| Archived
files |
|
01-data_science_and_machine_learning_course_intro.mkv
[9077b13bf24d0d03]
|
15,335,413 |
D14E83FE |
|
02-data_science_and_machine_learning_marketplace.mkv
[1c9dea1943c2f505]
|
52,109,988 |
E606528D |
|
03-data_science_job_opportunities.mkv
[e7967bb0450eabd9]
|
32,770,776 |
3BED4DED |
|
04-data_science_job_roles.mkv
[fef885afa84330d]
|
86,597,065 |
924F0668 |
|
05-what_is_a_data_scientist.mkv
[99bc1a827842a70c]
|
139,210,293 |
68D026FB |
|
06-how_to_get_a_data_science_job.mkv
[af6ca7488949a95d]
|
145,367,907 |
0D0384E9 |
|
07-data_science_projects_overview.mkv
[30a8fcc6b3c7fa61]
|
89,021,915 |
5D712F8B |
|
08-why_we_use_python.mkv
[f6468ca9f4575330]
|
15,847,235 |
EDAB777A |
|
09-what_is_data_science.mkv
[f1f1b2b1cc48676a]
|
98,070,288 |
B442595A |
|
100-adaboost_part_1.mkv
[8cd50c8c3c0e7e12]
|
29,782,231 |
C17A6189 |
|
101-adaboost_part_2.mkv
[4567f5dad9eaf1f8]
|
100,398,415 |
48BCD7C8 |
|
102-svm_outline.mkv
[df05d7635f39ebfd]
|
44,157,914 |
B02637B4 |
|
103-svm_intuition.mkv
[203355e7d317313f]
|
67,286,571 |
60B86BB6 |
|
104-hard_vs_soft_margins.mkv
[a1309f24d713da10]
|
81,557,446 |
36316592 |
|
105-c_hyper-parameter.mkv
[4fac2c33666c240e]
|
25,558,368 |
B38B09D5 |
|
106-kernel_trick.mkv
[b3c943d60ade932d]
|
92,519,887 |
2A350A5B |
|
107-svm-kernel_types.mkv
[6bc4184eb517de06]
|
151,009,137 |
AA545B9E |
|
108-svm_with_linear_dataset_(iris).mkv
[512ef1c685e12ee1]
|
126,541,079 |
7F988D4C |
|
109-svm_with_non-linear_dataset.mkv
[ba5daf82c1d9b3a0]
|
135,114,598 |
37038832 |
|
10-what_is_machine_learning.mkv
[ab289e01bcc01a9]
|
93,088,715 |
B4BBA249 |
|
110-svm_with_regression.mkv
[cd45361bf6bb0290]
|
28,698,686 |
6081AA58 |
|
111-project_voice_gender_recognition_using_svm.mkv
[6aca82708e661e4a]
|
48,210,148 |
0D7BC6BC |
|
112-unsupervised_machine_learning_intro.mkv
[992bb702e71ac2f0]
|
118,434,546 |
858B6457 |
|
113-unsupervised_machine_learning_continued.mkv
[dc5f18078e942475]
|
96,036,504 |
13A040E4 |
|
114-data_standardization.mkv
[5ec65d8b9cce3ba9]
|
130,430,808 |
D29FA915 |
|
115-pca_section_overview.mkv
[1e010465d40ddd53]
|
37,037,211 |
4AA9F8DC |
|
116-what_is_pca.mkv
[2cd3207d83648c77]
|
58,797,792 |
DC2037CA |
|
117-covariance_matrix_vs_svd.mkv
[e357b4653d30c42b]
|
48,885,129 |
8FDB4F46 |
|
118-image_compression_scratch.mkv
[eb99ca77ac75e11b]
|
299,738,182 |
31991A48 |
|
119-data_preprocessing_scratch.mkv
[2ae2f9bad261ea62]
|
145,969,973 |
DCB7E19D |
|
11-machine_learning_concepts__and_algorithms.mkv
[6852192668b6d954]
|
88,747,779 |
6689CCA2 |
|
120-creating_a_data_science_resume.mkv
[45d9a8c0a68d1c2d]
|
42,492,173 |
17BF3043 |
|
121-data_science_cover_letter.mkv
[db3a82c4ed047f2e]
|
26,301,576 |
6B6AD8B2 |
|
122-how_to_contact_recruiters.mkv
[b15dcbd45660d037]
|
28,120,960 |
674F49B7 |
|
123-getting_started_with_freelancing.mkv
[92c4e3c4ddc37ca3]
|
33,651,000 |
52F55D2C |
|
124-top_freelance_websites.mkv
[a1ad1da5ecd8d934]
|
33,620,443 |
4050BB64 |
|
125-personal_branding.mkv
[85f7e16ed602c176]
|
33,590,899 |
13B2338A |
|
126-networking_dos_and_donts.mkv
[bbf12ec4253c1815]
|
25,904,429 |
E2609D48 |
|
127-importance_of_a_website.mkv
[461f8012282bb0aa]
|
17,284,286 |
86FB851B |
|
12-machine_learning_vs_deep_learning.mkv
[291142df25440d4e]
|
85,470,456 |
F254E7D5 |
|
13-what_is_deep_learning.mkv
[22aeb8828059e92b]
|
87,235,166 |
D7FCC4BE |
|
14-what_is_python_programming.mkv
[43fd0369bdda9844]
|
21,342,426 |
530A55F5 |
|
15-why_python_for_data_science.mkv
[f7d38d60d8a56e69]
|
19,034,604 |
25406092 |
|
16-what_is_jupyter.mkv
[81adc5be7ad6ceb7]
|
17,025,673 |
74C8E042 |
|
17-what_is_google_colab.mkv
[53a0e61c30e5a117]
|
9,113,020 |
3D24546D |
|
18-getting_started_with_colab.mkv
[52bea820681249ab]
|
45,194,319 |
E7BFCEC0 |
|
19-python_variables_and_booleans_.mkv
[5ad4eeff6843e20c]
|
44,478,977 |
FDAC1B92 |
|
20-python_operators.mkv
[9a080fd72cfee443]
|
102,108,874 |
0095C363 |
|
21-python_numbers_and_booleans.mkv
[ae338b4ac24b05cd]
|
29,895,831 |
284CDEDE |
|
22-python_strings.mkv
[b964f69ee2bfbf09]
|
68,185,728 |
9EC43E1E |
|
23-python_conditional_statements.mkv
[6ecf0393eb502410]
|
65,057,725 |
D2DE2CFD |
|
24-python_for_loops_and_while_loops.mkv
[3846055fce38cb64]
|
30,163,056 |
3B65DEE3 |
|
25-python_lists.mkv
[d0706876b23315d7]
|
25,872,475 |
BAE5847C |
|
26-more_about_python_lists.mkv
[a8bdfba2a4ad398b]
|
73,517,367 |
73ED02B4 |
|
27-python_tuples.mkv
[fdaf4d99772c458]
|
67,958,150 |
134BF19A |
|
28-python_dictionaries.mkv
[64e124d0e412690d]
|
123,555,928 |
58AB9AA1 |
|
29-python_sets.mkv
[e4ab06bd030cf45a]
|
34,540,736 |
B40D5C8C |
|
30-compound_data_types.mkv
[d663b0c9de054e36]
|
56,344,731 |
2E117EB5 |
|
31-python_object_oriented_programming.mkv
[f53338e5dbd1c549]
|
83,346,847 |
A188B0EF |
|
32-intro_to_statistics.mkv
[a595767bca104621]
|
20,401,836 |
C22297B8 |
|
33-descriptive_statistics.mkv
[971b746fdd3d1cbc]
|
20,737,192 |
750DBB25 |
|
34-measure_of_variability.mkv
[876292487c172399]
|
45,568,137 |
4883193A |
|
35-measure_of_variability_continued.mkv
[da9f712437a6d151]
|
40,077,498 |
330EB55C |
|
36-measures_of_variable_relationship.mkv
[f0e5a14c1ec8fd96]
|
29,144,737 |
3C70E74C |
|
37-inferential_statistics.mkv
[73eecf2f799d02ff]
|
53,028,139 |
47A8AB58 |
|
38-measures_of_asymmetry.mkv
[ce9c5bf8690103f5]
|
8,111,071 |
30B0D6EF |
|
39-sampling_distribution.mkv
[81528e6b61798453]
|
31,548,893 |
C3C25EA4 |
|
40-what_exactly_probability.mkv
[4d698b0b1990d9aa]
|
30,113,402 |
4AD89654 |
|
41-expected_values.mkv
[443b0076c925dc88]
|
16,123,118 |
B50940EC |
|
42-relative_frequency.mkv
[3fbbdb978494b739]
|
36,795,105 |
4DD41CB2 |
|
43-hypothesis_testing_overview.mkv
[fc93ac84df11d4eb]
|
68,675,607 |
DE58DBF2 |
|
44-numpy_array_data_types.mkv
[adaa8d9d83c22ebe]
|
39,522,461 |
AD7E4C47 |
|
45-numpy_arrays.mkv
[7424441e1d872126]
|
35,834,500 |
1ACA18D4 |
|
46-numpy_array_basics.mkv
[5cef9d9f41d5aa97]
|
45,601,110 |
65C8242E |
|
47-numpy_array_indexing.mkv
[7c069fd454c4be44]
|
38,990,759 |
8E6EF301 |
|
48-numpy_array_computations.mkv
[fe5b3b06ddd54ad3]
|
19,251,947 |
2FE5A279 |
|
49-broadcasting.mkv
[f44fc0fc9ed8394f]
|
20,332,318 |
3A0FB7BC |
|
50-intro_to_pandas.mkv
[b9649e271133d3e0]
|
53,166,069 |
C708BAB9 |
|
51-pandas_continued.mkv
[e2b4249db160e311]
|
81,135,397 |
B811F4CB |
|
52-data_visualization_overview.mkv
[514979658ccbbfa2]
|
88,458,564 |
808D0B0A |
|
53-different_data_visualization_libraries.mkv
[f83fb2c2a1ffe562]
|
48,519,710 |
232AE050 |
|
54-python_data_visualization_implementation.mkv
[5bc8a5bf4be9021c]
|
32,083,475 |
D2AB8AAC |
|
55-intro_to_machine_learning.mkv
[25045bcede84a1f3]
|
112,452,134 |
591781AD |
|
56-exploratory_data_analysis.mkv
[c65af5944ea63a1f]
|
60,443,661 |
0D56657B |
|
57-feature_scaling.mkv
[4cf414fbe5f9e2dc]
|
21,616,170 |
3B29146C |
|
58-data_cleaning.mkv
[699596e1df6548d5]
|
25,844,888 |
C48662CC |
|
59-feature_engineering.mkv
[1f5f096ed727bb22]
|
21,079,338 |
9B86B8AF |
|
60-linear_regression_intro.mkv
[9c13a05c3fe27230]
|
34,814,816 |
0B562F02 |
|
61-gradient_descent.mkv
[132cd2f89a095d98]
|
17,820,305 |
EA1F946D |
|
62-linear_regression_and_correlation_methods.mkv
[55f67a7c86fb9d70]
|
127,720,513 |
FC77E750 |
|
63-linear_regression_implementation.mkv
[96464bee566a5f96]
|
21,588,762 |
53EB51E3 |
|
64-logistic_regression.mkv
[f470ad89983091a3]
|
10,156,372 |
54E2B8A1 |
|
65-knn_overview.mkv
[ce50a28dcfadd6fc]
|
14,448,514 |
67770797 |
|
66-parametic_vs_non-parametic_models.mkv
[832bb4d2517ccb54]
|
18,010,857 |
254ECB43 |
|
67-eda_on_iris_dataset.mkv
[3d7b084acf0bfbb8]
|
193,199,413 |
D77F1111 |
|
68-knn_intuition.mkv
[26d9849324969262]
|
9,577,427 |
9BDBA053 |
|
69-implement_the_knn_algorithm_from_scratch.mkv
[9afbe79bc8b25e5]
|
104,383,385 |
7CCD549F |
|
70-compare_the_result_with_sklearn_library.mkv
[90f98bd854f2c976]
|
29,536,066 |
38A790BD |
|
71-knn_hyperparameter_tuning_using_the_cross-validation.mkv
[fd252e18318f0a31]
|
109,223,319 |
94DF89FD |
|
72-the_decision_boundary_visualization.mkv
[285a8a7358bf28bd]
|
19,603,435 |
20C659CF |
|
73-knn-manhattan_vs_euclidean_distance.mkv
[772b739bc63c3d23]
|
88,651,694 |
121589F9 |
|
74-knn_scaling.mkv
[a6cc3192b830b010]
|
59,972,025 |
F17EB1CF |
|
75-curse_of_dimensionality.mkv
[a980bbcc253c836a]
|
59,595,569 |
09770FF7 |
|
76-knn_use_cases.mkv
[7ab496d8d0758849]
|
34,799,254 |
9D9BA368 |
|
77-knn_pros_and_cons.mkv
[67cdd0035ae2af63]
|
42,293,159 |
60A64A76 |
|
78-decision_trees_section_overview.mkv
[c95b74b639e464fa]
|
18,659,391 |
CD812453 |
|
79-eda_on_adult_dataset.mkv
[bb986496fea29b0a]
|
145,872,170 |
325466B4 |
|
80-what_is_entropy_and_info_gain.mkv
[4d872743cb18997e]
|
161,074,761 |
7A354D29 |
|
81-the_decisions_tree_id3_algorithm_part_1.mkv
[2f5b4d1ac0c2e2d1]
|
101,824,139 |
8A2E6B0F |
|
82-the_decisions_tree_id3_algorithm_part_2.mkv
[421d948ad62eabb8]
|
77,581,184 |
DA5E15CB |
|
83-the_decisions_tree_id3_algorithm_part_3.mkv
[1d3607b269662739]
|
40,841,306 |
49E4986D |
|
84-putting_everything_together.mkv
[9ef2f277089b69ce]
|
220,469,811 |
B663C43D |
|
85-evaluating_our_id3_implementation.mkv
[bbec28c1789cd069]
|
145,865,326 |
B82D4281 |
|
86-compare_with_sklearn_implementation.mkv
[782734de3bd2947a]
|
76,142,296 |
E9406C3C |
|
87-visualization_the_tree.mkv
[9a94a8796916da28]
|
80,277,774 |
B30C4C22 |
|
88-plot_the_features_importance.mkv
[6749b7cddf7e10d7]
|
37,289,628 |
E2964AFE |
|
89-decision_tree_hyper-parameters.mkv
[300affd4babbe9dc]
|
92,924,763 |
30C0ECE3 |
|
90-pruning.mkv
[e2c6b26a127ec2b0]
|
133,429,393 |
014B8257 |
|
91-optional_gain_ration.mkv
[1ac7465ef217493e]
|
21,165,742 |
D6088572 |
|
92-what_is_ensemble_learning.mkv
[53af74a15c61ddbb]
|
109,673,662 |
16B8EA78 |
|
93-what_is_bootstrap_sampling.mkv
[ef91243765c683d7]
|
68,021,674 |
C3EB5A25 |
|
94-what_is_bagging.mkv
[2a540127e1e7d316]
|
36,456,310 |
36A0F8F1 |
|
95-out-of-bag_error_(oob_error).mkv
[d5bbdda0b87e8ba]
|
50,035,617 |
421DA206 |
|
96-implementing_random_forests_from_scratch_part_1.mkv
[abe6d919d89e8497]
|
242,156,674 |
6AED033E |
|
97-implementing_random_forests_from_scratch_part_2.mkv
[476a9d9002425ed8]
|
60,472,116 |
590EFBAF |
|
98-random_forests_hyper-parameters.mkv
[be72f55af7841b94]
|
47,923,209 |
9A9CEFFA |
|
99-what_is_boosting.mkv
[90b2c37f164fdc84]
|
41,597,800 |
8E219730 |
|
| Total size: |
8,202,546,721 |
|
|
