|
RAR-files |
| pluralsight.feature.engineering.jul2020-rebar.rar |
50,000,000 |
2D8150F4 |
| pluralsight.feature.engineering.jul2020-rebar.r00 |
50,000,000 |
925B27D1 |
| pluralsight.feature.engineering.jul2020-rebar.r01 |
50,000,000 |
4927DF7F |
| pluralsight.feature.engineering.jul2020-rebar.r02 |
50,000,000 |
132CACDC |
| pluralsight.feature.engineering.jul2020-rebar.r03 |
50,000,000 |
37F3C408 |
| pluralsight.feature.engineering.jul2020-rebar.r04 |
50,000,000 |
8E8346CB |
| pluralsight.feature.engineering.jul2020-rebar.r05 |
50,000,000 |
23599148 |
| pluralsight.feature.engineering.jul2020-rebar.r06 |
50,000,000 |
03B967CF |
| pluralsight.feature.engineering.jul2020-rebar.r07 |
50,000,000 |
4F0E3A6D |
| pluralsight.feature.engineering.jul2020-rebar.r08 |
50,000,000 |
F5FE8670 |
| pluralsight.feature.engineering.jul2020-rebar.r09 |
50,000,000 |
CC110721 |
| pluralsight.feature.engineering.jul2020-rebar.r10 |
50,000,000 |
74AD40C6 |
| pluralsight.feature.engineering.jul2020-rebar.r11 |
50,000,000 |
6AF796E4 |
| pluralsight.feature.engineering.jul2020-rebar.r12 |
50,000,000 |
7E1E20F2 |
| pluralsight.feature.engineering.jul2020-rebar.r13 |
50,000,000 |
55E75000 |
| pluralsight.feature.engineering.jul2020-rebar.r14 |
50,000,000 |
DF6C01FA |
| pluralsight.feature.engineering.jul2020-rebar.r15 |
50,000,000 |
11F1A231 |
| pluralsight.feature.engineering.jul2020-rebar.r16 |
50,000,000 |
05169624 |
| pluralsight.feature.engineering.jul2020-rebar.r17 |
50,000,000 |
6D6E072B |
| pluralsight.feature.engineering.jul2020-rebar.r18 |
50,000,000 |
C3426A9D |
| pluralsight.feature.engineering.jul2020-rebar.r19 |
50,000,000 |
3C105DAC |
| pluralsight.feature.engineering.jul2020-rebar.r20 |
50,000,000 |
F086362B |
| pluralsight.feature.engineering.jul2020-rebar.r21 |
50,000,000 |
E970B69F |
| pluralsight.feature.engineering.jul2020-rebar.r22 |
50,000,000 |
E9FA2EE3 |
| pluralsight.feature.engineering.jul2020-rebar.r23 |
50,000,000 |
63DA5E6A |
| pluralsight.feature.engineering.jul2020-rebar.r24 |
50,000,000 |
41530F1F |
| pluralsight.feature.engineering.jul2020-rebar.r25 |
50,000,000 |
D3E9D013 |
| pluralsight.feature.engineering.jul2020-rebar.r26 |
50,000,000 |
972503A2 |
| pluralsight.feature.engineering.jul2020-rebar.r27 |
50,000,000 |
2325255B |
| pluralsight.feature.engineering.jul2020-rebar.r28 |
50,000,000 |
3962B66D |
| pluralsight.feature.engineering.jul2020-rebar.r29 |
50,000,000 |
49C6CA30 |
| pluralsight.feature.engineering.jul2020-rebar.r30 |
50,000,000 |
862CC717 |
| pluralsight.feature.engineering.jul2020-rebar.r31 |
50,000,000 |
F45F35D1 |
| pluralsight.feature.engineering.jul2020-rebar.r32 |
50,000,000 |
0091ED42 |
| pluralsight.feature.engineering.jul2020-rebar.r33 |
6,942,160 |
6451C9C0 |
|
| Total size: |
1,706,942,160 |
|
|
| Archived
files |
|
01 - Introduction\01 - Feature Engineering PDF.mp4
[a12d26a9774c6e63]
|
235,545 |
A75EF6B6 |
|
01 - Introduction\02 - Introduction.mp4
[43b1abb71aeda0df]
|
12,797,967 |
7E664590 |
|
02 - Raw data to features\03 - Raw Data to Features.mp4
[cf4142d0bcfe94a2]
|
30,807,761 |
947DA8F4 |
|
02 - Raw data to features\04 - Good vs Bad Features.mp4
[894de8b435aec0a7]
|
40,817,981 |
AD7F2847 |
|
02 - Raw data to features\05 - Quiz - Features are Related to the Objective.mp4
[72778e7c00490ddc]
|
39,149,867 |
1964E6AE |
|
02 - Raw data to features\06 - Quiz - Features are knowable at prediction time.mp4
[f4f8348d698613d2]
|
18,728,007 |
B4B28138 |
|
02 - Raw data to features\07 - Features are knowable at prediction time'.mp4
[16768fc8a4480f28]
|
39,149,867 |
118DC499 |
|
02 - Raw data to features\08 - Features should be numeric.mp4
[ddb42c828aab6a2a]
|
1,728,277 |
56ACB5AC |
|
02 - Raw data to features\09 - Quiz - Features should be numeric.mp4
[f6e1772f95655862]
|
36,292,783 |
2E12DFC9 |
|
02 - Raw data to features\10 - Features should have enough examples.mp4
[1312c78e4ddc935d]
|
19,772,658 |
FDCB7DE5 |
|
02 - Raw data to features\11 - Quiz - Features should have enough examples (part 1).mp4
[f9493aa8495e8b94]
|
21,625,795 |
7B3C9FF5 |
|
02 - Raw data to features\12 - Quiz - Features should have enough examples (part 2).mp4
[688e958bb3a4c729]
|
32,314,322 |
5355EB52 |
|
02 - Raw data to features\13 - Bringing human insights.mp4
[6c5b9d95c88a44a4]
|
3,989,529 |
8779261F |
|
02 - Raw data to features\14 - Representing Features.mp4
[32594a7e2278e4c0]
|
24,364,604 |
0AA82CBC |
|
02 - Raw data to features\15 - ML vs Statistics.mp4
[299b4f73958d98a8]
|
24,996,693 |
24199AD0 |
|
02 - Raw data to features\16 - Lab - Improving model accuracy with new features.mp4
[402652098971b8bd]
|
251,816 |
30B4FFFF |
|
02 - Raw data to features\17 - Improve model accuracy with new features.mp4
[bb25e72ba88c5018]
|
92,233,338 |
6BE47D80 |
|
03 - Preprocessing and feature creation\18 - Preprocessing and feature creation.mp4
[a371e0c8341c508f]
|
23,636,292 |
762EA5AB |
|
03 - Preprocessing and feature creation\19 - Apache Beam _ Cloud Dataflow.mp4
[90674ac086cd7ded]
|
30,198,767 |
07AD9D65 |
|
03 - Preprocessing and feature creation\20 - A Simple Dataflow Pipeline.mp4
[a2cf9e1ec95b2513]
|
2,301,962 |
B6B03E6E |
|
03 - Preprocessing and feature creation\21 - Lab - A simple Dataflow pipeline (Python).mp4
[82518dc80f84185a]
|
246,039 |
A2D924BC |
|
03 - Preprocessing and feature creation\22 - Lab Solution - A Simple Dataflow Pipeline.mp4
[533786d4fe159b51]
|
22,108,716 |
480447E1 |
|
03 - Preprocessing and feature creation\23 - Data Pipelines at Scale.mp4
[f9c1d3e308a15d4c]
|
15,393,029 |
D65C349F |
|
03 - Preprocessing and feature creation\24 - MapReduce in Dataflow.mp4
[8bcc434f67eaad49]
|
4,022,438 |
AF86DA65 |
|
03 - Preprocessing and feature creation\25 - Lab - MapReduce in Dataflow (Python).mp4
[bbb0346e597622e9]
|
237,325 |
D0279279 |
|
03 - Preprocessing and feature creation\26 - Lab Solution - MapReduce in Dataflow.mp4
[7623fd5ccd86ef17]
|
10,858,937 |
15D21F75 |
|
03 - Preprocessing and feature creation\27 - Dataflow Wrapup.mp4
[4371a15d28a9af44]
|
1,001,925 |
AF076B53 |
|
03 - Preprocessing and feature creation\28 - Preprocessing with Cloud Dataprep.mp4
[f037a650424382df]
|
27,388,169 |
606815D4 |
|
03 - Preprocessing and feature creation\29 - Lab Intro - Computing Time-Windowed Features in Cloud Dataprep.mp4
[25d22eae79af28be]
|
36,675,803 |
6D7FF089 |
|
03 - Preprocessing and feature creation\30 - Lab - Computing Time-Windowed Features in Cloud Dataprep.mp4
[3d210fb63795fb50]
|
257,904 |
973913B1 |
|
03 - Preprocessing and feature creation\31 - Lab Solution - Computing Time-Windowed Features in Cloud Dataprep.mp4
[332ba9c37f6289ba]
|
4,209,529 |
78E3B35C |
|
04 - Feature crosses\32 - Introduction.mp4
[623a3ed7177c3cec]
|
12,800,136 |
B01532E8 |
|
04 - Feature crosses\33 - What is a feature cross.mp4
[3dd6c719046e25b6]
|
49,273,352 |
11C7F1A8 |
|
04 - Feature crosses\34 - Discretization.mp4
[5a9bff1d889759c3]
|
19,591,949 |
9F1743FD |
|
04 - Feature crosses\35 - Memorization vs. Generalization.mp4
[9bd3c00ee2c6cdd1]
|
58,539,083 |
C404F5A9 |
|
04 - Feature crosses\36 - Taxi colors.mp4
[2740baa3193b43e2]
|
52,163,154 |
E653A378 |
|
04 - Feature crosses\37 - Lab Intro - Feature Crosses to create a good classifier.mp4
[7e241db0117ceaca]
|
2,787,650 |
BF8D2A54 |
|
04 - Feature crosses\38 - Lab Solution - Feature Crosses to create a good classifier.mp4
[8f7638e0dd2a4c85]
|
41,293,749 |
CA12FDB9 |
|
04 - Feature crosses\39 - Sparsity + Quiz.mp4
[b1f4fe5c686d1abd]
|
50,749,017 |
46FEF50A |
|
04 - Feature crosses\40 - Lab Intro - Too Much of a Good Thing.mp4
[a43a89134a14cd59]
|
7,207,594 |
5034FC89 |
|
04 - Feature crosses\41 - Lab Solution - Too Much of a Good Thing.mp4
[296540da0e6ebd14]
|
48,470,877 |
B50DA206 |
|
04 - Feature crosses\42 - Implementing Feature Crosses.mp4
[b62219a69e48a20f]
|
65,346,046 |
C5E83234 |
|
04 - Feature crosses\43 - Embedding Feature Crosses.mp4
[747f1a0ef1bd984]
|
103,130,918 |
EC00D63D |
|
04 - Feature crosses\44 - Where to Do Feature Engineering.mp4
[4cebbc502a4d952]
|
38,403,316 |
696A4F2A |
|
04 - Feature crosses\45 - Feature Creation in TensorFlow.mp4
[79e602edceec00b2]
|
6,069,347 |
E1E1127E |
|
04 - Feature crosses\46 - Feature Creation in DataFlow.mp4
[23e5ef193814512e]
|
11,665,288 |
670B2F3B |
|
04 - Feature crosses\47 - Lab Intro - Improve ML Model with Feature Engineering.mp4
[dea69c5dd5da7145]
|
5,084,390 |
DD122457 |
|
04 - Feature crosses\48 - Lab - Improve Machine Learning model with Feature Engineering.mp4
[f81a644a2ebd8b]
|
256,294 |
C2925AA1 |
|
04 - Feature crosses\49 - Debrief - ML Fairness.mp4
[d569f9834db184c3]
|
33,581,872 |
44577E34 |
|
04 - Feature crosses\50 - Solution - Improve ML Model with Feature Engineering.mp4
[d29613b3b6473385]
|
153,601,599 |
62D0B1D2 |
|
05 - TensorFlow Transform\51 - Introduction.mp4
[66646d7a96b9252f]
|
4,943,560 |
DA785583 |
|
05 - TensorFlow Transform\52 - TensorFlow Transform.mp4
[35a598c3f476b6f7]
|
71,792,370 |
977A28CE |
|
05 - TensorFlow Transform\53 - Analyze phase.mp4
[4428ee4ac2d90b29]
|
10,133,303 |
CCE9FF14 |
|
05 - TensorFlow Transform\54 - Transform phase.mp4
[a0f8da607c3123a1]
|
20,047,564 |
5F72003C |
|
05 - TensorFlow Transform\55 - Supporting serving.mp4
[a3599b96907534bc]
|
27,079,579 |
79C8B989 |
|
05 - TensorFlow Transform\56 - Exploring tf.transform.mp4
[35c6d7acaccb6335]
|
17,099,607 |
7D45B101 |
|
05 - TensorFlow Transform\57 - Lab - Exploring tf.transform.mp4
[b3a8c134f1b94fe1]
|
235,286 |
02C56C19 |
|
05 - TensorFlow Transform\58 - Exploring tf.transform.mp4
[5a1d596d720dcdc9]
|
151,540,089 |
1E9B878A |
|
06 - Summary\59 - Summary.mp4
[c79cee72186d5a58]
|
26,250,558 |
3A6E3E81 |
|
01 - Introduction |
0 |
00000000 |
|
02 - Raw data to features |
0 |
00000000 |
|
03 - Preprocessing and feature creation |
0 |
00000000 |
|
04 - Feature crosses |
0 |
00000000 |
|
05 - TensorFlow Transform |
0 |
00000000 |
|
06 - Summary |
0 |
00000000 |
|
| Total size: |
1,706,931,192 |
|
|
