|
RAR-files |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.rar |
15,000,000 |
84A9B088 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r00 |
15,000,000 |
B979B927 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r01 |
15,000,000 |
4CAA96DB |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r02 |
15,000,000 |
14B47F5E |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r03 |
15,000,000 |
25E760AF |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r04 |
15,000,000 |
ECDFD29C |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r05 |
15,000,000 |
8A061F6B |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r06 |
15,000,000 |
B9F843D9 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r07 |
15,000,000 |
BCA6BC41 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r08 |
15,000,000 |
6F3E2F00 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r09 |
15,000,000 |
55795623 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r10 |
15,000,000 |
E5FE0B6D |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r11 |
15,000,000 |
3F856E2A |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r12 |
15,000,000 |
0E9D2DF2 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r13 |
15,000,000 |
A9E4DA5F |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r14 |
15,000,000 |
8CB702BE |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r15 |
15,000,000 |
FBBC2E35 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r16 |
15,000,000 |
1BE88B53 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r17 |
15,000,000 |
4E258512 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r18 |
15,000,000 |
8713906D |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r19 |
15,000,000 |
EE401D49 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r20 |
15,000,000 |
89F02551 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r21 |
15,000,000 |
ECD7A1E6 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r22 |
15,000,000 |
76F0BCE4 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r23 |
15,000,000 |
7E3FB8FA |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r24 |
15,000,000 |
87D47BC5 |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r25 |
15,000,000 |
441FD4CC |
| rebar-building.machine.learning.models.in.python.with.scikit.learn.r26 |
12,538,207 |
E746354E |
|
| Total size: |
417,538,207 |
|
|
| Archived
files |
|
01 - Course Overview\01 - Course Overview.mp4
[558e28798a7b9707]
|
4,181,497 |
7AE75AB1 |
|
02 - Processing Data with scikit-learn\02 - Module Overview.mp4
[3e6411a26f7a159]
|
2,493,673 |
36D9C93B |
|
02 - Processing Data with scikit-learn\03 - Prerequisites and Course Overview.mp4
[b7590115c1497ce2]
|
4,541,486 |
C8131391 |
|
02 - Processing Data with scikit-learn\04 - Frequency Based Encoding- Count Vectors.mp4
[8e1111a4658bb071]
|
5,612,205 |
6BCDA7C3 |
|
02 - Processing Data with scikit-learn\05 - Frequency Based Encoding- TF-IDF.mp4
[2607a1bb52b57930]
|
3,513,600 |
C41F9A05 |
|
02 - Processing Data with scikit-learn\06 - Demo- CountVectorizers, TfidfVectorizer, HashingVectorizer.mp4
[12fbd8176a25d65f]
|
12,951,870 |
558625E1 |
|
02 - Processing Data with scikit-learn\07 - Representing Images in Numeric Form.mp4
[4edd45581a1e353a]
|
3,796,914 |
F7434986 |
|
02 - Processing Data with scikit-learn\08 - Demo- Extracting Features from Images.mp4
[a52a6d1c8520b28a]
|
10,875,480 |
EF5CA566 |
|
02 - Processing Data with scikit-learn\09 - Machine Learning Use Cases and scikit-learn.mp4
[f5831cd9889db88c]
|
12,102,874 |
40B5AF74 |
|
02 - Processing Data with scikit-learn\10 - Supervised and Unsupervised Learning Techniques.mp4
[a2e94b5b13125788]
|
9,892,341 |
C66AB4BA |
|
02 - Processing Data with scikit-learn\11 - Demo- Useful Python Packages.mp4
[7c36b0a9a49b4d25]
|
4,893,334 |
7B5A1097 |
|
02 - Processing Data with scikit-learn\12 - Mean and Variance.mp4
[eaaa88cd39d5478f]
|
7,789,244 |
487970C9 |
|
02 - Processing Data with scikit-learn\13 - Demo- Scaling Numeric Data.mp4
[7622c0caad09e486]
|
9,267,787 |
A8984DE4 |
|
02 - Processing Data with scikit-learn\14 - Categorical Data and One-hot Encoding.mp4
[45626e7253c0b019]
|
3,289,069 |
A4C58D58 |
|
02 - Processing Data with scikit-learn\15 - Demo- Representing Categorical Data in Numeric Form.mp4
[2ad94b574193a315]
|
7,707,717 |
B3418B7C |
|
02 - Processing Data with scikit-learn\16 - Representing Text in Numeric Form.mp4
[25c8599820cedbe3]
|
5,927,792 |
14836988 |
|
03 - Building Specialized Regression Models in scikit-learn\17 - Module Overview.mp4
[92ca0d9122a7bafc]
|
1,702,410 |
637294EF |
|
03 - Building Specialized Regression Models in scikit-learn\18 - Ordinary Least Square Regression.mp4
[a1c3cca3d9c2ae17]
|
4,965,185 |
946A8A91 |
|
03 - Building Specialized Regression Models in scikit-learn\19 - Demo- Ridge Regression.mp4
[b1ee846982191e4b]
|
10,934,512 |
61AA2C08 |
|
03 - Building Specialized Regression Models in scikit-learn\20 - Support Vector Regression.mp4
[cfb3f374bcec9e02]
|
9,571,932 |
EDEFE959 |
|
03 - Building Specialized Regression Models in scikit-learn\21 - Demo- Support Vector Regression.mp4
[82ef57005b700e12]
|
10,315,570 |
4FD635FC |
|
03 - Building Specialized Regression Models in scikit-learn\22 - Demo- SVR Reduced Penalty.mp4
[11492868c53a1704]
|
5,681,009 |
F0F5672F |
|
03 - Building Specialized Regression Models in scikit-learn\23 - Measuring Fit Using R-squared.mp4
[227c968cd95c413b]
|
6,650,390 |
9308D476 |
|
03 - Building Specialized Regression Models in scikit-learn\24 - Demo- Data Preparation.mp4
[381949aa2c474e0b]
|
19,485,031 |
25B255B8 |
|
03 - Building Specialized Regression Models in scikit-learn\25 - Demo- Linear Regression Using Estimators.mp4
[6b99517520b7478c]
|
15,192,400 |
1DC99F1E |
|
03 - Building Specialized Regression Models in scikit-learn\26 - L1 and L2 Norm.mp4
[6a6a87a2c7c91df4]
|
3,978,013 |
D3CAB0AC |
|
03 - Building Specialized Regression Models in scikit-learn\27 - Overfitting and The Bias-variance Trade-off.mp4
[662ccc42d59a9447]
|
7,577,362 |
30D5365C |
|
03 - Building Specialized Regression Models in scikit-learn\28 - Multicollinearity in Regression.mp4
[14b55ade0e7c6cfc]
|
7,505,708 |
CE6E9C59 |
|
03 - Building Specialized Regression Models in scikit-learn\29 - Lasso and Ridge Regression.mp4
[fbe2c4a012db9130]
|
5,820,999 |
08430EA9 |
|
03 - Building Specialized Regression Models in scikit-learn\30 - Demo- Lasso Regression.mp4
[da682f072a85159f]
|
11,335,631 |
592A9AB3 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\31 - Module Overview.mp4
[99633573b6be5fe8]
|
1,625,168 |
44ABEF68 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\32 - Support Vector Machines for Classification.mp4
[cc1362f4eb84986c]
|
4,810,637 |
FE0B548B |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\33 - Setting up the SVM Classification Problem.mp4
[2514191a8c4a5292]
|
8,693,333 |
C8504028 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\34 - Demo- SVM Text Classification.mp4
[3c2f476acedc2377]
|
14,764,931 |
06C335A0 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\35 - Demo- SVM Image Classification with Grid Search.mp4
[849338702fdbbe2d]
|
12,494,004 |
230ECBC0 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\36 - Decision Trees.mp4
[81f2b44983e27aa3]
|
14,234,443 |
AE075002 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\37 - Random Forests.mp4
[ee713e3d90e8b8ef]
|
2,388,527 |
0B7F493D |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\38 - Gradient Boosting Regression.mp4
[ab6a7912c68b118b]
|
5,586,938 |
0DFBE161 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\39 - Gradient Boosting Regression and Shrinkage Factor.mp4
[4de7ce02fd4bf1b5]
|
5,749,024 |
31651669 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn\40 - Demo- Gradient Boosting Regression with Grid Search.mp4
[41fbc0bc55dc00c1]
|
17,487,536 |
ED308811 |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\41 - Module Overview.mp4
[5d8ad391e8d29f6c]
|
2,202,910 |
DB86272D |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\42 - Clustering.mp4
[65c7e7a06ea637e2]
|
6,406,056 |
165B6F9B |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\43 - K-means Clustering.mp4
[2988f01720ba1fb9]
|
3,402,336 |
D7B84E84 |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\44 - Mean Shift Clustering.mp4
[aa271adaa2fc9dcf]
|
6,333,771 |
00521860 |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\45 - K-means vs. Mean Shift Clustering.mp4
[62af75f0f5ec8786]
|
2,740,640 |
182AAFFE |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\46 - Demo- Mean Shift Clustering.mp4
[eb737e87ced25bff]
|
14,855,388 |
909CDB4D |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\47 - Demo- Examine Mean Shift Clusters.mp4
[603d6094d08f3bc4]
|
14,639,596 |
DEBC539E |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\48 - Principal Components Analysis- Intuition.mp4
[643b288162c12f8a]
|
7,158,432 |
42D34520 |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\49 - Demo- Principal Components Analysis.mp4
[3c0790df41a929e2]
|
19,512,488 |
7CA08FA5 |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn\50 - Summary and Further Study.mp4
[ef7e3b5c357e1abe]
|
2,347,314 |
06505B8B |
|
python-scikit-learn-building-machine-learning-models.zip |
20,540,091 |
37D7A6D2 |
|
01 - Course Overview |
0 |
00000000 |
|
02 - Processing Data with scikit-learn |
0 |
00000000 |
|
03 - Building Specialized Regression Models in scikit-learn |
0 |
00000000 |
|
04 - Building SVM and Gradient Boosting Models in scikit-learn |
0 |
00000000 |
|
05 - Implementing Clustering and Dimensionality Reduction in scikit-learn |
0 |
00000000 |
|
| Total size: |
417,526,598 |
|
|
