mkv
[6791c938d8649261]
|
90,260,849 |
4351FFCD |
256-Plan_of_attack.mkv
[fe0930251a4c2ad9]
|
7,181,235 |
ACEF6D6A |
102-Preparation_of_the_Regression_Code_Templates.mkv
[cb9e7866230f279a]
|
212,595,378 |
C9F7B161 |
095-Random_Forest_Regression_Intuition.mkv
[97b4b483884cea60]
|
56,633,355 |
B9C6FFC4 |
185-Hierarchical_Clustering_in_R-Step_2.mkv
[918ff869fa01dee3]
|
22,107,630 |
FDC22901 |
083-SVR_in_Python-Step_2.mkv
[eb2c67e8c30d086f]
|
111,273,139 |
313D23F0 |
111-Logistic_Regression_in_Python-Step_1.mkv
[d294e35f39600d7b]
|
31,860,109 |
D9B03FE3 |
088-Decision_Tree_Regression_Intuition.mkv
[6fb463dabbaf165]
|
31,214,545 |
9910E8B7 |
258-The_Activation_Function.mkv
[3d22007691839e0d]
|
25,386,867 |
F82E3A68 |
269-ANN_in_Python-Step_4.mkv
[56917ca1f4831c7c]
|
51,066,997 |
5A8568F2 |
057-Multiple_Linear_Regression_in_Python-Step_2.mkv
[ac90df4a5b27e46a]
|
104,601,213 |
11549C23 |
011-Installing_R_and_R_Studio_(Mac_Linux_and_Windows).mkv
[c2beea80375e30]
|
42,202,716 |
9C06B996 |
109-Logistic_Regression_Intuition.mkv
[b959cfc72f9b15d3]
|
47,382,693 |
145F08F9 |
164-CAP_Curve_Analysis.mkv
[34eb2a89f230f20c]
|
22,207,082 |
95939E1C |
308-Kernel_PCA_in_R.mkv
[d71abf04e9485546]
|
419,012,763 |
BE27049B |
244-Natural_Language_Processing_in_R-Step_2.mkv
[4037d4c27cdf765b]
|
42,126,563 |
6E7E73E5 |
021-Taking_care_of_Missing_Data.mkv
[2056b09872d71bc7]
|
84,024,487 |
FAE9A2A1 |
041-Simple_Linear_Regression_in_Python-Step_3.mkv
[8b84b74d6f8f6a54]
|
35,512,958 |
6AA4C172 |
186-Hierarchical_Clustering_in_R-Step_3.mkv
[88f1c1d4f6955c27]
|
23,069,259 |
73D89BC0 |
251-Natural_Language_Processing_in_R-Step_9.mkv
[36aff7df4135356a]
|
70,926,139 |
7A979D8F |
291-CNN_in_Python-Step_5.mkv
[aa40fd74d8f857ca]
|
147,140,364 |
ED4DA429 |
264-Business_Problem_Description.mkv
[eb6653d32c6124a0]
|
71,835,465 |
2DF2B688 |
129-K-NN_in_Python.mkv
[9a626003b5a7f94d]
|
185,338,165 |
F979392E |
252-Natural_Language_Processing_in_R-Step_10.mkv
[5de38033ce2a9075]
|
122,247,820 |
B74E4A30 |
019-Importing_the_Dataset.mkv
[97178fc56ce433fa]
|
42,959,983 |
A723E2BD |
049-Multiple_Linear_Regression_Intuition-Step_1.mkv
[bf384b6d6f1e90ee]
|
2,807,610 |
F86174DF |
105-Evaluating_Regression_Models_Performance-Homeworks_Final_Part.mkv
[a894c8ebafec9ff]
|
48,697,714 |
A38BC740 |
145-Naive_Bayes_Intuition_(Challenge_Reveal).mkv
[aff81b132cbbc2f7]
|
19,065,771 |
02BFF992 |
152-Decision_Tree_Classification_in_Python.mkv
[31181a6322554797]
|
135,293,349 |
BB1C3094 |
220-Algorithm_Comparison_UCB_vs_Thompson_Sampling.mkv
[ad6958cad1b3b0b6]
|
25,079,771 |
877DD458 |
026-Getting_Started.mkv
[f64bd2450e0eb20c]
|
6,903,874 |
26815051 |
202-Eclat_in_Python.mkv
[db1f66e8e2e3ebed]
|
95,915,515 |
0EBEF855 |
284-Summary.mkv
[868cc2cdfe854ebf]
|
17,470,198 |
9AF494AF |
143-Bayes_Theorem.mkv
[e54ac5d08c42fdef]
|
96,641,531 |
F7B326F6 |
073-Polynomial_Regression_in_Python-Step_4.mkv
[5a7a1867ebe9fc3b]
|
31,771,458 |
31DF8CAB |
092-Decision_Tree_Regression_in_Python-Step_3.mkv
[381f36432c35d32f]
|
13,557,458 |
01ECBC75 |
091-Decision_Tree_Regression_in_Python-Step_2.mkv
[84af2c7267033042]
|
18,824,991 |
325ABF3C |
183-Hierarchical_Clustering_in_Python-Step_3.mkv
[75a64a3a7de3cb0e]
|
96,541,391 |
11A8466B |
163-CAP_Curve.mkv
[45db76915b53ee9]
|
27,031,638 |
65431CFA |
184-Hierarchical_Clustering_in_R-Step_1.mkv
[59466d4f4d7026ca]
|
12,880,992 |
349AA081 |
208-Upper_Confidence_Bound_in_Python-Step_1.mkv
[a9b0304faa92780]
|
69,681,431 |
A4469F11 |
157-Random_Forest_Classification_in_R.mkv
[fd6ffe77db8aaad5]
|
234,109,425 |
C503C1DD |
212-Upper_Confidence_Bound_in_Python-Step_5.mkv
[bf4a75dac9d2aeff]
|
27,200,128 |
6A579257 |
225-Thompson_Sampling_in_Python-Step_4.mkv
[1bae1c74a321f45b]
|
33,928,713 |
1209A9DD |
197-Apriori_in_R-Step_1.mkv
[6627498828d1ef02]
|
129,430,493 |
CC06DA99 |
045-Simple_Linear_Regression_in_R-Step_2.mkv
[b4372692e90410c2]
|
34,903,312 |
CD3B94D7 |
177-Hierarchical_Clustering_Intuition.mkv
[2fce697bc6c0f654]
|
26,032,764 |
04B55427 |
262-Stochastic_Gradient_Descent.mkv
[2013d63c1e9f37ee]
|
43,732,378 |
C615B4E7 |
271-ANN_in_R-Step_1.mkv
[8fca148b040e815]
|
238,952,396 |
30BFA246 |
206-Upper_Confidence_Bound_(UCB)_Intuition.mkv
[4642d3ff1cf1ed06]
|
90,629,748 |
ADCBB398 |
075-Polynomial_Regression_in_R-Step_2.mkv
[bd0602e40ba0dcc9]
|
74,591,064 |
B8D727AA |
243-Natural_Language_Processing_in_R-Step_1.mkv
[e96603768a554e8d]
|
88,369,049 |
3F657093 |
051-Multiple_Linear_Regression_Intuition-Step_3.mkv
[ffda86d77d7c986a]
|
31,810,191 |
A9BCB44A |
245-Natural_Language_Processing_in_R-Step_3.mkv
[65505deda9a050f9]
|
33,460,577 |
96397895 |
173-K-Means_Clustering_in_Python-Step_3.mkv
[cdaf5d9cd6291c02]
|
59,231,485 |
6755100A |
224-Thompson_Sampling_in_Python-Step_3.mkv
[8b8c87affb506621]
|
65,364,635 |
39D8FBE3 |
218-Upper_Confidence_Bound_in_R-Step_4.mkv
[13e4c91aae7f6ba4]
|
14,662,297 |
EE5C21E3 |
263-Backpropagation.mkv
[387d92689fb1e186]
|
24,072,455 |
BEB8E4D3 |
175-K-Means_Clustering_in_Python-Step_5.mkv
[74475358ca5a8578]
|
97,941,408 |
81DD83A6 |
161-Confusion_Matrix.mkv
[42d2aa3aa7e5025a]
|
11,546,125 |
551FC189 |
178-Hierarchical_Clustering_How_Dendrograms_Work.mkv
[fd5b3f501c9380e9]
|
27,296,892 |
D88EA93F |
154-Random_Forest_Classification_Intuition.mkv
[f831cc4584332459]
|
71,365,705 |
4237F235 |
017-Getting_Started.mkv
[e5b56fd90a594123]
|
68,056,940 |
082E7C82 |
059-Multiple_Linear_Regression_in_Python-Step_4.mkv
[2624fa5f45870589]
|
67,212,310 |
CB1ABF0A |
044-Simple_Linear_Regression_in_R-Step_1.mkv
[52aa8d2960b9e635]
|
19,312,072 |
5496EACB |
249-Natural_Language_Processing_in_R-Step_7.mkv
[79694b0fd9fcc021]
|
18,700,747 |
2919C868 |
171-K-Means_Clustering_in_Python-Step_1.mkv
[6cce0a42c3ec2949]
|
27,297,926 |
D0577040 |
231-Types_of_Natural_Language_Processing.mkv
[b0d0e91111d3c105]
|
12,764,980 |
C4448CBE |
297-PCA_in_Python-Step_1.mkv
[9a600264463ca619]
|
143,222,971 |
895F62A1 |
169-K-Means_Selecting_The_Number_Of_Clusters.mkv
[f48562a38e2bd297]
|
40,802,068 |
370A9BAB |
047-Simple_Linear_Regression_in_R-Step_4.mkv
[ac60a5aec6674803]
|
108,266,809 |
F1D97406 |
119-Logistic_Regression_in_R-Step_2.mkv
[9e603a25c9993500]
|
23,895,510 |
897AF990 |
285-Softmax_and_Cross-Entropy.mkv
[be39118d80f398c4]
|
67,116,642 |
F904848C |
133-SVM_in_Python.mkv
[b97bebef2929068]
|
160,624,794 |
AEF48104 |
062-Multiple_Linear_Regression_in_R-Step_1.mkv
[16104fe14b1a965d]
|
39,774,959 |
68667994 |
248-Natural_Language_Processing_in_R-Step_6.mkv
[7f040f421dcb0d8b]
|
31,032,045 |
4D016453 |
209-Upper_Confidence_Bound_in_Python-Step_2.mkv
[3338eb5096dc0e1e]
|
14,402,327 |
397A3DA8 |
287-CNN_in_Python-Step_1.mkv
[45360620d9dcefe6]
|
53,356,934 |
1140F1AA |
193-Apriori_in_Python-Step_1.mkv
[a05f116326bffeb6]
|
106,754,695 |
8302EACE |
040-Simple_Linear_Regression_in_Python-Step_2.mkv
[f27e77952dc36a41]
|
31,516,502 |
28554394 |
174-K-Means_Clustering_in_Python-Step_4.mkv
[c58b6a046d7ecfa8]
|
28,560,983 |
76C674C4 |
239-Natural_Language_Processing_in_Python-Step_5.mkv
[21e86faf449fd911]
|
143,687,046 |
505C2045 |
228-Thompson_Sampling_in_R-Step_2.mkv
[78484d3a50bc1d9e]
|
17,271,891 |
9EA500F8 |
240-Natural_Language_Processing_in_Python-Step_6.mkv
[cc8d55c65c113a22]
|
77,626,773 |
B31E0FF2 |
006-Why_Machine_Learning_is_the_Future.mkv
[2c18523eb9b5aa98]
|
31,197,745 |
F23AFB57 |
196-Apriori_in_Python-Step_4.mkv
[6e2899f7d6f4f162]
|
202,063,181 |
43E1E912 |
001-Applications_of_Machine_Learning.mkv
[6e128572ec95c35a]
|
35,845,849 |
5679200F |
198-Apriori_in_R-Step_2.mkv
[dfc841807e7634b]
|
170,017,125 |
03F913E0 |
030-Taking_care_of_Missing_Data.mkv
[d0f6b5c6135ff883]
|
35,937,275 |
AFA9F1A2 |
065-Multiple_Linear_Regression_in_R-Backward_Elimination-HOMEWORK_.mkv
[e3e5605a5f7fffb4]
|
115,512,253 |
4DF12F13 |
071-Polynomial_Regression_in_Python-Step_2.mkv
[d56afe1ebbb6a021]
|
54,788,405 |
DD772FB8 |
046-Simple_Linear_Regression_in_R-Step_3.mkv
[d60f67223f0a5f86]
|
27,200,079 |
634F918A |
168-K-Means_Random_Initialization_Trap.mkv
[8db5ff7d537a258e]
|
25,798,059 |
34A9AC83 |
181-Hierarchical_Clustering_in_Python-Step_1.mkv
[c54639649b609885]
|
31,730,415 |
4FD73833 |
210-Upper_Confidence_Bound_in_Python-Step_3.mkv
[af657523a3a89910]
|
30,061,759 |
F658A33A |
052-Multiple_Linear_Regression_Intuition-Step_4.mkv
[6d5e5b078f177192]
|
25,920,223 |
665064D4 |
301-PCA_in_R-Step_3.mkv
[674a51a23d6a2ab9]
|
119,800,341 |
F4A73AFB |
236-Natural_Language_Processing_in_Python-Step_2.mkv
[242ae58e36f1847f]
|
61,250,437 |
7B355818 |
311-k-Fold_Cross_Validation_in_Python.mkv
[3cf3e566862625cf]
|
102,732,985 |
A45432E0 |
259-How_do_Neural_Networks_work.mkv
[bacc768d7592ba92]
|
52,487,234 |
397E28BF |
093-Decision_Tree_Regression_in_Python-Step_4.mkv
[c46cfce80cd27d9d]
|
50,837,761 |
4428680E |
230-NLP_Intuition.mkv
[859327a6fe8d5f5d]
|
7,954,805 |
2C42CF64 |
214-Upper_Confidence_Bound_in_Python-Step_7.mkv
[7354ff7e71307ae0]
|
33,396,889 |
959C93FA |
022-Encoding_Categorical_Data.mkv
[3c6c980897497fb2]
|
108,864,995 |
22EB0DEB |
187-Hierarchical_Clustering_in_R-Step_4.mkv
[b9aac4f1d02dbdd0]
|
35,649,492 |
B27DC347 |
172-K-Means_Clustering_in_Python-Step_2.mkv
[3606df235a6023eb]
|
50,861,727 |
3EA83EFC |
247-Natural_Language_Processing_in_R-Step_5.mkv
[a18d7aace887e79c]
|
10,997,162 |
AA25AC17 |
100-Adjusted_R-Squared_Intuition.mkv
[e1ddc28717d54b4]
|
28,396,652 |
40F26CED |
194-Apriori_in_Python-Step_2.mkv
[38e106c0232f16b1]
|
138,223,680 |
8DCF86E9 |
086-SVR_in_Python-Step_5.mkv
[2e3ef8e950377577]
|
88,612,463 |
C6831A46 |
139-Non-Linear_Kernel_SVR_(Advanced).mkv
[4d371041e53c87b1]
|
47,615,583 |
A1E632AA |
123-Logistic_Regression_in_R-Step_5.mkv
[a011e0c2107a35e1]
|
343,614,389 |
FE516D51 |
138-Types_of_Kernel_Functions.mkv
[97068ad0cf13dff9]
|
16,596,813 |
486C6D4F |
106-Interpreting_Linear_Regression_Coefficients.mkv
[53743077030ec5e]
|
91,200,915 |
2D9DCBAB |
077-Polynomial_Regression_in_R-Step_4.mkv
[7c6fc993310b076f]
|
64,760,604 |
D35FD6D8 |
036-Simple_Linear_Regression_Intuition-Step_1.mkv
[5aca680a0deb191d]
|
14,692,986 |
5F8C46C4 |
134-SVM_in_R.mkv
[95cdbf5ac9b82b52]
|
162,844,550 |
759D75D7 |
121-Logistic_Regression_in_R-Step_4.mkv
[9475b83df4a6bf17]
|
21,745,596 |
C18E00B8 |
205-The_Multi-Armed_Bandit_Problem.mkv
[32c24ab931e7cba1]
|
57,440,405 |
CEF34927 |
160-False_Positives_and_False_Negatives.mkv
[8ceae3ff9c0478d7]
|
28,100,729 |
6B2F677C |
191-Apriori_Intuition.mkv
[eb60c67b49f09811]
|
85,771,045 |
3A5B8661 |
056-Multiple_Linear_Regression_in_Python-Step_1.mkv
[2f1ec05c54efde5]
|
35,120,714 |
82A41DF3 |
079-SVR_Intuition_(Updated).mkv
[a8c73549b0c64b41]
|
26,698,994 |
051BFB96 |
182-Hierarchical_Clustering_in_Python-Step_2.mkv
[2bca0c0ef41ffe56]
|
93,161,888 |
04FBD6C5 |
142-Kernel_SVM_in_R.mkv
[c65138b597180340]
|
256,851,793 |
A8873505 |
037-Simple_Linear_Regression_Intuition-Step_2.mkv
[2d77a532511bc447]
|
9,232,044 |
3483E38D |
090-Decision_Tree_Regression_in_Python-Step_1.mkv
[b69fee39309a2305]
|
33,913,490 |
46779B50 |
223-Thompson_Sampling_in_Python-Step_2.mkv
[16f8308c5943969c]
|
54,616,576 |
A50C4E81 |
097-Random_Forest_Regression_in_Python.mkv
[33f154d8c3cc58cb]
|
97,616,534 |
AC7D0E69 |
supplemental_assets |
0 |
00000000 |
|
Total size: |
17,812,225,041 |
|
|