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| [CourseClub.Me].url |
122б |
| [CourseClub.Me].url |
122б |
| [CourseClub.Me].url |
122б |
| [CourseClub.Me].url |
122б |
| [CourseClub.Me].url |
122б |
| [CourseClub.ME].url |
122б |
| [GigaCourse.Com].url |
49б |
| [GigaCourse.Com].url |
49б |
| [GigaCourse.Com].url |
49б |
| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
49б |
| 1. ACF and PACF.mp4 |
41.23Мб |
| 1. ACF and PACF.srt |
8.65Кб |
| 1. Basics of Decision Trees.mp4 |
42.64Мб |
| 1. Basics of Decision Trees.srt |
11.27Кб |
| 1. Basic Terminologies.mp4 |
40.42Мб |
| 1. Basic Terminologies.srt |
10.81Кб |
| 1. Boosting.mp4 |
30.58Мб |
| 1. Boosting.srt |
7.81Кб |
| 1. Classification tree.mp4 |
28.20Мб |
| 1. Classification tree.srt |
6.72Кб |
| 1. CNN Introduction.mp4 |
51.16Мб |
| 1. CNN Introduction.srt |
8.13Кб |
| 1. CNN model in Python - Preprocessing.mp4 |
40.63Мб |
| 1. CNN model in Python - Preprocessing.srt |
5.74Кб |
| 1. CNN on MNIST Fashion Dataset - Model Architecture.mp4 |
7.35Мб |
| 1. CNN on MNIST Fashion Dataset - Model Architecture.srt |
2.38Кб |
| 1. Content flow.mp4 |
8.64Мб |
| 1. Content flow.srt |
1.74Кб |
| 1. Data Loading in Python.mp4 |
108.87Мб |
| 1. Data Loading in Python.srt |
17.69Кб |
| 1. Ensemble technique 1 - Bagging.mp4 |
28.14Мб |
| 1. Ensemble technique 1 - Bagging.srt |
7.27Кб |
| 1. Ensemble technique 2 - Random Forests.mp4 |
18.20Мб |
| 1. Ensemble technique 2 - Random Forests.srt |
4.59Кб |
| 1. Gathering Business Knowledge.mp4 |
22.29Мб |
| 1. Gathering Business Knowledge.srt |
4.14Кб |
| 1. ILSVRC.mp4 |
20.93Мб |
| 1. ILSVRC.srt |
4.60Кб |
| 1. Importing Data into R.mp4 |
53.67Мб |
| 1. Importing Data into R.srt |
8.90Кб |
| 1. Installing Keras and Tensorflow.mp4 |
22.79Мб |
| 1. Installing Keras and Tensorflow.srt |
3.01Кб |
| 1. Installing Python and Anaconda.mp4 |
16.27Мб |
| 1. Installing Python and Anaconda.srt |
2.65Кб |
| 1. Installing R and R studio.mp4 |
35.71Мб |
| 1. Installing R and R studio.srt |
6.79Кб |
| 1. Introduction.mp4 |
29.40Мб |
| 1. Introduction.mp4 |
12.27Мб |
| 1. Introduction.srt |
4.49Кб |
| 1. Introduction.srt |
2.18Кб |
| 1. Introduction to Machine Learning.mp4 |
109.18Мб |
| 1. Introduction to Machine Learning.srt |
19.73Кб |
| 1. Introduction to Neural Networks and Course flow.mp4 |
29.07Мб |
| 1. Introduction to Neural Networks and Course flow.srt |
4.77Кб |
| 1. Keras and Tensorflow.mp4 |
14.92Мб |
| 1. Keras and Tensorflow.srt |
3.78Кб |
| 1. Kernel Based Support Vector Machines.mp4 |
40.12Мб |
| 1. Kernel Based Support Vector Machines.srt |
6.71Кб |
| 1. Linear Discriminant Analysis.mp4 |
40.96Мб |
| 1. Linear Discriminant Analysis.srt |
11.89Кб |
| 1. Logistic Regression.mp4 |
32.93Мб |
| 1. Logistic Regression.srt |
8.64Кб |
| 1. Project - Data Augmentation Preprocessing.mp4 |
41.42Мб |
| 1. Project - Data Augmentation Preprocessing.srt |
7.25Кб |
| 1. Project in R - Data Preprocessing.mp4 |
87.76Мб |
| 1. Project in R - Data Preprocessing.srt |
11.89Кб |
| 1. Project - Introduction.mp4 |
49.39Мб |
| 1. Project - Introduction.srt |
7.49Кб |
| 1. Project - Transfer Learning - VGG16 (Implementation).mp4 |
101.57Мб |
| 1. Project - Transfer Learning - VGG16 (Implementation).srt |
14.18Кб |
| 1. Regression and Classification Models.mp4 |
4.04Мб |
| 1. Regression and Classification Models.srt |
810б |
| 1. SARIMA model.mp4 |
39.03Мб |
| 1. SARIMA model.srt |
7.87Кб |
| 1. Support Vector classifiers.mp4 |
56.17Мб |
| 1. Support Vector classifiers.srt |
10.85Кб |
| 1. Test-Train Split.mp4 |
39.30Мб |
| 1. Test-Train Split.srt |
10.59Кб |
| 1. Test Train Split in Python.mp4 |
57.42Мб |
| 1. Test Train Split in Python.srt |
12.05Кб |
| 1. The Data and the Data Dictionary.mp4 |
79.01Мб |
| 1. The Data and the Data Dictionary.srt |
9.32Кб |
| 1. The final milestone!.mp4 |
11.85Мб |
| 1. The final milestone!.srt |
1.73Кб |
| 1. The Problem Statement.mp4 |
9.37Мб |
| 1. The Problem Statement.srt |
1.66Кб |
| 1. Three Classifiers and the problem statement.mp4 |
20.34Мб |
| 1. Three Classifiers and the problem statement.srt |
3.93Кб |
| 1. Types of Data.mp4 |
21.76Мб |
| 1. Types of Data.srt |
5.04Кб |
| 1. Understanding the results of classification models.mp4 |
41.64Мб |
| 1. Understanding the results of classification models.srt |
7.52Кб |
| 1. White Noise.mp4 |
11.37Мб |
| 1. White Noise.srt |
2.52Кб |
| 10. Classification model - Standardizing the data.mp4 |
9.72Мб |
| 10. Classification model - Standardizing the data.srt |
1.89Кб |
| 10. Evaluating performance of model.mp4 |
35.17Мб |
| 10. Evaluating performance of model.srt |
9.38Кб |
| 10. Exponential Smoothing.mp4 |
8.39Мб |
| 10. Exponential Smoothing.srt |
2.10Кб |
| 10. Multiple Linear Regression in Python.mp4 |
69.74Мб |
| 10. Multiple Linear Regression in Python.srt |
14.29Кб |
| 10. Outlier Treatment in Python.mp4 |
70.26Мб |
| 10. Outlier Treatment in Python.srt |
14.12Кб |
| 10. Test-Train split in Python.mp4 |
24.87Мб |
| 10. Test-Train split in Python.srt |
6.17Кб |
| 10. Using Functional API for complex architectures.mp4 |
92.11Мб |
| 10. Using Functional API for complex architectures.srt |
12.95Кб |
| 10. Variable transformation and Deletion in Python.mp4 |
29.26Мб |
| 10. Variable transformation and Deletion in Python.srt |
4.31Кб |
| 10. Working with Seaborn Library of Python.mp4 |
40.37Мб |
| 10. Working with Seaborn Library of Python.srt |
8.24Кб |
| 11. Evaluating model performance in Python.mp4 |
9.02Мб |
| 11. Evaluating model performance in Python.srt |
2.66Кб |
| 11. Multiple Linear Regression in R.mp4 |
62.38Мб |
| 11. Multiple Linear Regression in R.srt |
9.19Кб |
| 11. Outlier Treatment in R.mp4 |
30.74Мб |
| 11. Outlier Treatment in R.srt |
4.89Кб |
| 11. Saving - Restoring Models and Using Callbacks.mp4 |
151.59Мб |
| 11. Saving - Restoring Models and Using Callbacks.srt |
20.83Кб |
| 11. Splitting Data into Test and Train Set in R.mp4 |
43.98Мб |
| 11. Splitting Data into Test and Train Set in R.srt |
5.83Кб |
| 11. SVM Based classification model.mp4 |
64.13Мб |
| 11. SVM Based classification model.srt |
12.39Кб |
| 11. Variable transformation in R.mp4 |
38.03Мб |
| 11. Variable transformation in R.srt |
6.77Кб |
| 12. Creating Decision tree in Python.mp4 |
17.87Мб |
| 12. Creating Decision tree in Python.srt |
4.31Кб |
| 12. Dummy variable creation in Python.mp4 |
26.37Мб |
| 12. Dummy variable creation in Python.srt |
6.15Кб |
| 12. Hyperparameter Tuning.mp4 |
60.63Мб |
| 12. Hyper Parameter Tuning.mp4 |
57.74Мб |
| 12. Hyperparameter Tuning.srt |
9.81Кб |
| 12. Hyper Parameter Tuning.srt |
10.79Кб |
| 12. Missing Value Imputation.mp4 |
25.00Мб |
| 12. Missing Value Imputation.srt |
4.23Кб |
| 12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 |
55.70Мб |
| 12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt |
7.41Кб |
| 12. Test-train split.mp4 |
41.88Мб |
| 12. Test-train split.srt |
10.88Кб |
| 13. Bias Variance trade-off.mp4 |
25.09Мб |
| 13. Bias Variance trade-off.srt |
6.95Кб |
| 13. Building a Regression Tree in R.mp4 |
103.34Мб |
| 13. Building a Regression Tree in R.srt |
15.50Кб |
| 13. Dummy variable creation in R.mp4 |
44.36Мб |
| 13. Dummy variable creation in R.srt |
6.48Кб |
| 13. Missing Value Imputation in Python.mp4 |
23.42Мб |
| 13. Missing Value Imputation in Python.srt |
4.77Кб |
| 13. Polynomial Kernel with Hyperparameter Tuning.mp4 |
22.92Мб |
| 13. Polynomial Kernel with Hyperparameter Tuning.srt |
4.49Кб |
| 14. Evaluating model performance in Python.mp4 |
16.44Мб |
| 14. Evaluating model performance in Python.srt |
4.73Кб |
| 14. Missing Value imputation in R.mp4 |
26.01Мб |
| 14. Missing Value imputation in R.srt |
4.06Кб |
| 14. Radial Kernel with Hyperparameter Tuning.mp4 |
37.21Мб |
| 14. Radial Kernel with Hyperparameter Tuning.srt |
7.26Кб |
| 14. Test train split in Python.mp4 |
44.88Мб |
| 14. Test train split in Python.srt |
8.74Кб |
| 15. Plotting decision tree in Python.mp4 |
21.48Мб |
| 15. Plotting decision tree in Python.srt |
5.29Кб |
| 15. Seasonality in Data.mp4 |
17.02Мб |
| 15. Seasonality in Data.srt |
3.97Кб |
| 15. Test-Train Split in R.mp4 |
75.60Мб |
| 15. Test-Train Split in R.srt |
9.36Кб |
| 16. Bi-variate analysis and Variable transformation.mp4 |
100.40Мб |
| 16. Bi-variate analysis and Variable transformation.srt |
19.33Кб |
| 16. Pruning a tree.mp4 |
18.46Мб |
| 16. Pruning a tree.srt |
4.54Кб |
| 16. Regression models other than OLS.mp4 |
16.55Мб |
| 16. Regression models other than OLS.srt |
4.75Кб |
| 17. Pruning a tree in Python.mp4 |
73.50Мб |
| 17. Pruning a tree in Python.srt |
10.72Кб |
| 17. Subset selection techniques.mp4 |
79.07Мб |
| 17. Subset selection techniques.srt |
13.68Кб |
| 17. Variable transformation and deletion in Python.mp4 |
44.12Мб |
| 17. Variable transformation and deletion in Python.srt |
9.02Кб |
| 18. Pruning a Tree in R.mp4 |
82.10Мб |
| 18. Pruning a Tree in R.srt |
9.66Кб |
| 18. Subset selection in R.mp4 |
63.53Мб |
| 18. Subset selection in R.srt |
8.22Кб |
| 18. Variable transformation in R.mp4 |
55.43Мб |
| 18. Variable transformation in R.srt |
9.94Кб |
| 19. Non-usable variables.mp4 |
20.25Мб |
| 19. Non-usable variables.srt |
6.03Кб |
| 19. Shrinkage methods Ridge and Lasso.mp4 |
33.34Мб |
| 19. Shrinkage methods Ridge and Lasso.srt |
8.98Кб |
| 2. ARIMA model - Basics.mp4 |
21.37Мб |
| 2. ARIMA model - Basics.srt |
5.10Кб |
| 2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 |
43.37Мб |
| 2. Basic Equations and Ordinary Least Squares (OLS) method.srt |
10.44Кб |
| 2. Basics of R and R studio.mp4 |
38.85Мб |
| 2. Basics of R and R studio.srt |
11.97Кб |
| 2. Building a Machine Learning Model.mp4 |
39.48Мб |
| 2. Building a Machine Learning Model.srt |
10.25Кб |
| 2. CNN model in Python - structure and Compile.mp4 |
43.26Мб |
| 2. CNN model in Python - structure and Compile.srt |
7.27Кб |
| 2. CNN Project in R - Structure and Compile.mp4 |
46.12Мб |
| 2. CNN Project in R - Structure and Compile.srt |
5.55Кб |
| 2. Congratulations & About your certificate.html |
1.60Кб |
| 2. Course Resources.html |
370б |
| 2. Data Exploration.mp4 |
20.51Мб |
| 2. Data Exploration.srt |
3.88Кб |
| 2. Data for the project.html |
232б |
| 2. Data Import in Python.mp4 |
22.06Мб |
| 2. Data Import in Python.srt |
5.28Кб |
| 2. Data Normalization and Test-Train Split.mp4 |
111.78Мб |
| 2. Data Normalization and Test-Train Split.srt |
12.87Кб |
| 2. Data Preprocessing.mp4 |
67.03Мб |
| 2. Data Preprocessing.srt |
7.46Кб |
| 2. Ensemble technique 1 - Bagging in Python.mp4 |
77.30Мб |
| 2. Ensemble technique 1 - Bagging in Python.srt |
12.28Кб |
| 2. Ensemble technique 2 - Random Forests in Python.mp4 |
46.70Мб |
| 2. Ensemble technique 2 - Random Forests in Python.srt |
6.69Кб |
| 2. Ensemble technique 3a - Boosting in Python.mp4 |
39.88Мб |
| 2. Ensemble technique 3a - Boosting in Python.srt |
5.44Кб |
| 2. Gradient Descent.mp4 |
60.34Мб |
| 2. Gradient Descent.srt |
12.70Кб |
| 2. Installing Tensorflow and Keras.mp4 |
20.06Мб |
| 2. Installing Tensorflow and Keras.srt |
4.14Кб |
| 2. LDA in Python.mp4 |
11.40Мб |
| 2. LDA in Python.srt |
2.57Кб |
| 2. LeNET.mp4 |
7.00Мб |
| 2. LeNET.srt |
1.85Кб |
| 2. Limitations of Support Vector Classifiers.mp4 |
10.80Мб |
| 2. Limitations of Support Vector Classifiers.srt |
1.62Кб |
| 2. Naive (Persistence) model in Python.mp4 |
43.38Мб |
| 2. Naive (Persistence) model in Python.srt |
8.17Кб |
| 2. Perceptron.mp4 |
44.75Мб |
| 2. Perceptron.srt |
10.22Кб |
| 2. Project - Data Augmentation Training and Results.mp4 |
53.04Мб |
| 2. Project - Data Augmentation Training and Results.srt |
6.81Кб |
| 2. Project - Transfer Learning - VGG16 (Performance).mp4 |
64.11Мб |
| 2. Project - Transfer Learning - VGG16 (Performance).srt |
8.81Кб |
| 2. Random Walk.mp4 |
21.17Мб |
| 2. Random Walk.srt |
4.59Кб |
| 2. SARIMA model in Python.mp4 |
66.23Мб |
| 2. SARIMA model in Python.srt |
11.58Кб |
| 2. Stride.mp4 |
16.58Мб |
| 2. Stride.srt |
3.01Кб |
| 2. Summary of the three models.mp4 |
22.22Мб |
| 2. Summary of the three models.srt |
5.96Кб |
| 2. Test-Train Split.mp4 |
50.48Мб |
| 2. Test-Train Split.srt |
6.04Кб |
| 2. Test-Train Split in Python.mp4 |
33.10Мб |
| 2. Test-Train Split in Python.srt |
7.39Кб |
| 2. The Concept of a Hyperplane.mp4 |
29.42Мб |
| 2. The Concept of a Hyperplane.srt |
5.31Кб |
| 2. The Data set for Classification problem.mp4 |
18.57Мб |
| 2. The Data set for Classification problem.srt |
1.91Кб |
| 2. The Data set for the Regression problem.mp4 |
37.20Мб |
| 2. The Data set for the Regression problem.srt |
3.28Кб |
| 2. This is a milestone!.mp4 |
20.66Мб |
| 2. This is a milestone!.srt |
3.78Кб |
| 2. Time Series Forecasting - Use cases.mp4 |
25.92Мб |
| 2. Time Series Forecasting - Use cases.srt |
2.51Кб |
| 2. Time Series - Visualization Basics.mp4 |
63.72Мб |
| 2. Time Series - Visualization Basics.srt |
10.25Кб |
| 2. Training a Simple Logistic Model in Python.mp4 |
47.87Мб |
| 2. Training a Simple Logistic Model in Python.srt |
10.63Кб |
| 2. Types of Statistics.mp4 |
10.94Мб |
| 2. Types of Statistics.srt |
3.17Кб |
| 2. Understanding a Regression Tree.mp4 |
43.72Мб |
| 2. Understanding a Regression Tree.srt |
11.91Кб |
| 2. Why can't we use Linear Regression.mp4 |
16.94Мб |
| 2. Why can't we use Linear Regression.srt |
5.49Кб |
| 20. Dummy variable creation Handling qualitative data.mp4 |
36.81Мб |
| 20. Dummy variable creation Handling qualitative data.srt |
5.77Кб |
| 20. Ridge regression and Lasso in Python.mp4 |
128.85Мб |
| 20. Ridge regression and Lasso in Python.srt |
20.90Кб |
| 21. Dummy variable creation in Python.mp4 |
26.53Мб |
| 21. Dummy variable creation in Python.srt |
6.21Кб |
| 21. Ridge regression and Lasso in R.mp4 |
103.43Мб |
| 21. Ridge regression and Lasso in R.srt |
12.38Кб |
| 22. Dummy variable creation in R.mp4 |
43.99Мб |
| 22. Dummy variable creation in R.srt |
6.09Кб |
| 22. Heteroscedasticity.mp4 |
14.49Мб |
| 22. Heteroscedasticity.srt |
2.82Кб |
| 23. Correlation Analysis.mp4 |
71.60Мб |
| 23. Correlation Analysis.srt |
11.91Кб |
| 24. Correlation Analysis in Python.mp4 |
55.30Мб |
| 24. Correlation Analysis in Python.srt |
6.96Кб |
| 25. Correlation Matrix in R.mp4 |
83.13Мб |
| 25. Correlation Matrix in R.srt |
9.58Кб |
| 26. Quiz.html |
170б |
| 3. Activation Functions.mp4 |
34.62Мб |
| 3. Activation Functions.srt |
8.17Кб |
| 3. ARIMA model in Python.mp4 |
74.44Мб |
| 3. ARIMA model in Python.srt |
14.30Кб |
| 3. Assessing accuracy of predicted coefficients.mp4 |
92.11Мб |
| 3. Assessing accuracy of predicted coefficients.srt |
17.40Кб |
| 3. Auto Regression Model - Basics.mp4 |
16.89Мб |
| 3. Auto Regression Model - Basics.srt |
3.64Кб |
| 3. Back Propagation.mp4 |
122.20Мб |
| 3. Back Propagation.srt |
24.77Кб |
| 3. Bagging in R.mp4 |
58.96Мб |
| 3. Bagging in R.srt |
7.13Кб |
| 3. Building,Compiling and Training.mp4 |
130.74Мб |
| 3. Building,Compiling and Training.srt |
16.27Кб |
| 3. Classification tree in Python Preprocessing.mp4 |
45.38Мб |
| 3. Classification tree in Python Preprocessing.srt |
8.92Кб |
| 3. CNN model in Python - Training and results.mp4 |
55.15Мб |
| 3. CNN model in Python - Training and results.srt |
6.41Кб |
| 3. Creating Model Architecture.mp4 |
71.60Мб |
| 3. Creating Model Architecture.srt |
6.29Кб |
| 3. Dataset for classification.mp4 |
56.19Мб |
| 3. Dataset for classification.srt |
7.90Кб |
| 3. Decomposing Time Series in Python.mp4 |
59.84Мб |
| 3. Decomposing Time Series in Python.srt |
10.43Кб |
| 3. Describing data Graphically.mp4 |
65.40Мб |
| 3. Describing data Graphically.srt |
12.77Кб |
| 3. Forecasting model creation - Steps.mp4 |
10.11Мб |
| 3. Forecasting model creation - Steps.srt |
2.92Кб |
| 3. Gradient Boosting in R.mp4 |
69.09Мб |
| 3. Gradient Boosting in R.srt |
8.55Кб |
| 3. Importing data for regression model.mp4 |
25.84Мб |
| 3. Importing data for regression model.srt |
5.88Кб |
| 3. Importing the dataset into R.mp4 |
13.47Мб |
| 3. Importing the dataset into R.srt |
2.81Кб |
| 3. Linear Discriminant Analysis in R.mp4 |
74.36Мб |
| 3. Linear Discriminant Analysis in R.srt |
10.22Кб |
| 3. Maximum Margin Classifier.mp4 |
22.48Мб |
| 3. Maximum Margin Classifier.srt |
3.46Кб |
| 3. More about test-train split.html |
559б |
| 3. Opening Jupyter Notebook.mp4 |
65.19Мб |
| 3. Opening Jupyter Notebook.srt |
9.84Кб |
| 3. Packages in R.mp4 |
82.95Мб |
| 3. Packages in R.srt |
12.24Кб |
| 3. Padding.mp4 |
31.63Мб |
| 3. Padding.srt |
4.95Кб |
| 3. Project - Data Preprocessing in Python.mp4 |
71.83Мб |
| 3. Project - Data Preprocessing in Python.srt |
9.16Кб |
| 3. Project in R - Training.mp4 |
24.58Мб |
| 3. Project in R - Training.srt |
3.16Кб |
| 3. Stationary time Series.mp4 |
5.58Мб |
| 3. Stationary time Series.srt |
1.70Кб |
| 3. Test-Train Split in R.mp4 |
74.23Мб |
| 3. Test-Train Split in R.srt |
9.81Кб |
| 3. The Dataset and the Data Dictionary.mp4 |
69.29Мб |
| 3. The Dataset and the Data Dictionary.srt |
8.75Кб |
| 3. The stopping criteria for controlling tree growth.mp4 |
13.98Мб |
| 3. The stopping criteria for controlling tree growth.srt |
3.51Кб |
| 3. Time Series - Visualization in Python.mp4 |
165.20Мб |
| 3. Time Series - Visualization in Python.srt |
28.94Кб |
| 3. Training a Simple Logistic model in R.mp4 |
25.57Мб |
| 3. Training a Simple Logistic model in R.srt |
4.21Кб |
| 3. Using Grid Search in Python.mp4 |
80.67Мб |
| 3. Using Grid Search in Python.srt |
13.69Кб |
| 3. VGG16NET.mp4 |
10.35Мб |
| 3. VGG16NET.srt |
1.98Кб |
| 4. ARIMA model with Walk Forward Validation in Python.mp4 |
32.15Мб |
| 4. ARIMA model with Walk Forward Validation in Python.srt |
6.20Кб |
| 4. Assessing Model Accuracy RSE and R squared.mp4 |
43.60Мб |
| 4. Assessing Model Accuracy RSE and R squared.srt |
8.37Кб |
| 4. Auto Regression Model creation in Python.mp4 |
53.49Мб |
| 4. Auto Regression Model creation in Python.srt |
10.20Кб |
| 4. Classification SVM model using Linear Kernel.mp4 |
139.16Мб |
| 4. Classification SVM model using Linear Kernel.srt |
17.75Кб |
| 4. Classification tree in Python Training.mp4 |
82.72Мб |
| 4. Classification tree in Python Training.srt |
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| 4. Comparison - Pooling vs Without Pooling in Python.mp4 |
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| 4. Comparison - Pooling vs Without Pooling in Python.srt |
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| 4. Compiling and training.mp4 |
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| 4. Compiling and training.srt |
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| 4. Differencing.mp4 |
32.35Мб |
| 4. Differencing.srt |
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| 4. EDD in Python.mp4 |
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| 4. EDD in Python.srt |
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| 4. Ensemble technique 3b - AdaBoost in Python.mp4 |
30.54Мб |
| 4. Ensemble technique 3b - AdaBoost in Python.srt |
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| 4. Evaluating and Predicting.mp4 |
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| 4. Evaluating and Predicting.srt |
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| 4. Filters and Feature maps.mp4 |
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| 4. Filters and Feature maps.srt |
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| 4. Forecasting model creation - Steps 1 (Goal).mp4 |
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| 4. Forecasting model creation - Steps 1 (Goal).srt |
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| 4. GoogLeNet.mp4 |
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| 4. GoogLeNet.srt |
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| 4. Importing Data in Python.mp4 |
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| 4. Importing Data in Python.srt |
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| 4. Inputting data part 1 Inbuilt datasets of R.mp4 |
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| 4. Inputting data part 1 Inbuilt datasets of R.srt |
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| 4. Introduction to Jupyter.mp4 |
40.92Мб |
| 4. Introduction to Jupyter.srt |
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| 4. K-Nearest Neighbors classifier.mp4 |
75.42Мб |
| 4. K-Nearest Neighbors classifier.srt |
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| 4. Limitations of Maximum Margin Classifier.mp4 |
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| 4. Limitations of Maximum Margin Classifier.srt |
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| 4. Measures of Centers.mp4 |
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| 4. Measures of Centers.srt |
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| 4. Normalization and Test-Train split.mp4 |
44.20Мб |
| 4. Normalization and Test-Train split.srt |
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| 4. Project in R - Model Performance.mp4 |
23.18Мб |
| 4. Project in R - Model Performance.srt |
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| 4. Project - Training CNN model in Python.mp4 |
65.98Мб |
| 4. Project - Training CNN model in Python.srt |
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| 4. Python - Creating Perceptron model.mp4 |
86.56Мб |
| 4. Python - Creating Perceptron model.srt |
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| 4. Random Forest in R.mp4 |
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| 4. Random Forest in R.srt |
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| 4. Result of Simple Logistic Regression.mp4 |
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| 4. Result of Simple Logistic Regression.srt |
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| 4. Some Important Concepts.mp4 |
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| 4. Some Important Concepts.srt |
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| 4. The Data set for this part.mp4 |
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| 4. The Data set for this part.srt |
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| 4. Time Series - Feature Engineering Basics.mp4 |
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| 4. Time Series - Feature Engineering Basics.srt |
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| 4. X-y Split.mp4 |
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| 4. X-y Split.srt |
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| 5. AdaBoosting in R.mp4 |
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| 5. AdaBoosting in R.srt |
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| 5. ANN with NeuralNets Package.mp4 |
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| 5. ANN with NeuralNets Package.srt |
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| 5. Arithmetic operators in Python Python Basics.mp4 |
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| 5. Arithmetic operators in Python Python Basics.srt |
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| 5. Auto Regression with Walk Forward validation in Python.mp4 |
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| 5. Auto Regression with Walk Forward validation in Python.srt |
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| 5. Building a classification Tree in R.mp4 |
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| 5. Building a classification Tree in R.srt |
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| 5. Channels.mp4 |
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| 5. Channels.srt |
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| 5. Differencing in Python.mp4 |
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| 5. Differencing in Python.srt |
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| 5. Different ways to create ANN using Keras.mp4 |
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| 5. Different ways to create ANN using Keras.srt |
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| 5. EDD in R.mp4 |
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| 5. EDD in R.srt |
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| 5. Hyperparameter.mp4 |
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| 5. Hyperparameter.srt |
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| 5. Hyperparameter Tuning for Linear Kernel.mp4 |
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| 5. Hyperparameter Tuning for Linear Kernel.srt |
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| 5. Importing the Data set into Python.mp4 |
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| 5. Importing the Data set into Python.srt |
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| 5. Importing the dataset into R.mp4 |
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| 5. Importing the dataset into R.srt |
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| 5. Inputting data part 2 Manual data entry.mp4 |
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| 5. Inputting data part 2 Manual data entry.srt |
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| 5. K-Nearest Neighbors in Python Part 1.mp4 |
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| 5. K-Nearest Neighbors in Python Part 1.srt |
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| 5. Logistic with multiple predictors.mp4 |
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| 5. Logistic with multiple predictors.srt |
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| 5. Measures of Dispersion.mp4 |
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| 5. Measures of Dispersion.srt |
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| 5. Model Performance.mp4 |
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| 5. Model Performance.srt |
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| 5. Project in Python - model results.mp4 |
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| 5. Project in Python - model results.srt |
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| 5. Project in R - Data Augmentation.mp4 |
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| 5. Project in R - Data Augmentation.srt |
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| 5. Simple Linear Regression in Python.mp4 |
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| 5. Simple Linear Regression in Python.srt |
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| 5. Test-Train Split.mp4 |
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| 5. Test-Train Split.srt |
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| 5. Time Series - Basic Notations.mp4 |
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| 5. Time Series - Basic Notations.srt |
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| 5. Time Series - Feature Engineering in Python.mp4 |
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| 5. Time Series - Feature Engineering in Python.srt |
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| 5. Transfer Learning.mp4 |
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| 5. Transfer Learning.srt |
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| 6. Advantages and Disadvantages of Decision Trees.mp4 |
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| 6. Advantages and Disadvantages of Decision Trees.srt |
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| 6. Building Regression Model with Functional API.mp4 |
131.13Мб |
| 6. Building Regression Model with Functional API.srt |
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| 6. Building the Neural Network using Keras.mp4 |
79.11Мб |
| 6. Building the Neural Network using Keras.srt |
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| 6. Comparison - Pooling vs Without Pooling in R.mp4 |
44.60Мб |
| 6. Comparison - Pooling vs Without Pooling in R.srt |
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| 6. Ensemble technique 3c - XGBoost in Python.mp4 |
75.01Мб |
| 6. Ensemble technique 3c - XGBoost in Python.srt |
11.43Кб |
| 6. Importing the Data set into R.mp4 |
43.70Мб |
| 6. Importing the Data set into R.srt |
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| 6. Inputting data part 3 Importing from CSV or Text files.mp4 |
60.11Мб |
| 6. Inputting data part 3 Importing from CSV or Text files.srt |
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| 6. K-Nearest Neighbors in Python Part 2.mp4 |
42.36Мб |
| 6. K-Nearest Neighbors in Python Part 2.srt |
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| 6. Moving Average model -Basics.mp4 |
24.10Мб |
| 6. Moving Average model -Basics.srt |
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| 6. Outlier treatment in Python.mp4 |
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| 6. Outlier treatment in Python.srt |
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| 6. Polynomial Kernel with Hyperparameter Tuning.mp4 |
83.14Мб |
| 6. Polynomial Kernel with Hyperparameter Tuning.srt |
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| 6. PoolingLayer.mp4 |
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| 6. PoolingLayer.srt |
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| 6. Project in R - Validation Performance.mp4 |
23.69Мб |
| 6. Project in R - Validation Performance.srt |
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| 6. Project - Transfer Learning - VGG16.mp4 |
129.10Мб |
| 6. Project - Transfer Learning - VGG16.srt |
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| 6. Simple Linear Regression in R.mp4 |
40.83Мб |
| 6. Simple Linear Regression in R.srt |
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| 6. Standardizing the data.mp4 |
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| 6. Standardizing the data.srt |
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| 6. Strings in Python Python Basics.mp4 |
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| 6. Strings in Python Python Basics.srt |
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| 6. Time Series - Upsampling and Downsampling.mp4 |
16.96Мб |
| 6. Time Series - Upsampling and Downsampling.srt |
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| 6. Training multiple predictor Logistic model in Python.mp4 |
26.25Мб |
| 6. Training multiple predictor Logistic model in Python.srt |
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| 6. Univariate analysis and EDD.mp4 |
24.19Мб |
| 6. Univariate analysis and EDD.srt |
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| 7. Compiling and Training the Neural Network model.mp4 |
81.63Мб |
| 7. Compiling and Training the Neural Network model.srt |
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| 7. Complex Architectures using Functional API.mp4 |
79.57Мб |
| 7. Complex Architectures using Functional API.srt |
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| 7. Creating Barplots in R.mp4 |
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| 7. Creating Barplots in R.srt |
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| 7. EDD in Python.mp4 |
61.81Мб |
| 7. EDD in Python.srt |
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| 7. K-Nearest Neighbors in R.mp4 |
64.85Мб |
| 7. K-Nearest Neighbors in R.srt |
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| 7. Lists, Tuples and Directories Python Basics.mp4 |
60.33Мб |
| 7. Lists, Tuples and Directories Python Basics.srt |
20.11Кб |
| 7. Missing value treatment in Python.mp4 |
17.93Мб |
| 7. Missing value treatment in Python.srt |
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| 7. Moving Average model in Python.mp4 |
56.65Мб |
| 7. Moving Average model in Python.srt |
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| 7. Multiple Linear Regression.mp4 |
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| 7. Multiple Linear Regression.srt |
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| 7. Outlier Treatment in R.mp4 |
25.37Мб |
| 7. Outlier Treatment in R.srt |
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| 7. Radial Kernel with Hyperparameter Tuning.mp4 |
56.68Мб |
| 7. Radial Kernel with Hyperparameter Tuning.srt |
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| 7. SVM based Regression Model in Python.mp4 |
67.64Мб |
| 7. SVM based Regression Model in Python.srt |
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| 7. Time Series - Upsampling and Downsampling in Python.mp4 |
100.67Мб |
| 7. Time Series - Upsampling and Downsampling in Python.srt |
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| 7. Training multiple predictor Logistic model in R.mp4 |
15.78Мб |
| 7. Training multiple predictor Logistic model in R.srt |
2.02Кб |
| 7. XGBoosting in R.mp4 |
161.30Мб |
| 7. XGBoosting in R.srt |
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| 8. Confusion Matrix.mp4 |
21.10Мб |
| 8. Confusion Matrix.srt |
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| 8. Creating Histograms in R.mp4 |
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| 8. Creating Histograms in R.srt |
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| 8. Dummy Variable creation in Python.mp4 |
24.94Мб |
| 8. Dummy Variable creation in Python.srt |
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| 8. EDD in R.mp4 |
96.98Мб |
| 8. EDD in R.srt |
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| 8. Evaluating performance and Predicting using Keras.mp4 |
69.91Мб |
| 8. Evaluating performance and Predicting using Keras.srt |
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| 8. Missing Value Imputation in Python.mp4 |
22.56Мб |
| 8. Missing Value Imputation in Python.srt |
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| 8. Saving - Restoring Models and Using Callbacks.mp4 |
216.03Мб |
| 8. Saving - Restoring Models and Using Callbacks.srt |
21.38Кб |
| 8. SVM based Regression Model in R.mp4 |
106.12Мб |
| 8. SVM based Regression Model in R.srt |
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| 8. The Data set for the Classification problem.mp4 |
18.56Мб |
| 8. The Data set for the Classification problem.srt |
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| 8. The F - statistic.mp4 |
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| 8. The F - statistic.srt |
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| 8. Time Series - Power Transformation.mp4 |
14.86Мб |
| 8. Time Series - Power Transformation.srt |
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| 8. Working with Numpy Library of Python.mp4 |
43.88Мб |
| 8. Working with Numpy Library of Python.srt |
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| 9. Building Neural Network for Regression Problem.mp4 |
155.90Мб |
| 9. Building Neural Network for Regression Problem.srt |
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| 9. Classification model - Preprocessing.mp4 |
45.38Мб |
| 9. Classification model - Preprocessing.srt |
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| 9. Creating Confusion Matrix in Python.mp4 |
51.25Мб |
| 9. Creating Confusion Matrix in Python.srt |
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| 9. Dependent- Independent Data split in Python.mp4 |
15.18Мб |
| 9. Dependent- Independent Data split in Python.srt |
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| 9. Interpreting results of Categorical variables.mp4 |
22.50Мб |
| 9. Interpreting results of Categorical variables.srt |
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| 9. Missing Value imputation in R.mp4 |
19.05Мб |
| 9. Missing Value imputation in R.srt |
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| 9. Moving Average.mp4 |
38.71Мб |
| 9. Moving Average.srt |
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| 9. Outlier Treatment.mp4 |
24.50Мб |
| 9. Outlier Treatment.srt |
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| 9. Working with Pandas Library of Python.mp4 |
46.88Мб |
| 9. Working with Pandas Library of Python.srt |
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