|
RAR-files |
| packt.data.science.and.data.preparation.with.knime-xqzt.rar |
400,000,000 |
13CCC538 |
| packt.data.science.and.data.preparation.with.knime-xqzt.r00 |
400,000,000 |
E93352FC |
| packt.data.science.and.data.preparation.with.knime-xqzt.r01 |
400,000,000 |
1F31AE9A |
| packt.data.science.and.data.preparation.with.knime-xqzt.r02 |
400,000,000 |
34A9C15D |
| packt.data.science.and.data.preparation.with.knime-xqzt.r03 |
400,000,000 |
607A38F9 |
| packt.data.science.and.data.preparation.with.knime-xqzt.r04 |
400,000,000 |
92BEAAAD |
| packt.data.science.and.data.preparation.with.knime-xqzt.r05 |
400,000,000 |
0E580B76 |
| packt.data.science.and.data.preparation.with.knime-xqzt.r06 |
400,000,000 |
178C355F |
| packt.data.science.and.data.preparation.with.knime-xqzt.r07 |
400,000,000 |
4131D589 |
| packt.data.science.and.data.preparation.with.knime-xqzt.r08 |
295,927,185 |
F4300E8A |
|
| Total size: |
3,895,927,185 |
|
|
| Archived
files |
|
01.01-course_introduction.mkv
[63010cc01a0168]
|
12,594,042 |
35723C5B |
|
01.02-reading_multiple_csv_files_in_bulk_into_knime_update.mkv
[cee37ecd7518de61]
|
407,582,866 |
63A68A4F |
|
01.03-reading_multiple_excel_files_in_bulk_into_knime_update.mkv
[86872c6e7735ee0f]
|
249,733,929 |
3977D15D |
|
01.04-a_great_helper_node_for_time_series_analysis_in_knime.mkv
[6775a76049eda8af]
|
125,993,609 |
43C01764 |
|
01.05-examples_of_how_to_use_loops_in_knime.mkv
[52c515f7e5eee68d]
|
91,898,351 |
F6ECAB74 |
|
01.06-more_on_loops_in_knime-several_ways_to_get_the_same_result.mkv
[85b12029310ca28c]
|
85,031,571 |
9E19D1C9 |
|
01.07-loops-how_to_split_data_into_multiple_output_files.mkv
[8ed171c6be745e2e]
|
201,225,787 |
8FB95FCC |
|
01.08-loops_recursion_in_knime.mkv
[45366c0473801336]
|
210,893,517 |
42BC7A73 |
|
01.09-webscraping_with_knime.mkv
[a55e7dd3b1f9a0d2]
|
220,582,154 |
1F451980 |
|
01.10-webscraping_with_knime-financial_data.mkv
[502ccd01248bda47]
|
268,571,262 |
E1115406 |
|
01.11-scripting-how_to_use_python_in_knime.mkv
[f8650c69250e38b5]
|
138,413,980 |
E682FE05 |
|
01.12-python_in_knime-further_examples.mkv
[c8e69dddbe60f98b]
|
145,230,370 |
89023DDE |
|
01.13-hyperparameter_optimization_in_knime-data_preparation.mkv
[5fc78e62816a47f2]
|
150,565,957 |
5BB7D623 |
|
01.14-hyperparameter_optimization_for_machine_learning_models_using_loops_in_knime.mkv
[9b5738c8d91a7f17]
|
261,812,559 |
63B9F061 |
|
01.15-feature_selection_in_knime.mkv
[4ffd90c9ca8f48d7]
|
239,682,657 |
FF9EC9D6 |
|
01.16-machine_learning_prediction_process.mkv
[cf44447410bdb602]
|
163,372,847 |
E90A9403 |
|
01.17-knime_logout.mkv
[c3aff0d990a89988]
|
3,684,837 |
64B75A06 |
|
02.01-reading_multiple_csv_files_in_bulk_into_knime.mkv
[9802972a9dae271]
|
318,755,400 |
EC4A418B |
|
02.02-read_multiple_excel_sheets_in_bulk_into_knime.mkv
[bcbd75a2c46b0298]
|
238,046,986 |
E785E04D |
|
02.03-loops_how_to_split_data_into_multiple_output_files.mkv
[6f9d3a9ea033a4f7]
|
194,189,567 |
53CC02D4 |
|
02.04-python_in_knime-further_examples.mkv
[91e34a966c40a047]
|
127,664,551 |
E9E61061 |
|
9781801073288_Code.zip |
40,397,301 |
5A229790 |
|
| Total size: |
3,895,924,100 |
|
|
