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Category:
Language:
English
Total Size:
11.7 GB
Info Hash:
89A416054201781C60DF1B3747D9F7E42DD48357
Added By:
Added:
Oct. 26, 2023, 9:06 a.m.
Stats:
|
(Last updated: Nov. 23, 2025, 10:33 a.m.)
| File | Size |
|---|---|
| 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 |
| 4. ROC, AUC - Calculating the optimal threshold (best Accuracy method).srt | 12.6 KB |
| 4. args and kwargs.srt | 12.5 KB |
| 9. Discussion forum.srt | 3.8 KB |
| 0 | 61 bytes |
| 2. Scatter plot on Iris dataset.mp4 | 153.4 MB |
| 6. Pre-processing re-visited continues.srt | 12.5 KB |
| 9. HeatMap.srt | 12.4 KB |
| 4. Logistic regression on multi-class classification.srt | 12.4 KB |
| 1. Bias, Variance and overfitting.srt | 12.3 KB |
| 7. Sets.srt | 12.2 KB |
| 2. RandomizedSearchCV.srt | 12.1 KB |
| 4. Matplotlib Scatter plots and Pie charts.srt | 12.0 KB |
| 8. Literal matching, Sub and verbose.srt | 12.0 KB |
| 4. Tuple unpacking.srt | 12.0 KB |
| 2. Class attributes and Methods.srt | 11.9 KB |
| 1. AdaBoost and XGBoost regressor.srt | 11.9 KB |
| 2. Classification model master template with evaluation and different data set.srt | 11.9 KB |
| 1. Setting up.srt | 11.6 KB |
| 3. SVM, in 2D space.srt | 11.6 KB |
| 2. Class method decorator.srt | 11.5 KB |
| 1. AdaBoost and XGBoost classifier.srt | 11.4 KB |
| 6. Facetgrid plots.srt | 11.4 KB |
| 1. Python packages.srt | 11.1 KB |
| 2. While loop.srt | 11.1 KB |
| 6. Most common data distributions, PDF and PMF.srt | 11.0 KB |
| 7. In.srt | 11.0 KB |
| 2. Likelihood vs probability.srt | 10.9 KB |
| 5. Matplotlib 3D scatter and simple plot.srt | 10.8 KB |
| 1. Try except finally.srt | 10.8 KB |
| 3. For loop.srt | 10.7 KB |
| 1. Data import.srt | 10.6 KB |
| 6. Operations.srt | 10.4 KB |
| 4. SVM implementation using python.srt | 10.4 KB |
| 1. Data types.srt | 10.3 KB |
| 3. Feature selection and Encoding categorical data.srt | 10.2 KB |
| 4. Decision Tree implementation - multiple features.srt | 10.2 KB |
| 1. Matrices.srt | 10.2 KB |
| 4. GroupBy.srt | 10.2 KB |
| 4. K Fold cross validation without GridSearchCV continues.srt | 10.2 KB |
| 2. Random Forest.srt | 10.1 KB |
| 2. Python numbers.srt | 10.1 KB |
| 4. String basics.srt | 10.0 KB |
| 8. Lists in Python.srt | 10.0 KB |
| 1. Ensemble Learning.srt | 9.9 KB |
| 2. Confusion matrix.srt | 9.7 KB |
| 8. Input and import.srt | 9.7 KB |
| 2. ROC, AUC - Evaluating best model.srt | 9.7 KB |
| 4. Curse of dimensionality.srt | 9.7 KB |
| 7. String formatting.srt | 9.6 KB |
| 14. Comparison operators.srt | 9.6 KB |
| 12. Dictionary in python.srt | 9.5 KB |
| 4. Update Anaconda website updated.srt | 9.3 KB |
| 3. Visualization and few more things.srt | 9.3 KB |
| 1. Python setting up.srt | 9.3 KB |
| 2. Unsupervised learning.srt | 9.2 KB |
| 1. Model deployment basics.srt | 9.2 KB |
| 2. Prediction using value.srt | 9.2 KB |
| 3. Variables and assignment.srt | 9.1 KB |
| 1. Thanks for taking this course.srt | 1.8 KB |
| [TGx]Downloaded from torrentgalaxy.to .txt | 585 bytes |
| 1 | 506 bytes |
| 2. User defined packages.mp4 | 144.4 MB |
| 3. Type of data.srt | 9.1 KB |
| 6. Numpy operations.srt | 9.1 KB |
| 5. Identity matrix, matrix inverse properties, transpose of matrix.srt | 9.0 KB |
| 2. KNN in python.srt | 8.9 KB |
| 5. Math Matrix multiplication.srt | 8.8 KB |
| 1. Classification model master template.srt | 8.8 KB |
| 3. Pycharm python IDE.srt | 8.8 KB |
| 3. Matrix multiplication.srt | 8.5 KB |
| 5. KNN on multi class classification.srt | 8.5 KB |
| 1. Decision Tree and Random forest.srt | 8.5 KB |
| 6. BeginsWith endsWith and dot character continues.srt | 8.5 KB |
| 1. K Fold cross validation.srt | 8.4 KB |
| 11. Tuples.srt | 8.3 KB |
| 2. Balanced vs imbalanced data.srt | 8.3 KB |
| 4. LabelEncoding classes.srt | 8.2 KB |
| 1. SVM (regression) Background.srt | 7.9 KB |
| 3. Linear regression implementation in python - Part 2.srt | 7.8 KB |
| 2. Adjusted R-Square.srt | 7.4 KB |
| 2. Help function.srt | 7.3 KB |
| 3. Logistic regression under python.srt | 6.8 KB |
| 4. Mean Mode median.srt | 6.7 KB |
| 3. User defined packages continues.srt | 6.6 KB |
| 13. None and Bool.srt | 6.5 KB |
| 6. String slicing.srt | 5.9 KB |
| 2. Matrix operations and scalar operations.srt | 5.8 KB |
| 1. Autocomplete on jupyter notebook.srt | 5.8 KB |
| 1. Files introduction.srt | 4.8 KB |
| 6. Assignment and tips.srt | 4.5 KB |
| 4. Tips dataset.srt | 4.2 KB |
| 16. Connect on LinkedIn, It's good!.srt | 4.1 KB |
| 2. SVR under Python.srt | 4.0 KB |
| 2. Master template regression model - Models and evaluation.srt | 3.9 KB |
| 8. Short discussion.srt | 3.7 KB |
| 5. Logistic regression on multi-class classification under python.srt | 3.7 KB |
| 7. About Project files.srt | 3.2 KB |
| 2 | 1.3 MB |
| 1. Polynomial regression.mp4 | 143.8 MB |
| 3 | 187.9 KB |
| 2. Updated template with GridSearchCV.mp4 | 143.3 MB |
| 4 | 701.2 KB |
| 7. CAP curve with multiple models and multi-class.mp4 | 135.7 MB |
| 5 | 335.0 KB |
| 1. Master template regression model - Data creation.mp4 | 134.7 MB |
| 6 | 1.3 MB |
| 1. ROC, AUC and PR curve background.mp4 | 131.4 MB |
| 7 | 571.6 KB |
| 8. Assignment solution and OneHotEncoding - Part 02.mp4 | 126.2 MB |
| 8 | 1.8 MB |
| 3. ROC, AUC - Calculating the optimal threshold (Youdens method).mp4 | 124.4 MB |
| 9 | 1.6 MB |
| 2. Polynomial regression on multiple feature dataset.mp4 | 119.3 MB |
| 10 | 748.1 KB |
| 2. RandomizedSearchCV.mp4 | 115.4 MB |
| 11 | 608.7 KB |
| 1. Voting classifier.mp4 | 114.7 MB |
| 12 | 1.3 MB |
| 7. Assignment solution and OneHotEncoding - Part 01.mp4 | 113.2 MB |
| 13 | 809.1 KB |
| 1. Why Co-relation is important.mp4 | 110.5 MB |
| 14 | 1.5 MB |
| 5. Pre-processing re-visited.mp4 | 110.4 MB |
| 15 | 1.6 MB |
| 1. Updated template with GridSearchCV.mp4 | 109.0 MB |
| 16 | 1022.8 KB |
| 7. Feature selection.mp4 | 106.1 MB |
| 17 | 1.9 MB |
| 2. DataFrame introduction.mp4 | 98.1 MB |
| 18 | 1.9 MB |
| 4. Test and train data split and Feature scaling.mp4 | 97.9 MB |
| 19 | 54.2 KB |
| 3. Read mode, write mode and methods.mp4 | 97.1 MB |
| 20 | 958.6 KB |
| 2. Jupyter notebook.mp4 | 95.4 MB |
| 21 | 584.4 KB |
| 5. Seaborn plots.mp4 | 95.3 MB |
| 22 | 725.4 KB |
| 2. Linear regression implementation in python - Part 1.mp4 | 92.5 MB |
| 23 | 1.5 MB |
| 3. K Fold cross validation without GridSearchCV.mp4 | 91.9 MB |
| 24 | 89.6 KB |
| 3. Visualization of decision tree model.mp4 | 89.3 MB |
| 25 | 726.7 KB |
| 7. Percentiles, moment and Quantiles.mp4 | 88.8 MB |
| 26 | 1.2 MB |
| 6. Gaussian naive Bayes under Python & Visualization of models.mp4 | 88.5 MB |
| 27 | 1.5 MB |
| 5. Under and over sampling.mp4 | 87.6 MB |
| 28 | 368.9 KB |
| 5. BeginsWith endsWith and dot character.mp4 | 86.9 MB |
| 29 | 1.1 MB |
| 1. Python packages.mp4 | 86.8 MB |
| 30 | 1.2 MB |
| 2. Error types, else and finally.mp4 | 86.3 MB |
| 31 | 1.7 MB |
| 3. Gradient decent in 2D and 3D space.mp4 | 85.1 MB |
| 32 | 879.8 KB |
| 4. The log scale.mp4 | 83.6 MB |
| 33 | 365.6 KB |
| 4. Vector Multiplication.mp4 | 82.9 MB |
| 34 | 1.1 MB |
| 3. Matplotlib Subplot and histogram.mp4 | 82.5 MB |
| 35 | 1.5 MB |
| 1. Measuring Entropy & Gini impurity.mp4 | 81.9 MB |
| 36 | 59.3 KB |
| 4. Multiple, multi level inheritance and MRO.mp4 | 79.8 MB |
| 37 | 238.9 KB |
| 5. Gaussian naive bayes.mp4 | 77.4 MB |
| 38 | 583.7 KB |
| 2. Random under numpy and Arange.mp4 | 77.1 MB |
| 39 | 934.4 KB |
| 1. Python setting up.mp4 | 76.7 MB |
| 40 | 1.3 MB |
| 3. Python generators.mp4 | 76.1 MB |
| 41 | 1.9 MB |
| 3. DataFrame Selections.mp4 | 75.7 MB |
| 42 | 355.0 KB |
| 2. Classification model master template with evaluation and different data set.mp4 | 75.2 MB |
| 43 | 869.1 KB |
| 3. Multiple linear regression behind the scene - Part 2.mp4 | 75.1 MB |
| 44 | 922.4 KB |
| 6. Range, enumerate and zip.mp4 | 75.0 MB |
| 45 | 1.0 MB |
| 4. Confusion matrix 3D.mp4 | 75.0 MB |
| 46 | 1.0 MB |
| 1. Bayes theorem.mp4 | 73.8 MB |
| 47 | 216.1 KB |
| 1. Python decorators.mp4 | 72.8 MB |
| 48 | 1.2 MB |
| 1. Regular expression introduction.mp4 | 72.5 MB |
| 49 | 1.5 MB |
| 5. Concatenation.mp4 | 72.3 MB |
| 50 | 1.7 MB |
| 3. Repetition and range.mp4 | 71.6 MB |
| 51 | 456.4 KB |
| 2. handling missing data.mp4 | 71.5 MB |
| 52 | 465.7 KB |
| 6. Pre-processing re-visited continues.mp4 | 71.2 MB |
| 53 | 813.6 KB |
| 16. Connect on LinkedIn, It's good!.mp4 | 71.0 MB |
| 54 | 982.7 KB |
| 1. Data import.mp4 | 71.0 MB |
| 55 | 983.4 KB |
| 1. Python random class.mp4 | 70.6 MB |
| 56 | 1.4 MB |
| 1. Multiple linear regression in Python.mp4 | 69.6 MB |
| 57 | 397.4 KB |
| 4. ROC, AUC - Calculating the optimal threshold (best Accuracy method).mp4 | 69.3 MB |
| 58 | 674.3 KB |
| 2. Matplotlib Bar-graph and multiple plotting.mp4 | 68.6 MB |
| 59 | 1.4 MB |
| 4. K Fold cross validation without GridSearchCV continues.mp4 | 68.4 MB |
| 60 | 1.6 MB |
| 3. Pair plot and limitations.mp4 | 67.8 MB |
| 61 | 174.2 KB |
| 1. AdaBoost and XGBoost classifier.mp4 | 67.7 MB |
| 62 | 348.5 KB |
| 5. Maps, Filters and Lambdas.mp4 | 67.6 MB |
| 63 | 456.0 KB |
| 6. CAP curve implementation.mp4 | 67.0 MB |
| 64 | 1.0 MB |
| 1. AdaBoost and XGBoost regressor.mp4 | 67.0 MB |
| 65 | 1.0 MB |
| 3. Accuracy, precision, recall, Specificity, F1 Score.mp4 | 66.1 MB |
| 66 | 1.9 MB |
| 6. Special class methods.mp4 | 65.8 MB |
| 67 | 193.3 KB |
| 3. Multinomial naive bayes.mp4 | 65.0 MB |
| 68 | 1017.4 KB |
| 4. SVM implementation using python.mp4 | 64.2 MB |
| 69 | 1.8 MB |
| 3. Python collections.mp4 | 64.0 MB |
| 70 | 2.0 MB |
| 2. Boosting.mp4 | 63.9 MB |
| 71 | 75.4 KB |
| 2. Paths.mp4 | 62.8 MB |
| 72 | 1.2 MB |
| 1. KNN background.mp4 | 62.3 MB |
| 73 | 1.7 MB |
| 9. Discussion forum.mp4 | 61.6 MB |
| 74 | 393.0 KB |
| 4. Greedy, non-greedy matches and findall.mp4 | 61.5 MB |
| 75 | 562.0 KB |
| 2. ROC, AUC - Evaluating best model.mp4 | 61.1 MB |
| 76 | 905.2 KB |
| 5. String Start Stop and Step.mp4 | 61.0 MB |
| 77 | 1006.2 KB |
| 3. Scopes.mp4 | 61.0 MB |
| 78 | 1.0 MB |
| 2. Gradient decent - Background.mp4 | 60.5 MB |
| 79 | 1.5 MB |
| 3. Pycharm python IDE.mp4 | 60.5 MB |
| 80 | 1.5 MB |
| 3. Visualization and few more things.mp4 | 59.8 MB |
| 81 | 182.1 KB |
| 1. Decision Tree and Random forest.mp4 | 59.5 MB |
| 82 | 524.5 KB |
| 1. Classification model master template.mp4 | 57.9 MB |
| 83 | 104.8 KB |
| 7. About Project files.mp4 | 57.7 MB |
| 84 | 356.6 KB |
| 3. User defined packages continues.mp4 | 57.6 MB |
| 85 | 421.8 KB |
| 3. Random array based methods.mp4 | 57.4 MB |
| 86 | 617.1 KB |
| 3. Co-relation.mp4 | 57.4 MB |
| 87 | 623.2 KB |
| 2. Co-variance.mp4 | 57.3 MB |
| 88 | 675.3 KB |
| 1. SVM getting started with 1D data.mp4 | 57.3 MB |
| 89 | 710.4 KB |
| 7. Univariate Analysis using PDF.mp4 | 57.3 MB |
| 90 | 729.8 KB |
| 6. Matpotlib Wireframe surface plotting.mp4 | 57.0 MB |
| 91 | 1000.1 KB |
| 4. Update Anaconda website updated.mp4 | 56.0 MB |
| 92 | 2.0 MB |
| 1. Matplotlib simple plot, line graphs.mp4 | 54.6 MB |
| 93 | 1.4 MB |
| 10. Sets.mp4 | 54.5 MB |
| 94 | 1.5 MB |
| 5. Matplotlib 3D scatter and simple plot.mp4 | 54.5 MB |
| 95 | 1.5 MB |
| 5. Matrices selection and conditional selection.mp4 | 54.4 MB |
| 96 | 1.6 MB |
| 2. Prediction using value.mp4 | 54.3 MB |
| 97 | 1.7 MB |
| 4. Python counter from collections.mp4 | 54.2 MB |
| 98 | 1.8 MB |
| 1. Data types.mp4 | 54.0 MB |
| 99 | 2.0 MB |
| 5. CAP curve background.mp4 | 53.9 MB |
| 100 | 102.0 KB |
| 2. Decision Tree implementation with 1 feature.mp4 | 53.8 MB |
| 101 | 200.5 KB |
| 4. Decision Tree implementation - multiple features.mp4 | 53.7 MB |
| 102 | 259.1 KB |
| 8. Boxplot and Violin Plot.mp4 | 53.1 MB |
| 103 | 952.5 KB |
| 2. Class method decorator.mp4 | 52.8 MB |
| 104 | 1.2 MB |
| 1. Euler's number.mp4 | 52.7 MB |
| 105 | 1.3 MB |
| 2. Random Forest.mp4 | 52.6 MB |
| 106 | 1.4 MB |
| 1. Ensemble Learning.mp4 | 52.6 MB |
| 107 | 1.4 MB |
| 2. Unsupervised learning.mp4 | 52.5 MB |
| 108 | 1.5 MB |
| 2. Likelihood vs probability.mp4 | 52.3 MB |
| 109 | 1.7 MB |
| 2. Logistic regression background.mp4 | 52.0 MB |
| 110 | 2.0 MB |
| 1. Naming conventions and introduction.mp4 | 52.0 MB |
| 111 | 28.5 KB |
| 1. R-square.mp4 | 51.7 MB |
| 112 | 347.9 KB |
| 6. Most common data distributions, PDF and PMF.mp4 | 51.4 MB |
| 113 | 569.4 KB |
| 1. Model deployment basics.mp4 | 51.4 MB |
| 114 | 622.9 KB |
| 1. Why Logistic regression.mp4 | 51.0 MB |
| 115 | 999.5 KB |
| 4. args and kwargs.mp4 | 51.0 MB |
| 116 | 1.0 MB |
| 6. Lambda once again.mp4 | 49.7 MB |
| 117 | 331.0 KB |
| 2. KNN in python.mp4 | 48.7 MB |
| 118 | 1.3 MB |
| 4. Matplotlib Scatter plots and Pie charts.mp4 | 48.5 MB |
| 119 | 1.5 MB |
| 3. Feature selection and Encoding categorical data.mp4 | 48.2 MB |
| 120 | 1.8 MB |
| 2. Regular expression, grouping and pipe.mp4 | 48.2 MB |
| 121 | 1.8 MB |
| 15. Logical operators.mp4 | 47.9 MB |
| 122 | 118.0 KB |
| 2. SVM, mapping higher dimension.mp4 | 47.8 MB |
| 123 | 205.7 KB |
| 1. User-defined functions.mp4 | 47.5 MB |
| 124 | 544.7 KB |
| 4. LabelEncoding classes.mp4 | 47.2 MB |
| 125 | 863.7 KB |
| 1. Introduction to ML & Supervised learning.mp4 | 46.9 MB |
| 126 | 1.1 MB |
| 1. Bagging.mp4 | 46.1 MB |
| 127 | 1.9 MB |
| 1. The accuracy, not so accurate.mp4 | 46.0 MB |
| 128 | 2.0 MB |
| 1. If ElIf & else.mp4 | 45.3 MB |
| 129 | 702.5 KB |
| 1. Setting up.mp4 | 45.0 MB |
| 130 | 1.0 MB |
| 6. Facetgrid plots.mp4 | 44.7 MB |
| 131 | 1.3 MB |
| 2. NumPy array functions - Array generate.mp4 | 44.6 MB |
| 132 | 1.4 MB |
| 3. SVM, in 2D space.mp4 | 44.6 MB |
| 133 | 1.4 MB |
| 5. KNN on multi class classification.mp4 | 44.3 MB |
| 134 | 1.7 MB |
| 4. Slicing and broadcast.mp4 | 44.1 MB |
| 135 | 1.9 MB |
| 9. List shorting, reversing, removing, clear, list of list.mp4 | 44.1 MB |
| 136 | 1.9 MB |
| 1. Try except finally.mp4 | 43.1 MB |
| 137 | 953.9 KB |
| 4. Logistic regression on multi-class classification.mp4 | 42.8 MB |
| 138 | 1.2 MB |
| 9. HeatMap.mp4 | 42.8 MB |
| 139 | 1.2 MB |
| 1. Panda series.mp4 | 42.3 MB |
| 140 | 1.7 MB |
| 5. Meet your Author.mp4 | 42.1 MB |
| 141 | 1.9 MB |
| 1. Bias, Variance and overfitting.mp4 | 42.1 MB |
| 142 | 1.9 MB |
| 3. Inheritance.mp4 | 42.0 MB |
| 143 | 14.6 KB |
| 3. Logistic regression under python.mp4 | 41.7 MB |
| 144 | 323.1 KB |
| 2. Class attributes and Methods.mp4 | 41.2 MB |
| 145 | 826.4 KB |
| 5. Polymorphism.mp4 | 41.1 MB |
| 146 | 884.9 KB |
| 6. Numpy operations.mp4 | 40.7 MB |
| 147 | 1.3 MB |
| 8. Literal matching, Sub and verbose.mp4 | 39.9 MB |
| 148 | 138.0 KB |
| 1. Autocomplete on jupyter notebook.mp4 | 38.7 MB |
| 149 | 1.3 MB |
| 7. String formatting.mp4 | 38.5 MB |
| 150 | 1.5 MB |
| 2. Confusion matrix.mp4 | 38.2 MB |
| 151 | 1.8 MB |
| 1. Linear regression working and Cost function.mp4 | 37.6 MB |
| 152 | 412.2 KB |
| 7. Sets.mp4 | 37.5 MB |
| 153 | 506.6 KB |
| 5. Break, continue and pass.mp4 | 37.3 MB |
| 154 | 680.0 KB |
| 4. GroupBy.mp4 | 37.3 MB |
| 155 | 732.7 KB |
| 4. String basics.mp4 | 35.3 MB |
| 156 | 725.9 KB |
| 3. Linear regression implementation in python - Part 2.mp4 | 35.3 MB |
| 157 | 747.9 KB |
| 3. For loop.mp4 | 35.0 MB |
| 158 | 1023.2 KB |
| 12. Dictionary in python.mp4 | 34.2 MB |
| 159 | 1.8 MB |
| 4. Curse of dimensionality.mp4 | 33.8 MB |
| 160 | 194.8 KB |
| 8. Lists in Python.mp4 | 33.4 MB |
| 161 | 660.4 KB |
| 6. Operations.mp4 | 33.0 MB |
| 162 | 1.0 MB |
| 5. Standard deviation.mp4 | 32.8 MB |
| 163 | 1.2 MB |
| 6. Assignment and tips.mp4 | 32.6 MB |
| 164 | 1.4 MB |
| 2. While loop.mp4 | 32.5 MB |
| 165 | 1.5 MB |
| 3. Variables and assignment.mp4 | 31.8 MB |
| 166 | 193.2 KB |
| 4. Tuple unpacking.mp4 | 31.3 MB |
| 167 | 741.6 KB |
| 1. Thanks for taking this course.mp4 | 29.8 MB |
| 168 | 239.7 KB |
| 5. Logistic regression on multi-class classification under python.mp4 | 29.5 MB |
| 169 | 480.1 KB |
| 2. Python numbers.mp4 | 28.9 MB |
| 170 | 1.1 MB |
| 5. Identity matrix, matrix inverse properties, transpose of matrix.mp4 | 28.8 MB |
| 171 | 1.2 MB |
| 6. BeginsWith endsWith and dot character continues.mp4 | 28.7 MB |
| 172 | 1.3 MB |
| 7. In.mp4 | 28.5 MB |
| 173 | 1.5 MB |
| 14. Comparison operators.mp4 | 28.3 MB |
| 174 | 1.7 MB |
| 2. Balanced vs imbalanced data.mp4 | 28.1 MB |
| 175 | 1.9 MB |
| 8. Input and import.mp4 | 28.0 MB |
| 176 | 3.0 KB |
| 1. SVM (regression) Background.mp4 | 26.9 MB |
| 177 | 1.1 MB |
| 11. Tuples.mp4 | 26.7 MB |
| 178 | 1.3 MB |
| 8. Short discussion.mp4 | 26.3 MB |
| 179 | 1.7 MB |
| 4. Tips dataset.mp4 | 26.1 MB |
| 180 | 1.9 MB |
| 5. Math Matrix multiplication.mp4 | 24.1 MB |
| 181 | 1.9 MB |
| 3. Type of data.mp4 | 23.8 MB |
| 182 | 249.4 KB |
| 2. Help function.mp4 | 23.6 MB |
| 183 | 421.5 KB |
| 3. Matrix multiplication.mp4 | 23.4 MB |
| 184 | 628.6 KB |
| 1. Matrices.mp4 | 23.3 MB |
| 185 | 701.2 KB |
| 2. Master template regression model - Models and evaluation.mp4 | 22.0 MB |
| 186 | 24.4 KB |
| 2. SVR under Python.mp4 | 21.7 MB |
| 187 | 273.4 KB |
| 13. None and Bool.mp4 | 21.7 MB |
| 188 | 343.8 KB |
| 2. Adjusted R-Square.mp4 | 21.6 MB |
| 189 | 446.1 KB |
| 6. String slicing.mp4 | 20.0 MB |
| 190 | 2.0 MB |
| 1. K Fold cross validation.mp4 | 20.0 MB |
| 191 | 2.0 MB |
| 1. Files introduction.mp4 | 16.8 MB |
| 192 | 1.2 MB |
| 4. Mean Mode median.mp4 | 16.0 MB |
| 193 | 42.2 KB |
| 2. Matrix operations and scalar operations.mp4 | 14.0 MB |
Name
DL
Uploader
Size
S/L
Added
-
358.7 MB
[23
/
9]
2025-04-03
| Uploaded by freecoursewb | Size 358.7 MB | Health [ 23 /9 ] | Added 2025-04-03 |
-
1.6 GB
[18
/
3]
2023-10-30
| Uploaded by FreeCourseWeb | Size 1.6 GB | Health [ 18 /3 ] | Added 2023-10-30 |
NOTE
SOURCE: Udemy MACHINE LEARNING MASTER CLASS AI MADE EASY Zero to Hero
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