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| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
61B |
| 1 |
506B |
| 1. AdaBoost and XGBoost classifier.mp4 |
67.66MB |
| 1. AdaBoost and XGBoost classifier.srt |
11.39KB |
| 1. AdaBoost and XGBoost regressor.mp4 |
66.97MB |
| 1. AdaBoost and XGBoost regressor.srt |
11.91KB |
| 1. Autocomplete on jupyter notebook.mp4 |
38.70MB |
| 1. Autocomplete on jupyter notebook.srt |
5.78KB |
| 1. Bagging.mp4 |
46.11MB |
| 1. Bagging.srt |
12.83KB |
| 1. Bayes theorem.mp4 |
73.79MB |
| 1. Bayes theorem.srt |
22.20KB |
| 1. Bias, Variance and overfitting.mp4 |
42.05MB |
| 1. Bias, Variance and overfitting.srt |
12.28KB |
| 1. Classification model master template.mp4 |
57.90MB |
| 1. Classification model master template.srt |
8.80KB |
| 1. Data import.mp4 |
71.04MB |
| 1. Data import.srt |
10.58KB |
| 1. Data types.mp4 |
54.01MB |
| 1. Data types.srt |
10.29KB |
| 1. Decision Tree and Random forest.mp4 |
59.49MB |
| 1. Decision Tree and Random forest.srt |
8.49KB |
| 1. Ensemble Learning.mp4 |
52.57MB |
| 1. Ensemble Learning.srt |
9.92KB |
| 1. Euler's number.mp4 |
52.69MB |
| 1. Euler's number.srt |
18.11KB |
| 1. Files introduction.mp4 |
16.81MB |
| 1. Files introduction.srt |
4.84KB |
| 1. If ElIf & else.mp4 |
45.31MB |
| 1. If ElIf & else.srt |
13.98KB |
| 1. Introduction to ML & Supervised learning.mp4 |
46.91MB |
| 1. Introduction to ML & Supervised learning.srt |
14.05KB |
| 1. K Fold cross validation.mp4 |
20.01MB |
| 1. K Fold cross validation.srt |
8.40KB |
| 1. KNN background.mp4 |
62.26MB |
| 1. KNN background.srt |
18.09KB |
| 1. Linear regression working and Cost function.mp4 |
37.60MB |
| 1. Linear regression working and Cost function.srt |
13.64KB |
| 1. Master template regression model - Data creation.mp4 |
134.74MB |
| 1. Master template regression model - Data creation.srt |
22.85KB |
| 1. Matplotlib simple plot, line graphs.mp4 |
54.56MB |
| 1. Matplotlib simple plot, line graphs.srt |
13.62KB |
| 1. Matrices.mp4 |
23.32MB |
| 1. Matrices.srt |
10.22KB |
| 1. Measuring Entropy & Gini impurity.mp4 |
81.94MB |
| 1. Measuring Entropy & Gini impurity.srt |
21.27KB |
| 1. Model deployment basics.mp4 |
51.39MB |
| 1. Model deployment basics.srt |
9.19KB |
| 1. Multiple linear regression in Python.mp4 |
69.61MB |
| 1. Multiple linear regression in Python.srt |
12.70KB |
| 1. Naming conventions and introduction.mp4 |
51.97MB |
| 1. Naming conventions and introduction.srt |
14.80KB |
| 1. Panda series.mp4 |
42.26MB |
| 1. Panda series.srt |
13.27KB |
| 1. Polynomial regression.mp4 |
143.82MB |
| 1. Polynomial regression.srt |
23.00KB |
| 1. Python decorators.mp4 |
72.83MB |
| 1. Python decorators.srt |
17.72KB |
| 1. Python packages.mp4 |
86.78MB |
| 1. Python packages.srt |
11.13KB |
| 1. Python random class.mp4 |
70.59MB |
| 1. Python random class.srt |
18.64KB |
| 1. Python setting up.mp4 |
76.70MB |
| 1. Python setting up.srt |
9.30KB |
| 1. Regular expression introduction.mp4 |
72.46MB |
| 1. Regular expression introduction.srt |
15.65KB |
| 1. ROC, AUC and PR curve background.mp4 |
131.44MB |
| 1. R-square.mp4 |
51.66MB |
| 1. R-square.srt |
17.49KB |
| 1. Setting up.mp4 |
44.98MB |
| 1. Setting up.srt |
11.65KB |
| 1. SVM (regression) Background.mp4 |
26.94MB |
| 1. SVM (regression) Background.srt |
7.89KB |
| 1. SVM getting started with 1D data.mp4 |
57.31MB |
| 1. SVM getting started with 1D data.srt |
17.25KB |
| 1. Thanks for taking this course.mp4 |
29.77MB |
| 1. Thanks for taking this course.srt |
1.80KB |
| 1. The accuracy, not so accurate.mp4 |
46.01MB |
| 1. The accuracy, not so accurate.srt |
13.39KB |
| 1. Try except finally.mp4 |
43.07MB |
| 1. Try except finally.srt |
10.76KB |
| 1. Updated template with GridSearchCV.mp4 |
109.00MB |
| 1. Updated template with GridSearchCV.srt |
13.76KB |
| 1. User-defined functions.mp4 |
47.47MB |
| 1. User-defined functions.srt |
15.79KB |
| 1. Voting classifier.mp4 |
114.74MB |
| 1. Voting classifier.srt |
19.05KB |
| 1. Why Co-relation is important.mp4 |
110.46MB |
| 1. Why Co-relation is important.srt |
19.24KB |
| 1. Why Logistic regression.mp4 |
51.02MB |
| 1. Why Logistic regression.srt |
14.07KB |
| 10 |
748.08KB |
| 10. Sets.mp4 |
54.49MB |
| 10. Sets.srt |
14.76KB |
| 100 |
101.97KB |
| 101 |
200.51KB |
| 102 |
259.09KB |
| 103 |
952.55KB |
| 104 |
1.24MB |
| 105 |
1.31MB |
| 106 |
1.39MB |
| 107 |
1.43MB |
| 108 |
1.50MB |
| 109 |
1.68MB |
| 11 |
608.72KB |
| 11. Tuples.mp4 |
26.73MB |
| 11. Tuples.srt |
8.32KB |
| 110 |
1.99MB |
| 111 |
28.48KB |
| 112 |
347.86KB |
| 113 |
569.37KB |
| 114 |
622.87KB |
| 115 |
999.48KB |
| 116 |
1.05MB |
| 117 |
330.96KB |
| 118 |
1.27MB |
| 119 |
1.47MB |
| 12 |
1.26MB |
| 12. Dictionary in python.mp4 |
34.20MB |
| 12. Dictionary in python.srt |
9.45KB |
| 120 |
1.79MB |
| 121 |
1.82MB |
| 122 |
118.01KB |
| 123 |
205.73KB |
| 124 |
544.67KB |
| 125 |
863.73KB |
| 126 |
1.09MB |
| 127 |
1.89MB |
| 128 |
1.99MB |
| 129 |
702.49KB |
| 13 |
809.11KB |
| 13. None and Bool.mp4 |
21.66MB |
| 13. None and Bool.srt |
6.47KB |
| 130 |
1.02MB |
| 131 |
1.29MB |
| 132 |
1.36MB |
| 133 |
1.38MB |
| 134 |
1.68MB |
| 135 |
1.86MB |
| 136 |
1.91MB |
| 137 |
953.89KB |
| 138 |
1.19MB |
| 139 |
1.22MB |
| 14 |
1.54MB |
| 14. Comparison operators.mp4 |
28.30MB |
| 14. Comparison operators.srt |
9.57KB |
| 140 |
1.74MB |
| 141 |
1.90MB |
| 142 |
1.95MB |
| 143 |
14.56KB |
| 144 |
323.06KB |
| 145 |
826.36KB |
| 146 |
884.94KB |
| 147 |
1.31MB |
| 148 |
137.96KB |
| 149 |
1.30MB |
| 15 |
1.57MB |
| 15. Logical operators.mp4 |
47.88MB |
| 15. Logical operators.srt |
14.21KB |
| 150 |
1.55MB |
| 151 |
1.76MB |
| 152 |
412.18KB |
| 153 |
506.64KB |
| 154 |
680.01KB |
| 155 |
732.69KB |
| 156 |
725.86KB |
| 157 |
747.94KB |
| 158 |
1023.21KB |
| 159 |
1.80MB |
| 16 |
1022.78KB |
| 16. Connect on LinkedIn, It's good!.mp4 |
71.04MB |
| 16. Connect on LinkedIn, It's good!.srt |
4.10KB |
| 160 |
194.76KB |
| 161 |
660.40KB |
| 162 |
1.01MB |
| 163 |
1.23MB |
| 164 |
1.42MB |
| 165 |
1.52MB |
| 166 |
193.21KB |
| 167 |
741.58KB |
| 168 |
239.66KB |
| 169 |
480.07KB |
| 17 |
1.87MB |
| 170 |
1.08MB |
| 171 |
1.23MB |
| 172 |
1.35MB |
| 173 |
1.53MB |
| 174 |
1.70MB |
| 175 |
1.93MB |
| 176 |
2.96KB |
| 177 |
1.06MB |
| 178 |
1.27MB |
| 179 |
1.72MB |
| 18 |
1.86MB |
| 180 |
1.92MB |
| 181 |
1.93MB |
| 182 |
249.39KB |
| 183 |
421.50KB |
| 184 |
628.63KB |
| 185 |
701.15KB |
| 186 |
24.41KB |
| 187 |
273.36KB |
| 188 |
343.75KB |
| 189 |
446.12KB |
| 19 |
54.25KB |
| 190 |
1.96MB |
| 191 |
1.99MB |
| 192 |
1.19MB |
| 193 |
42.17KB |
| 2 |
1.33MB |
| 2. Adjusted R-Square.mp4 |
21.56MB |
| 2. Adjusted R-Square.srt |
7.39KB |
| 2. Balanced vs imbalanced data.mp4 |
28.07MB |
| 2. Balanced vs imbalanced data.srt |
8.28KB |
| 2. Boosting.mp4 |
63.93MB |
| 2. Boosting.srt |
18.83KB |
| 2. Class attributes and Methods.mp4 |
41.19MB |
| 2. Class attributes and Methods.srt |
11.93KB |
| 2. Classification model master template with evaluation and different data set.mp4 |
75.15MB |
| 2. Classification model master template with evaluation and different data set.srt |
11.90KB |
| 2. Class method decorator.mp4 |
52.76MB |
| 2. Class method decorator.srt |
11.53KB |
| 2. Confusion matrix.mp4 |
38.24MB |
| 2. Confusion matrix.srt |
9.75KB |
| 2. Co-variance.mp4 |
57.34MB |
| 2. Co-variance.srt |
17.50KB |
| 2. DataFrame introduction.mp4 |
98.14MB |
| 2. DataFrame introduction.srt |
25.46KB |
| 2. Decision Tree implementation with 1 feature.mp4 |
53.80MB |
| 2. Decision Tree implementation with 1 feature.srt |
15.65KB |
| 2. Error types, else and finally.mp4 |
86.25MB |
| 2. Error types, else and finally.srt |
16.79KB |
| 2. Gradient decent - Background.mp4 |
60.51MB |
| 2. Gradient decent - Background.srt |
19.68KB |
| 2. handling missing data.mp4 |
71.55MB |
| 2. handling missing data.srt |
14.74KB |
| 2. Help function.mp4 |
23.59MB |
| 2. Help function.srt |
7.27KB |
| 2. Jupyter notebook.mp4 |
95.43MB |
| 2. Jupyter notebook.srt |
15.08KB |
| 2. KNN in python.mp4 |
48.73MB |
| 2. KNN in python.srt |
8.86KB |
| 2. Likelihood vs probability.mp4 |
52.32MB |
| 2. Likelihood vs probability.srt |
10.92KB |
| 2. Linear regression implementation in python - Part 1.mp4 |
92.47MB |
| 2. Linear regression implementation in python - Part 1.srt |
21.97KB |
| 2. Logistic regression background.mp4 |
52.01MB |
| 2. Logistic regression background.srt |
15.14KB |
| 2. Master template regression model - Models and evaluation.mp4 |
21.98MB |
| 2. Master template regression model - Models and evaluation.srt |
3.94KB |
| 2. Matplotlib Bar-graph and multiple plotting.mp4 |
68.58MB |
| 2. Matplotlib Bar-graph and multiple plotting.srt |
14.01KB |
| 2. Matrix operations and scalar operations.mp4 |
13.99MB |
| 2. Matrix operations and scalar operations.srt |
5.84KB |
| 2. Multiple linear regression behind the scene - Part 1.mp4 |
160.26MB |
| 2. Multiple linear regression behind the scene - Part 1.srt |
20.98KB |
| 2. NumPy array functions - Array generate.mp4 |
44.64MB |
| 2. NumPy array functions - Array generate.srt |
15.77KB |
| 2. Paths.mp4 |
62.83MB |
| 2. Paths.srt |
15.98KB |
| 2. Polynomial regression on multiple feature dataset.mp4 |
119.27MB |
| 2. Polynomial regression on multiple feature dataset.srt |
27.97KB |
| 2. Prediction using value.mp4 |
54.35MB |
| 2. Prediction using value.srt |
9.19KB |
| 2. Python numbers.mp4 |
28.92MB |
| 2. Python numbers.srt |
10.08KB |
| 2. Random Forest.mp4 |
52.61MB |
| 2. Random Forest.srt |
10.14KB |
| 2. RandomizedSearchCV.mp4 |
115.41MB |
| 2. RandomizedSearchCV.srt |
12.11KB |
| 2. Random under numpy and Arange.mp4 |
77.09MB |
| 2. Random under numpy and Arange.srt |
17.60KB |
| 2. Regular expression, grouping and pipe.mp4 |
48.18MB |
| 2. Regular expression, grouping and pipe.srt |
14.11KB |
| 2. ROC, AUC - Evaluating best model.mp4 |
61.12MB |
| 2. ROC, AUC - Evaluating best model.srt |
9.71KB |
| 2. Scatter plot on Iris dataset.mp4 |
153.44MB |
| 2. Scatter plot on Iris dataset.srt |
23.26KB |
| 2. SVM, mapping higher dimension.mp4 |
47.80MB |
| 2. SVM, mapping higher dimension.srt |
15.63KB |
| 2. SVR under Python.mp4 |
21.73MB |
| 2. SVR under Python.srt |
4.01KB |
| 2. Unsupervised learning.mp4 |
52.50MB |
| 2. Unsupervised learning.srt |
9.25KB |
| 2. Updated template with GridSearchCV.mp4 |
143.32MB |
| 2. Updated template with GridSearchCV.srt |
24.17KB |
| 2. User defined packages.mp4 |
144.43MB |
| 2. User defined packages.srt |
20.06KB |
| 2. While loop.mp4 |
32.48MB |
| 2. While loop.srt |
11.13KB |
| 20 |
958.56KB |
| 21 |
584.42KB |
| 22 |
725.39KB |
| 23 |
1.53MB |
| 24 |
89.58KB |
| 25 |
726.70KB |
| 26 |
1.24MB |
| 27 |
1.53MB |
| 28 |
368.92KB |
| 29 |
1.12MB |
| 3 |
187.87KB |
| 3. Accuracy, precision, recall, Specificity, F1 Score.mp4 |
66.10MB |
| 3. Accuracy, precision, recall, Specificity, F1 Score.srt |
14.76KB |
| 3. Co-relation.mp4 |
57.39MB |
| 3. Co-relation.srt |
13.78KB |
| 3. DataFrame Selections.mp4 |
75.65MB |
| 3. DataFrame Selections.srt |
18.70KB |
| 3. Feature selection and Encoding categorical data.mp4 |
48.21MB |
| 3. Feature selection and Encoding categorical data.srt |
10.23KB |
| 3. For loop.mp4 |
35.00MB |
| 3. For loop.srt |
10.71KB |
| 3. Gradient decent in 2D and 3D space.mp4 |
85.14MB |
| 3. Gradient decent in 2D and 3D space.srt |
13.11KB |
| 3. Inheritance.mp4 |
41.99MB |
| 3. Inheritance.srt |
12.58KB |
| 3. K Fold cross validation without GridSearchCV.mp4 |
91.91MB |
| 3. K Fold cross validation without GridSearchCV.srt |
19.21KB |
| 3. Linear regression implementation in python - Part 2.mp4 |
35.27MB |
| 3. Linear regression implementation in python - Part 2.srt |
7.82KB |
| 3. Logistic regression under python.mp4 |
41.68MB |
| 3. Logistic regression under python.srt |
6.81KB |
| 3. Matplotlib Subplot and histogram.mp4 |
82.54MB |
| 3. Matplotlib Subplot and histogram.srt |
25.37KB |
| 3. Matrix multiplication.mp4 |
23.39MB |
| 3. Matrix multiplication.srt |
8.50KB |
| 3. Multinomial naive bayes.mp4 |
65.01MB |
| 3. Multinomial naive bayes.srt |
16.90KB |
| 3. Multiple linear regression behind the scene - Part 2.mp4 |
75.10MB |
| 3. Multiple linear regression behind the scene - Part 2.srt |
17.37KB |
| 3. Pair plot and limitations.mp4 |
67.83MB |
| 3. Pair plot and limitations.srt |
13.40KB |
| 3. Pycharm python IDE.mp4 |
60.50MB |
| 3. Pycharm python IDE.srt |
8.76KB |
| 3. Python collections.mp4 |
64.02MB |
| 3. Python collections.srt |
17.05KB |
| 3. Python generators.mp4 |
76.10MB |
| 3. Python generators.srt |
17.50KB |
| 3. Random array based methods.mp4 |
57.40MB |
| 3. Random array based methods.srt |
14.15KB |
| 3. Read mode, write mode and methods.mp4 |
97.06MB |
| 3. Read mode, write mode and methods.srt |
19.09KB |
| 3. Repetition and range.mp4 |
71.55MB |
| 3. Repetition and range.srt |
12.98KB |
| 3. ROC, AUC - Calculating the optimal threshold (Youdens method).mp4 |
124.39MB |
| 3. ROC, AUC - Calculating the optimal threshold (Youdens method).srt |
21.02KB |
| 3. Scopes.mp4 |
60.95MB |
| 3. Scopes.srt |
16.24KB |
| 3. SVM, in 2D space.mp4 |
44.62MB |
| 3. SVM, in 2D space.srt |
11.64KB |
| 3. Type of data.mp4 |
23.76MB |
| 3. Type of data.srt |
9.09KB |
| 3. User defined packages continues.mp4 |
57.59MB |
| 3. User defined packages continues.srt |
6.56KB |
| 3. Variables and assignment.mp4 |
31.81MB |
| 3. Variables and assignment.srt |
9.12KB |
| 3. Visualization and few more things.mp4 |
59.82MB |
| 3. Visualization and few more things.srt |
9.32KB |
| 3. Visualization of decision tree model.mp4 |
89.29MB |
| 3. Visualization of decision tree model.srt |
15.92KB |
| 30 |
1.22MB |
| 31 |
1.75MB |
| 32 |
879.81KB |
| 33 |
365.65KB |
| 34 |
1.05MB |
| 35 |
1.46MB |
| 36 |
59.32KB |
| 37 |
238.91KB |
| 38 |
583.66KB |
| 39 |
934.37KB |
| 4 |
701.17KB |
| 4. args and kwargs.mp4 |
50.95MB |
| 4. args and kwargs.srt |
12.51KB |
| 4. Confusion matrix 3D.mp4 |
74.97MB |
| 4. Confusion matrix 3D.srt |
15.32KB |
| 4. Curse of dimensionality.mp4 |
33.81MB |
| 4. Curse of dimensionality.srt |
9.65KB |
| 4. Decision Tree implementation - multiple features.mp4 |
53.75MB |
| 4. Decision Tree implementation - multiple features.srt |
10.23KB |
| 4. Greedy, non-greedy matches and findall.mp4 |
61.45MB |
| 4. Greedy, non-greedy matches and findall.srt |
15.03KB |
| 4. GroupBy.mp4 |
37.28MB |
| 4. GroupBy.srt |
10.22KB |
| 4. K Fold cross validation without GridSearchCV continues.mp4 |
68.43MB |
| 4. K Fold cross validation without GridSearchCV continues.srt |
10.21KB |
| 4. LabelEncoding classes.mp4 |
47.16MB |
| 4. LabelEncoding classes.srt |
8.17KB |
| 4. Logistic regression on multi-class classification.mp4 |
42.81MB |
| 4. Logistic regression on multi-class classification.srt |
12.37KB |
| 4. Matplotlib Scatter plots and Pie charts.mp4 |
48.53MB |
| 4. Matplotlib Scatter plots and Pie charts.srt |
12.05KB |
| 4. Mean Mode median.mp4 |
15.96MB |
| 4. Mean Mode median.srt |
6.66KB |
| 4. Multiple, multi level inheritance and MRO.mp4 |
79.77MB |
| 4. Multiple, multi level inheritance and MRO.srt |
19.95KB |
| 4. Python counter from collections.mp4 |
54.21MB |
| 4. Python counter from collections.srt |
14.01KB |
| 4. ROC, AUC - Calculating the optimal threshold (best Accuracy method).mp4 |
69.34MB |
| 4. ROC, AUC - Calculating the optimal threshold (best Accuracy method).srt |
12.55KB |
| 4. Slicing and broadcast.mp4 |
44.14MB |
| 4. Slicing and broadcast.srt |
16.24KB |
| 4. String basics.mp4 |
35.29MB |
| 4. String basics.srt |
10.02KB |
| 4. SVM implementation using python.mp4 |
64.16MB |
| 4. SVM implementation using python.srt |
10.42KB |
| 4. Test and train data split and Feature scaling.mp4 |
97.95MB |
| 4. Test and train data split and Feature scaling.srt |
20.78KB |
| 4. The log scale.mp4 |
83.64MB |
| 4. The log scale.srt |
26.21KB |
| 4. Tips dataset.mp4 |
26.08MB |
| 4. Tips dataset.srt |
4.19KB |
| 4. Tuple unpacking.mp4 |
31.28MB |
| 4. Tuple unpacking.srt |
11.97KB |
| 4. Update Anaconda website updated.mp4 |
56.04MB |
| 4. Update Anaconda website updated.srt |
9.32KB |
| 4. Vector Multiplication.mp4 |
82.95MB |
| 4. Vector Multiplication.srt |
15.54KB |
| 40 |
1.30MB |
| 41 |
1.90MB |
| 42 |
355.00KB |
| 43 |
869.10KB |
| 44 |
922.37KB |
| 45 |
1.00MB |
| 46 |
1.03MB |
| 47 |
216.14KB |
| 48 |
1.17MB |
| 49 |
1.54MB |
| 5 |
334.99KB |
| 5. BeginsWith endsWith and dot character.mp4 |
86.88MB |
| 5. BeginsWith endsWith and dot character.srt |
21.90KB |
| 5. Break, continue and pass.mp4 |
37.34MB |
| 5. Break, continue and pass.srt |
15.03KB |
| 5. CAP curve background.mp4 |
53.90MB |
| 5. CAP curve background.srt |
14.31KB |
| 5. Concatenation.mp4 |
72.27MB |
| 5. Concatenation.srt |
14.51KB |
| 5. Gaussian naive bayes.mp4 |
77.43MB |
| 5. Gaussian naive bayes.srt |
20.80KB |
| 5. Identity matrix, matrix inverse properties, transpose of matrix.mp4 |
28.77MB |
| 5. Identity matrix, matrix inverse properties, transpose of matrix.srt |
9.02KB |
| 5. KNN on multi class classification.mp4 |
44.32MB |
| 5. KNN on multi class classification.srt |
8.50KB |
| 5. Logistic regression on multi-class classification under python.mp4 |
29.53MB |
| 5. Logistic regression on multi-class classification under python.srt |
3.72KB |
| 5. Maps, Filters and Lambdas.mp4 |
67.55MB |
| 5. Maps, Filters and Lambdas.srt |
20.81KB |
| 5. Math Matrix multiplication.mp4 |
24.07MB |
| 5. Math Matrix multiplication.srt |
8.82KB |
| 5. Matplotlib 3D scatter and simple plot.mp4 |
54.46MB |
| 5. Matplotlib 3D scatter and simple plot.srt |
10.78KB |
| 5. Matrices selection and conditional selection.mp4 |
54.39MB |
| 5. Matrices selection and conditional selection.srt |
19.23KB |
| 5. Meet your Author.mp4 |
42.10MB |
| 5. Meet your Author.srt |
2.47KB |
| 5. Polymorphism.mp4 |
41.14MB |
| 5. Polymorphism.srt |
13.03KB |
| 5. Pre-processing re-visited.mp4 |
110.43MB |
| 5. Pre-processing re-visited.srt |
19.53KB |
| 5. Seaborn plots.mp4 |
95.29MB |
| 5. Seaborn plots.srt |
26.15KB |
| 5. Standard deviation.mp4 |
32.77MB |
| 5. Standard deviation.srt |
14.76KB |
| 5. String Start Stop and Step.mp4 |
61.02MB |
| 5. String Start Stop and Step.srt |
16.38KB |
| 5. Under and over sampling.mp4 |
87.64MB |
| 5. Under and over sampling.srt |
17.03KB |
| 50 |
1.73MB |
| 51 |
456.44KB |
| 52 |
465.68KB |
| 53 |
813.60KB |
| 54 |
982.67KB |
| 55 |
983.39KB |
| 56 |
1.41MB |
| 57 |
397.42KB |
| 58 |
674.29KB |
| 59 |
1.42MB |
| 6 |
1.26MB |
| 6. Assignment and tips.mp4 |
32.58MB |
| 6. Assignment and tips.srt |
4.49KB |
| 6. BeginsWith endsWith and dot character continues.mp4 |
28.65MB |
| 6. BeginsWith endsWith and dot character continues.srt |
8.46KB |
| 6. CAP curve implementation.mp4 |
66.97MB |
| 6. CAP curve implementation.srt |
12.99KB |
| 6. Facetgrid plots.mp4 |
44.71MB |
| 6. Facetgrid plots.srt |
11.38KB |
| 6. Gaussian naive Bayes under Python & Visualization of models.mp4 |
88.47MB |
| 6. Gaussian naive Bayes under Python & Visualization of models.srt |
14.79KB |
| 6. Lambda once again.mp4 |
49.68MB |
| 6. Lambda once again.srt |
12.91KB |
| 6. Linkedin and Instagram links.html |
511B |
| 6. Matpotlib Wireframe surface plotting.mp4 |
57.02MB |
| 6. Matpotlib Wireframe surface plotting.srt |
12.92KB |
| 6. Most common data distributions, PDF and PMF.mp4 |
51.44MB |
| 6. Most common data distributions, PDF and PMF.srt |
11.03KB |
| 6. Numpy operations.mp4 |
40.69MB |
| 6. Numpy operations.srt |
9.08KB |
| 6. Operations.mp4 |
32.99MB |
| 6. Operations.srt |
10.42KB |
| 6. Pre-processing re-visited continues.mp4 |
71.21MB |
| 6. Pre-processing re-visited continues.srt |
12.51KB |
| 6. Range, enumerate and zip.mp4 |
75.00MB |
| 6. Range, enumerate and zip.srt |
25.78KB |
| 6. Special class methods.mp4 |
65.81MB |
| 6. Special class methods.srt |
17.52KB |
| 6. String slicing.mp4 |
20.04MB |
| 6. String slicing.srt |
5.90KB |
| 60 |
1.57MB |
| 61 |
174.25KB |
| 62 |
348.48KB |
| 63 |
456.03KB |
| 64 |
1.03MB |
| 65 |
1.03MB |
| 66 |
1.90MB |
| 67 |
193.25KB |
| 68 |
1017.43KB |
| 69 |
1.84MB |
| 7 |
571.55KB |
| 7. About Project files.mp4 |
57.65MB |
| 7. About Project files.srt |
3.22KB |
| 7. Assignment solution and OneHotEncoding - Part 01.mp4 |
113.21MB |
| 7. Assignment solution and OneHotEncoding - Part 01.srt |
20.19KB |
| 7. CAP curve with multiple models and multi-class.mp4 |
135.67MB |
| 7. CAP curve with multiple models and multi-class.srt |
20.08KB |
| 7. Feature selection.mp4 |
106.13MB |
| 7. Feature selection.srt |
16.99KB |
| 7. In.mp4 |
28.47MB |
| 7. In.srt |
11.01KB |
| 7. Percentiles, moment and Quantiles.mp4 |
88.76MB |
| 7. Percentiles, moment and Quantiles.srt |
15.85KB |
| 7. Sets.mp4 |
37.51MB |
| 7. Sets.srt |
12.24KB |
| 7. String formatting.mp4 |
38.45MB |
| 7. String formatting.srt |
9.63KB |
| 7. Univariate Analysis using PDF.mp4 |
57.29MB |
| 7. Univariate Analysis using PDF.srt |
13.98KB |
| 70 |
1.98MB |
| 71 |
75.44KB |
| 72 |
1.17MB |
| 73 |
1.74MB |
| 74 |
393.00KB |
| 75 |
562.04KB |
| 76 |
905.17KB |
| 77 |
1006.18KB |
| 78 |
1.05MB |
| 79 |
1.49MB |
| 8 |
1.78MB |
| 8. Assignment solution and OneHotEncoding - Part 02.mp4 |
126.22MB |
| 8. Assignment solution and OneHotEncoding - Part 02.srt |
19.21KB |
| 8. Boxplot and Violin Plot.mp4 |
53.07MB |
| 8. Boxplot and Violin Plot.srt |
17.65KB |
| 8. Input and import.mp4 |
28.00MB |
| 8. Input and import.srt |
9.73KB |
| 8. Lists in Python.mp4 |
33.36MB |
| 8. Lists in Python.srt |
9.99KB |
| 8. Literal matching, Sub and verbose.mp4 |
39.87MB |
| 8. Literal matching, Sub and verbose.srt |
12.01KB |
| 8. Short discussion.mp4 |
26.28MB |
| 8. Short discussion.srt |
3.74KB |
| 80 |
1.50MB |
| 81 |
182.11KB |
| 82 |
524.45KB |
| 83 |
104.79KB |
| 84 |
356.59KB |
| 85 |
421.81KB |
| 86 |
617.08KB |
| 87 |
623.17KB |
| 88 |
675.30KB |
| 89 |
710.39KB |
| 9 |
1.61MB |
| 9. Discussion forum.mp4 |
61.62MB |
| 9. Discussion forum.srt |
3.77KB |
| 9. HeatMap.mp4 |
42.78MB |
| 9. HeatMap.srt |
12.43KB |
| 9. List shorting, reversing, removing, clear, list of list.mp4 |
44.09MB |
| 9. List shorting, reversing, removing, clear, list of list.srt |
12.90KB |
| 90 |
729.77KB |
| 91 |
1000.13KB |
| 92 |
1.96MB |
| 93 |
1.44MB |
| 94 |
1.51MB |
| 95 |
1.54MB |
| 96 |
1.61MB |
| 97 |
1.65MB |
| 98 |
1.79MB |
| 99 |
1.99MB |
| TutsNode.com.txt |
63B |