Udemy MACHINE LEARNING MASTER CLASS AI MADE EASY Zero to Hero
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| Torrent Hash : | 89A416054201781C60DF1B3747D9F7E42DD48357 |
| Torrent Added : | at Oct. 26, 2023, 9:06 a.m. in Other |
| Torrent Size : | 11.7 GB |
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Udemy MACHINE LEARNING MASTER CLASS AI MADE EASY Zero to Hero
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Udemy MACHINE LEARNING MASTER CLASS AI MADE EASY Zero to Hero
2. Multiple linear regression behind the scene - Part 1.mp4 -
TutsNode.com.txt -
2. Polynomial regression on multiple feature dataset.srt -
4. The log scale.srt -
5. Seaborn plots.srt -
6. Range, enumerate and zip.srt -
2. DataFrame introduction.srt -
3. Matplotlib Subplot and histogram.srt -
2. Updated template with GridSearchCV.srt -
2. Scatter plot on Iris dataset.srt -
1. Polynomial regression.srt -
5. Meet your Author.srt -
6. Linkedin and Instagram links.html -
1. Master template regression model - Data creation.srt -
1. Bayes theorem.srt -
2. Linear regression implementation in python - Part 1.srt -
5. BeginsWith endsWith and dot character.srt -
1. Measuring Entropy & Gini impurity.srt -
3. ROC, AUC - Calculating the optimal threshold (Youdens method).srt -
2. Multiple linear regression behind the scene - Part 1.srt -
5. Maps, Filters and Lambdas.srt -
5. Gaussian naive bayes.srt -
4. Test and train data split and Feature scaling.srt -
7. Assignment solution and OneHotEncoding - Part 01.srt -
7. CAP curve with multiple models and multi-class.srt -
2. User defined packages.srt -
4. Multiple, multi level inheritance and MRO.srt -
2. Gradient decent - Background.srt -
5. Pre-processing re-visited.srt -
1. Why Co-relation is important.srt -
5. Matrices selection and conditional selection.srt -
8. Assignment solution and OneHotEncoding - Part 02.srt -
3. K Fold cross validation without GridSearchCV.srt -
3. Read mode, write mode and methods.srt -
1. Voting classifier.srt -
2. Boosting.srt -
3. DataFrame Selections.srt -
1. Python random class.srt -
1. Euler's number.srt -
1. KNN background.srt -
1. Python decorators.srt -
8. Boxplot and Violin Plot.srt -
2. Random under numpy and Arange.srt -
6. Special class methods.srt -
2. Co-variance.srt -
3. Python generators.srt -
1. R-square.srt -
3. Multiple linear regression behind the scene - Part 2.srt -
1. SVM getting started with 1D data.srt -
3. Python collections.srt -
5. Under and over sampling.srt -
7. Feature selection.srt -
3. Multinomial naive bayes.srt -
2. Error types, else and finally.srt -
5. String Start Stop and Step.srt -
3. Scopes.srt -
4. Slicing and broadcast.srt -
2. Paths.srt -
3. Visualization of decision tree model.srt -
7. Percentiles, moment and Quantiles.srt -
1. User-defined functions.srt -
2. NumPy array functions - Array generate.srt -
1. Regular expression introduction.srt -
2. Decision Tree implementation with 1 feature.srt -
2. SVM, mapping higher dimension.srt -
4. Vector Multiplication.srt -
4. Confusion matrix 3D.srt -
2. Logistic regression background.srt -
2. Jupyter notebook.srt -
5. Break, continue and pass.srt -
4. Greedy, non-greedy matches and findall.srt -
1. Naming conventions and introduction.srt -
6. Gaussian naive Bayes under Python & Visualization of models.srt -
10. Sets.srt -
3. Accuracy, precision, recall, Specificity, F1 Score.srt -
5. Standard deviation.srt -
2. handling missing data.srt -
5. Concatenation.srt -
5. CAP curve background.srt -
15. Logical operators.srt -
3. Random array based methods.srt -
2. Regular expression, grouping and pipe.srt -
1. Why Logistic regression.srt -
1. Introduction to ML & Supervised learning.srt -
2. Matplotlib Bar-graph and multiple plotting.srt -
4. Python counter from collections.srt -
7. Univariate Analysis using PDF.srt -
1. If ElIf & else.srt -
3. Co-relation.srt -
1. Updated template with GridSearchCV.srt -
1. Linear regression working and Cost function.srt -
1. Matplotlib simple plot, line graphs.srt -
3. Pair plot and limitations.srt -
1. The accuracy, not so accurate.srt -
1. Panda series.srt -
3. Gradient decent in 2D and 3D space.srt -
5. Polymorphism.srt -
6. CAP curve implementation.srt -
3. Repetition and range.srt -
6. Matpotlib Wireframe surface plotting.srt -
6. Lambda once again.srt -
9. List shorting, reversing, removing, clear, list of list.srt -
1. Bagging.srt -
1. Multiple linear regression in Python.srt -
3. Inheritance.srt -
4. ROC, AUC - Calculating the optimal threshold (best Accuracy method).srt -
4. args and kwargs.srt -
9. Discussion forum.srt -
0 -
2. Scatter plot on Iris dataset.mp4 -
6. Pre-processing re-visited continues.srt -
9. HeatMap.srt -
4. Logistic regression on multi-class classification.srt -
1. Bias, Variance and overfitting.srt -
7. Sets.srt -
2. RandomizedSearchCV.srt -
4. Matplotlib Scatter plots and Pie charts.srt -
8. Literal matching, Sub and verbose.srt -
4. Tuple unpacking.srt -
2. Class attributes and Methods.srt -
1. AdaBoost and XGBoost regressor.srt -
2. Classification model master template with evaluation and different data set.srt -
1. Setting up.srt -
3. SVM, in 2D space.srt -
2. Class method decorator.srt -
1. AdaBoost and XGBoost classifier.srt -
6. Facetgrid plots.srt -
1. Python packages.srt -
2. While loop.srt -
6. Most common data distributions, PDF and PMF.srt -
7. In.srt -
2. Likelihood vs probability.srt -
5. Matplotlib 3D scatter and simple plot.srt -
1. Try except finally.srt -
3. For loop.srt -
1. Data import.srt -
6. Operations.srt -
4. SVM implementation using python.srt -
1. Data types.srt -
3. Feature selection and Encoding categorical data.srt -
4. Decision Tree implementation - multiple features.srt -
1. Matrices.srt -
4. GroupBy.srt -
4. K Fold cross validation without GridSearchCV continues.srt -
2. Random Forest.srt -
2. Python numbers.srt -
4. String basics.srt -
8. Lists in Python.srt -
1. Ensemble Learning.srt -
2. Confusion matrix.srt -
8. Input and import.srt -
2. ROC, AUC - Evaluating best model.srt -
4. Curse of dimensionality.srt -
7. String formatting.srt -
14. Comparison operators.srt -
12. Dictionary in python.srt -
4. Update Anaconda website updated.srt -
3. Visualization and few more things.srt -
1. Python setting up.srt -
2. Unsupervised learning.srt -
1. Model deployment basics.srt -
2. Prediction using value.srt -
3. Variables and assignment.srt -
1. Thanks for taking this course.srt -
[TGx]Downloaded from torrentgalaxy.to .txt -
1 -
2. User defined packages.mp4 -
3. Type of data.srt -
6. Numpy operations.srt -
5. Identity matrix, matrix inverse properties, transpose of matrix.srt -
2. KNN in python.srt -
5. Math Matrix multiplication.srt -
1. Classification model master template.srt -
3. Pycharm python IDE.srt -
3. Matrix multiplication.srt -
5. KNN on multi class classification.srt -
1. Decision Tree and Random forest.srt -
6. BeginsWith endsWith and dot character continues.srt -
1. K Fold cross validation.srt -
11. Tuples.srt -
2. Balanced vs imbalanced data.srt -
4. LabelEncoding classes.srt -
1. SVM (regression) Background.srt -
3. Linear regression implementation in python - Part 2.srt -
2. Adjusted R-Square.srt -
2. Help function.srt -
3. Logistic regression under python.srt -
4. Mean Mode median.srt -
3. User defined packages continues.srt -
13. None and Bool.srt -
6. String slicing.srt -
2. Matrix operations and scalar operations.srt -
1. Autocomplete on jupyter notebook.srt -
1. Files introduction.srt -
6. Assignment and tips.srt -
4. Tips dataset.srt -
16. Connect on LinkedIn, It's good!.srt -
2. SVR under Python.srt -
2. Master template regression model - Models and evaluation.srt -
8. Short discussion.srt -
5. Logistic regression on multi-class classification under python.srt -
7. About Project files.srt -
2 -
1. Polynomial regression.mp4 -
3 -
2. Updated template with GridSearchCV.mp4 -
4 -
7. CAP curve with multiple models and multi-class.mp4 -
5 -
1. Master template regression model - Data creation.mp4 -
6 -
1. ROC, AUC and PR curve background.mp4 -
7 -
8. Assignment solution and OneHotEncoding - Part 02.mp4 -
8 -
3. ROC, AUC - Calculating the optimal threshold (Youdens method).mp4 -
9 -
2. Polynomial regression on multiple feature dataset.mp4 -
10 -
2. RandomizedSearchCV.mp4 -
11 -
1. Voting classifier.mp4 -
12 -
7. Assignment solution and OneHotEncoding - Part 01.mp4 -
13 -
1. Why Co-relation is important.mp4 -
14 -
5. Pre-processing re-visited.mp4 -
15 -
1. Updated template with GridSearchCV.mp4 -
16 -
7. Feature selection.mp4 -
17 -
2. DataFrame introduction.mp4 -
18 -
4. Test and train data split and Feature scaling.mp4 -
19 -
3. Read mode, write mode and methods.mp4 -
20 -
2. Jupyter notebook.mp4 -
21 -
5. Seaborn plots.mp4 -
22 -
2. Linear regression implementation in python - Part 1.mp4 -
23 -
3. K Fold cross validation without GridSearchCV.mp4 -
24 -
3. Visualization of decision tree model.mp4 -
25 -
7. Percentiles, moment and Quantiles.mp4 -
26 -
6. Gaussian naive Bayes under Python & Visualization of models.mp4 -
27 -
5. Under and over sampling.mp4 -
28 -
5. BeginsWith endsWith and dot character.mp4 -
29 -
1. Python packages.mp4 -
30 -
2. Error types, else and finally.mp4 -
31 -
3. Gradient decent in 2D and 3D space.mp4 -
32 -
4. The log scale.mp4 -
33 -
4. Vector Multiplication.mp4 -
34 -
3. Matplotlib Subplot and histogram.mp4 -
35 -
1. Measuring Entropy & Gini impurity.mp4 -
36 -
4. Multiple, multi level inheritance and MRO.mp4 -
37 -
5. Gaussian naive bayes.mp4 -
38 -
2. Random under numpy and Arange.mp4 -
39 -
1. Python setting up.mp4 -
40 -
3. Python generators.mp4 -
41 -
3. DataFrame Selections.mp4 -
42 -
2. Classification model master template with evaluation and different data set.mp4 -
43 -
3. Multiple linear regression behind the scene - Part 2.mp4 -
44 -
6. Range, enumerate and zip.mp4 -
45 -
4. Confusion matrix 3D.mp4 -
46 -
1. Bayes theorem.mp4 -
47 -
1. Python decorators.mp4 -
48 -
1. Regular expression introduction.mp4 -
49 -
5. Concatenation.mp4 -
50 -
3. Repetition and range.mp4 -
51 -
2. handling missing data.mp4 -
52 -
6. Pre-processing re-visited continues.mp4 -
53 -
16. Connect on LinkedIn, It's good!.mp4 -
54 -
1. Data import.mp4 -
55 -
1. Python random class.mp4 -
56 -
1. Multiple linear regression in Python.mp4 -
57 -
4. ROC, AUC - Calculating the optimal threshold (best Accuracy method).mp4 -
58 -
2. Matplotlib Bar-graph and multiple plotting.mp4 -
59 -
4. K Fold cross validation without GridSearchCV continues.mp4 -
60 -
3. Pair plot and limitations.mp4 -
61 -
1. AdaBoost and XGBoost classifier.mp4 -
62 -
5. Maps, Filters and Lambdas.mp4 -
63 -
6. CAP curve implementation.mp4 -
64 -
1. AdaBoost and XGBoost regressor.mp4 -
65 -
3. Accuracy, precision, recall, Specificity, F1 Score.mp4 -
66 -
6. Special class methods.mp4 -
67 -
3. Multinomial naive bayes.mp4 -
68 -
4. SVM implementation using python.mp4 -
69 -
3. Python collections.mp4 -
70 -
2. Boosting.mp4 -
71 -
2. Paths.mp4 -
72 -
1. KNN background.mp4 -
73 -
9. Discussion forum.mp4 -
74 -
4. Greedy, non-greedy matches and findall.mp4 -
75 -
2. ROC, AUC - Evaluating best model.mp4 -
76 -
5. String Start Stop and Step.mp4 -
77 -
3. Scopes.mp4 -
78 -
2. Gradient decent - Background.mp4 -
79 -
3. Pycharm python IDE.mp4 -
80 -
3. Visualization and few more things.mp4 -
81 -
1. Decision Tree and Random forest.mp4 -
82 -
1. Classification model master template.mp4 -
83 -
7. About Project files.mp4 -
84 -
3. User defined packages continues.mp4 -
85 -
3. Random array based methods.mp4 -
86 -
3. Co-relation.mp4 -
87 -
2. Co-variance.mp4 -
88 -
1. SVM getting started with 1D data.mp4 -
89 -
7. Univariate Analysis using PDF.mp4 -
90 -
6. Matpotlib Wireframe surface plotting.mp4 -
91 -
4. Update Anaconda website updated.mp4 -
92 -
1. Matplotlib simple plot, line graphs.mp4 -
93 -
10. Sets.mp4 -
94 -
5. Matplotlib 3D scatter and simple plot.mp4 -
95 -
5. Matrices selection and conditional selection.mp4 -
96 -
2. Prediction using value.mp4 -
97 -
4. Python counter from collections.mp4 -
98 -
1. Data types.mp4 -
99 -
5. CAP curve background.mp4 -
100 -
2. Decision Tree implementation with 1 feature.mp4 -
101 -
4. Decision Tree implementation - multiple features.mp4 -
102 -
8. Boxplot and Violin Plot.mp4 -
103 -
2. Class method decorator.mp4 -
104 -
1. Euler's number.mp4 -
105 -
2. Random Forest.mp4 -
106 -
1. Ensemble Learning.mp4 -
107 -
2. Unsupervised learning.mp4 -
108 -
2. Likelihood vs probability.mp4 -
109 -
2. Logistic regression background.mp4 -
110 -
1. Naming conventions and introduction.mp4 -
111 -
1. R-square.mp4 -
112 -
6. Most common data distributions, PDF and PMF.mp4 -
113 -
1. Model deployment basics.mp4 -
114 -
1. Why Logistic regression.mp4 -
115 -
4. args and kwargs.mp4 -
116 -
6. Lambda once again.mp4 -
117 -
2. KNN in python.mp4 -
118 -
4. Matplotlib Scatter plots and Pie charts.mp4 -
119 -
3. Feature selection and Encoding categorical data.mp4 -
120 -
2. Regular expression, grouping and pipe.mp4 -
121 -
15. Logical operators.mp4 -
122 -
2. SVM, mapping higher dimension.mp4 -
123 -
1. User-defined functions.mp4 -
124 -
4. LabelEncoding classes.mp4 -
125 -
1. Introduction to ML & Supervised learning.mp4 -
126 -
1. Bagging.mp4 -
127 -
1. The accuracy, not so accurate.mp4 -
128 -
1. If ElIf & else.mp4 -
129 -
1. Setting up.mp4 -
130 -
6. Facetgrid plots.mp4 -
131 -
2. NumPy array functions - Array generate.mp4 -
132 -
3. SVM, in 2D space.mp4 -
133 -
5. KNN on multi class classification.mp4 -
134 -
4. Slicing and broadcast.mp4 -
135 -
9. List shorting, reversing, removing, clear, list of list.mp4 -
136 -
1. Try except finally.mp4 -
137 -
4. Logistic regression on multi-class classification.mp4 -
138 -
9. HeatMap.mp4 -
139 -
1. Panda series.mp4 -
140 -
5. Meet your Author.mp4 -
141 -
1. Bias, Variance and overfitting.mp4 -
142 -
3. Inheritance.mp4 -
143 -
3. Logistic regression under python.mp4 -
144 -
2. Class attributes and Methods.mp4 -
145 -
5. Polymorphism.mp4 -
146 -
6. Numpy operations.mp4 -
147 -
8. Literal matching, Sub and verbose.mp4 -
148 -
1. Autocomplete on jupyter notebook.mp4 -
149 -
7. String formatting.mp4 -
150 -
2. Confusion matrix.mp4 -
151 -
1. Linear regression working and Cost function.mp4 -
152 -
7. Sets.mp4 -
153 -
5. Break, continue and pass.mp4 -
154 -
4. GroupBy.mp4 -
155 -
4. String basics.mp4 -
156 -
3. Linear regression implementation in python - Part 2.mp4 -
157 -
3. For loop.mp4 -
158 -
12. Dictionary in python.mp4 -
159 -
4. Curse of dimensionality.mp4 -
160 -
8. Lists in Python.mp4 -
161 -
6. Operations.mp4 -
162 -
5. Standard deviation.mp4 -
163 -
6. Assignment and tips.mp4 -
164 -
2. While loop.mp4 -
165 -
3. Variables and assignment.mp4 -
166 -
4. Tuple unpacking.mp4 -
167 -
1. Thanks for taking this course.mp4 -
168 -
5. Logistic regression on multi-class classification under python.mp4 -
169 -
2. Python numbers.mp4 -
170 -
5. Identity matrix, matrix inverse properties, transpose of matrix.mp4 -
171 -
6. BeginsWith endsWith and dot character continues.mp4 -
172 -
7. In.mp4 -
173 -
14. Comparison operators.mp4 -
174 -
2. Balanced vs imbalanced data.mp4 -
175 -
8. Input and import.mp4 -
176 -
1. SVM (regression) Background.mp4 -
177 -
11. Tuples.mp4 -
178 -
8. Short discussion.mp4 -
179 -
4. Tips dataset.mp4 -
180 -
5. Math Matrix multiplication.mp4 -
181 -
3. Type of data.mp4 -
182 -
2. Help function.mp4 -
183 -
3. Matrix multiplication.mp4 -
184 -
1. Matrices.mp4 -
185 -
2. Master template regression model - Models and evaluation.mp4 -
186 -
2. SVR under Python.mp4 -
187 -
13. None and Bool.mp4 -
188 -
2. Adjusted R-Square.mp4 -
189 -
6. String slicing.mp4 -
190 -
1. K Fold cross validation.mp4 -
191 -
1. Files introduction.mp4 -
192 -
4. Mean Mode median.mp4 -
193 -
2. Matrix operations and scalar operations.mp4 -
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2. Multiple linear regression behind the scene - Part 1.mp4 -
160.3 MB
TutsNode.com.txt -
63 bytes
2. Polynomial regression on multiple feature dataset.srt -
28.0 KB
4. The log scale.srt -
26.2 KB
5. Seaborn plots.srt -
26.2 KB
6. Range, enumerate and zip.srt -
25.8 KB
2. DataFrame introduction.srt -
25.5 KB
3. Matplotlib Subplot and histogram.srt -
25.4 KB
2. Updated template with GridSearchCV.srt -
24.2 KB
2. Scatter plot on Iris dataset.srt -
23.3 KB
1. Polynomial regression.srt -
23.0 KB
5. Meet your Author.srt -
2.5 KB
6. Linkedin and Instagram links.html -
511 bytes
1. Master template regression model - Data creation.srt -
22.9 KB
1. Bayes theorem.srt -
22.2 KB
2. Linear regression implementation in python - Part 1.srt -
22.0 KB
5. BeginsWith endsWith and dot character.srt -
21.9 KB
1. Measuring Entropy & Gini impurity.srt -
21.3 KB
3. ROC, AUC - Calculating the optimal threshold (Youdens method).srt -
21.0 KB
2. Multiple linear regression behind the scene - Part 1.srt -
21.0 KB
5. Maps, Filters and Lambdas.srt -
20.8 KB
5. Gaussian naive bayes.srt -
20.8 KB
4. Test and train data split and Feature scaling.srt -
20.8 KB
7. Assignment solution and OneHotEncoding - Part 01.srt -
20.2 KB
7. CAP curve with multiple models and multi-class.srt -
20.1 KB
2. User defined packages.srt -
20.1 KB
4. Multiple, multi level inheritance and MRO.srt -
19.9 KB
2. Gradient decent - Background.srt -
19.7 KB
5. Pre-processing re-visited.srt -
19.5 KB
1. Why Co-relation is important.srt -
19.2 KB
5. Matrices selection and conditional selection.srt -
19.2 KB
8. Assignment solution and OneHotEncoding - Part 02.srt -
19.2 KB
3. K Fold cross validation without GridSearchCV.srt -
19.2 KB
3. Read mode, write mode and methods.srt -
19.1 KB
1. Voting classifier.srt -
19.1 KB
2. Boosting.srt -
18.8 KB
3. DataFrame Selections.srt -
18.7 KB
1. Python random class.srt -
18.6 KB
1. Euler's number.srt -
18.1 KB
1. KNN background.srt -
18.1 KB
1. Python decorators.srt -
17.7 KB
8. Boxplot and Violin Plot.srt -
17.6 KB
2. Random under numpy and Arange.srt -
17.6 KB
6. Special class methods.srt -
17.5 KB
2. Co-variance.srt -
17.5 KB
3. Python generators.srt -
17.5 KB
1. R-square.srt -
17.5 KB
3. Multiple linear regression behind the scene - Part 2.srt -
17.4 KB
1. SVM getting started with 1D data.srt -
17.3 KB
3. Python collections.srt -
17.1 KB
5. Under and over sampling.srt -
17.0 KB
7. Feature selection.srt -
17.0 KB
3. Multinomial naive bayes.srt -
16.9 KB
2. Error types, else and finally.srt -
16.8 KB
5. String Start Stop and Step.srt -
16.4 KB
3. Scopes.srt -
16.2 KB
4. Slicing and broadcast.srt -
16.2 KB
2. Paths.srt -
16.0 KB
3. Visualization of decision tree model.srt -
15.9 KB
7. Percentiles, moment and Quantiles.srt -
15.8 KB
1. User-defined functions.srt -
15.8 KB
2. NumPy array functions - Array generate.srt -
15.8 KB
1. Regular expression introduction.srt -
15.6 KB
2. Decision Tree implementation with 1 feature.srt -
15.6 KB
2. SVM, mapping higher dimension.srt -
15.6 KB
4. Vector Multiplication.srt -
15.5 KB
4. Confusion matrix 3D.srt -
15.3 KB
2. Logistic regression background.srt -
15.1 KB
2. Jupyter notebook.srt -
15.1 KB
5. Break, continue and pass.srt -
15.0 KB
4. Greedy, non-greedy matches and findall.srt -
15.0 KB
1. Naming conventions and introduction.srt -
14.8 KB
6. Gaussian naive Bayes under Python & Visualization of models.srt -
14.8 KB
10. Sets.srt -
14.8 KB
3. Accuracy, precision, recall, Specificity, F1 Score.srt -
14.8 KB
5. Standard deviation.srt -
14.8 KB
2. handling missing data.srt -
14.7 KB
5. Concatenation.srt -
14.5 KB
5. CAP curve background.srt -
14.3 KB
15. Logical operators.srt -
14.2 KB
3. Random array based methods.srt -
14.1 KB
2. Regular expression, grouping and pipe.srt -
14.1 KB
1. Why Logistic regression.srt -
14.1 KB
1. Introduction to ML & Supervised learning.srt -
14.0 KB
2. Matplotlib Bar-graph and multiple plotting.srt -
14.0 KB
4. Python counter from collections.srt -
14.0 KB
7. Univariate Analysis using PDF.srt -
14.0 KB
1. If ElIf & else.srt -
14.0 KB
3. Co-relation.srt -
13.8 KB
1. Updated template with GridSearchCV.srt -
13.8 KB
1. Linear regression working and Cost function.srt -
13.6 KB
1. Matplotlib simple plot, line graphs.srt -
13.6 KB
3. Pair plot and limitations.srt -
13.4 KB
1. The accuracy, not so accurate.srt -
13.4 KB
1. Panda series.srt -
13.3 KB
3. Gradient decent in 2D and 3D space.srt -
13.1 KB
5. Polymorphism.srt -
13.0 KB
6. CAP curve implementation.srt -
13.0 KB
3. Repetition and range.srt -
13.0 KB
6. Matpotlib Wireframe surface plotting.srt -
12.9 KB
6. Lambda once again.srt -
12.9 KB
9. List shorting, reversing, removing, clear, list of list.srt -
12.9 KB
1. Bagging.srt -
12.8 KB
1. Multiple linear regression in Python.srt -
12.7 KB
3. Inheritance.srt -
12.6 KB
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1. Model deployment basics.mp4 -
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3. Inheritance.mp4 -
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3. Logistic regression under python.mp4 -
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2. Class attributes and Methods.mp4 -
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5. Polymorphism.mp4 -
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2. Confusion matrix.mp4 -
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1. Linear regression working and Cost function.mp4 -
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8. Lists in Python.mp4 -
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6. Operations.mp4 -
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3. Variables and assignment.mp4 -
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1. Thanks for taking this course.mp4 -
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5. Logistic regression on multi-class classification under python.mp4 -
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11. Tuples.mp4 -
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8. Short discussion.mp4 -
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2. Master template regression model - Models and evaluation.mp4 -
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2. SVR under Python.mp4 -
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13. None and Bool.mp4 -
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2. Adjusted R-Square.mp4 -
21.6 MB
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