Machine Learning In Python - From A To Z Machine Learning
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3:36:14
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1 - Print & Comments.mp4
02:20
2 - Variables part 1.mp4
06:04
3 - Variables part 2.mp4
03:58
4 - Data types part 1.mp4
09:05
5 - Data types part 2.mp4
05:06
6 - Operators.mp4
08:40
7 - If Statements.mp4
05:05
8 - Loops.mp4
04:27
9 - Functions.mp4
04:03
10 - Pandas.mp4
06:09
11 - Pandas 2.mp4
01:49
12 - Pandas 3.mp4
01:38
13 - Feature Scaling.mp4
02:00
14 - Feature Scaling in Python.mp4
06:36
15 - Label Encoding.mp4
03:40
16 - One Hot Encoding.mp4
04:20
17 - Outlier Detection.mp4
06:25
18 - TrainTest Split.mp4
04:05
19 - MSE RMSE.mp4
02:00
20 - Confusion Matrix Accuracy Score.mp4
02:14
21 - Supervised vs Unsupervised Machine Learning.mp4
01:26
22 - EDA.mp4
06:22
23 - Feature Engineering.mp4
07:45
24 - EDA.mp4
06:12
25 - Feature Engineering.mp4
04:11
26 - Linear Regression.mp4
01:22
27 - Linear Regression 2.mp4
04:04
28 - Linear Regression 3.mp4
02:20
29 - Linear Regression Coding.mp4
07:10
30 - Logistic Regression.mp4
01:12
31 - Logistic Regression Coding.mp4
07:00
32 - K Nearest Neighbors.mp4
02:11
33 - KNearest Neighbors Coding Elbow Method.mp4
11:01
34 - KNearest Neighbors Coding.mp4
06:46
35 - Support Vector Machines.mp4
01:41
36 - Support Vector Regression Coding.mp4
10:46
37 - Decision Tree.mp4
01:07
38 - Random Forest.mp4
01:04
39 - Random Forest Regression.mp4
09:12
40 - About this section.html
41 - For regression data.mp4
11:53
42 - For classification data.mp4
10:20
43 - Classification part 2.mp4
09:16
44 - Kmeans Clustering.mp4
02:09
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- language english
- Training sessions 43
- duration 3:36:14
- Release Date 2023/04/03
