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Please note that this page does not hosts or makes available any of the listed filenames. You
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| [CourseClub.Me].url |
122B |
| [CourseClub.Me].url |
122B |
| [GigaCourse.Com].url |
49B |
| [GigaCourse.Com].url |
49B |
| 1.1 Classification preprocessed data Python.csv |
40.97KB |
| 1.2 Classification preprocessed data R.csv |
40.97KB |
| 1. ACF and PACF.mp4 |
41.23MB |
| 1. ACF and PACF.srt |
8.92KB |
| 1. Basic Terminologies.mp4 |
40.42MB |
| 1. Basic Terminologies.srt |
11.35KB |
| 1. Bonus Lecture.html |
2.32KB |
| 1. Boosting.mp4 |
30.58MB |
| 1. Boosting.srt |
9.58KB |
| 1. Classification tree.mp4 |
28.20MB |
| 1. Classification tree.srt |
8.11KB |
| 1. CNN Introduction.mp4 |
56.75MB |
| 1. CNN Introduction.srt |
8.33KB |
| 1. CNN model in Python - Preprocessing.mp4 |
40.63MB |
| 1. CNN model in Python - Preprocessing.srt |
5.88KB |
| 1. CNN on MNIST Fashion Dataset - Model Architecture.mp4 |
7.35MB |
| 1. CNN on MNIST Fashion Dataset - Model Architecture.srt |
2.47KB |
| 1. Data Loading in Python.mp4 |
108.86MB |
| 1. Data Loading in Python.srt |
18.51KB |
| 1. Ensemble technique 1 - Bagging.mp4 |
28.14MB |
| 1. Ensemble technique 1 - Bagging.srt |
7.58KB |
| 1. Ensemble technique 2 - Random Forests.mp4 |
18.20MB |
| 1. Ensemble technique 2 - Random Forests.srt |
5.07KB |
| 1. Gathering Business Knowledge.mp4 |
14.52MB |
| 1. Gathering Business Knowledge.srt |
3.80KB |
| 1. ILSVRC.mp4 |
20.92MB |
| 1. ILSVRC.srt |
4.73KB |
| 1. Importing and preprocessing data in R.mp4 |
24.99MB |
| 1. Importing and preprocessing data in R.srt |
2.81KB |
| 1. Installing Keras and Tensorflow.mp4 |
22.78MB |
| 1. Installing Keras and Tensorflow.srt |
3.11KB |
| 1. Installing Python and Anaconda.mp4 |
16.27MB |
| 1. Installing Python and Anaconda.srt |
2.67KB |
| 1. Installing R and R studio.mp4 |
35.71MB |
| 1. Installing R and R studio.srt |
7.37KB |
| 1. Introduction.mp4 |
29.39MB |
| 1. Introduction.mp4 |
18.68MB |
| 1. Introduction.srt |
4.64KB |
| 1. Introduction.srt |
2.88KB |
| 1. Introduction to Decision trees.mp4 |
44.78MB |
| 1. Introduction to Decision trees.srt |
4.74KB |
| 1. Introduction to Machine Learning.mp4 |
109.17MB |
| 1. Introduction to Machine Learning.srt |
19.38KB |
| 1. Introduction to Neural Networks and Course flow.mp4 |
29.07MB |
| 1. Introduction to Neural Networks and Course flow.srt |
4.96KB |
| 1. Introduction to SVM's.mp4 |
21.62MB |
| 1. Introduction to SVM's.srt |
3.26KB |
| 1. Keras and Tensorflow.mp4 |
14.91MB |
| 1. Keras and Tensorflow.srt |
3.89KB |
| 1. Kernel Based Support Vector Machines.mp4 |
40.12MB |
| 1. Kernel Based Support Vector Machines.srt |
8.46KB |
| 1. Linear Discriminant Analysis.mp4 |
40.96MB |
| 1. Linear Discriminant Analysis.srt |
12.29KB |
| 1. Logistic Regression.mp4 |
32.92MB |
| 1. Logistic Regression.srt |
8.92KB |
| 1. Project - Data Augmentation Preprocessing.mp4 |
41.41MB |
| 1. Project - Data Augmentation Preprocessing.srt |
7.53KB |
| 1. Project in R - Data Preprocessing.mp4 |
87.76MB |
| 1. Project in R - Data Preprocessing.srt |
12.47KB |
| 1. Project - Introduction.mp4 |
49.39MB |
| 1. Project - Introduction.srt |
7.75KB |
| 1. Project - Transfer Learning - VGG16 (Implementation).mp4 |
101.58MB |
| 1. Project - Transfer Learning - VGG16 (Implementation).srt |
14.81KB |
| 1. Regression and Classification Models.mp4 |
4.03MB |
| 1. Regression and Classification Models.srt |
817B |
| 1. SARIMA model.mp4 |
39.02MB |
| 1. SARIMA model.srt |
8.17KB |
| 1. Support Vector classifiers.mp4 |
56.16MB |
| 1. Support Vector classifiers.srt |
12.46KB |
| 1. Test-Train Split.mp4 |
39.30MB |
| 1. Test-Train Split.srt |
10.97KB |
| 1. Test Train Split in Python.mp4 |
57.41MB |
| 1. Test Train Split in Python.srt |
12.33KB |
| 1. The Problem Statement.mp4 |
9.38MB |
| 1. The Problem Statement.srt |
1.84KB |
| 1. Three classification models and Data set.mp4 |
52.27MB |
| 1. Three classification models and Data set.srt |
6.93KB |
| 1. Types of Data.mp4 |
21.77MB |
| 1. Types of Data.srt |
5.20KB |
| 1. Understanding the results of classification models.mp4 |
41.64MB |
| 1. Understanding the results of classification models.srt |
7.80KB |
| 1. White Noise.mp4 |
11.37MB |
| 1. White Noise.srt |
2.61KB |
| 10. Evaluating performance of model.mp4 |
35.16MB |
| 10. Evaluating performance of model.srt |
9.67KB |
| 10. Exponential Smoothing.mp4 |
8.38MB |
| 10. Exponential Smoothing.srt |
2.17KB |
| 10. Multiple Linear Regression in Python.mp4 |
69.73MB |
| 10. Multiple Linear Regression in Python.srt |
14.42KB |
| 10. Outlier Treatment in Python.mp4 |
70.25MB |
| 10. Outlier Treatment in Python.srt |
14.49KB |
| 10. Radial Kernel with Hyperparameter Tuning.mp4 |
37.21MB |
| 10. Radial Kernel with Hyperparameter Tuning.srt |
7.23KB |
| 10. Test-Train split in Python.mp4 |
25.62MB |
| 10. Test-Train split in Python.srt |
5.29KB |
| 10. Using Functional API for complex architectures.mp4 |
92.10MB |
| 10. Using Functional API for complex architectures.srt |
13.37KB |
| 10. Working with Seaborn Library of Python.mp4 |
40.37MB |
| 10. Working with Seaborn Library of Python.srt |
9.08KB |
| 11. Evaluating model performance in Python.mp4 |
9.01MB |
| 11. Evaluating model performance in Python.srt |
2.62KB |
| 11. Multiple Linear Regression in R.mp4 |
62.37MB |
| 11. Multiple Linear Regression in R.srt |
9.56KB |
| 11. Outlier Treatment in R.mp4 |
30.75MB |
| 11. Outlier Treatment in R.srt |
4.91KB |
| 11. Saving - Restoring Models and Using Callbacks.mp4 |
151.58MB |
| 11. Saving - Restoring Models and Using Callbacks.srt |
21.59KB |
| 11. Splitting Data into Test and Train Set in R.mp4 |
43.97MB |
| 11. Splitting Data into Test and Train Set in R.srt |
7.29KB |
| 12. Creating Decision tree in Python.mp4 |
17.87MB |
| 12. Creating Decision tree in Python.srt |
4.34KB |
| 12. Hyperparameter Tuning.mp4 |
60.63MB |
| 12. Hyperparameter Tuning.srt |
10.17KB |
| 12. Missing Value Imputation.mp4 |
23.15MB |
| 12. Missing Value Imputation.srt |
4.25KB |
| 12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 |
55.70MB |
| 12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt |
7.65KB |
| 12. Test-train split.mp4 |
41.89MB |
| 12. Test-train split.srt |
12.64KB |
| 13. Bias Variance trade-off.mp4 |
25.10MB |
| 13. Bias Variance trade-off.srt |
8.20KB |
| 13. Building a Regression Tree in R.mp4 |
103.33MB |
| 13. Building a Regression Tree in R.srt |
18.88KB |
| 13. Missing Value Imputation in Python.mp4 |
23.42MB |
| 13. Missing Value Imputation in Python.srt |
4.73KB |
| 14. Evaluating model performance in Python.mp4 |
16.44MB |
| 14. Evaluating model performance in Python.srt |
4.81KB |
| 14. Missing Value imputation in R.mp4 |
26.00MB |
| 14. Missing Value imputation in R.srt |
4.11KB |
| 14. Test train split in Python.mp4 |
44.89MB |
| 14. Test train split in Python.srt |
8.82KB |
| 15. Plotting decision tree in Python.mp4 |
21.48MB |
| 15. Plotting decision tree in Python.srt |
5.48KB |
| 15. Seasonality in Data.mp4 |
17.02MB |
| 15. Seasonality in Data.srt |
4.11KB |
| 15. Test-Train Split in R.mp4 |
75.60MB |
| 15. Test-Train Split in R.srt |
9.61KB |
| 16. Bi-variate analysis and Variable transformation.mp4 |
100.39MB |
| 16. Bi-variate analysis and Variable transformation.srt |
20.20KB |
| 16. Pruning a tree.mp4 |
18.46MB |
| 16. Pruning a tree.srt |
5.42KB |
| 16. Regression models other than OLS.mp4 |
16.55MB |
| 16. Regression models other than OLS.srt |
5.28KB |
| 17. Pruning a tree in Python.mp4 |
73.50MB |
| 17. Pruning a tree in Python.srt |
11.06KB |
| 17. Subset selection techniques.mp4 |
79.06MB |
| 17. Subset selection techniques.srt |
15.28KB |
| 17. Variable transformation and deletion in Python.mp4 |
44.11MB |
| 17. Variable transformation and deletion in Python.srt |
9.32KB |
| 18. Pruning a Tree in R.mp4 |
82.09MB |
| 18. Pruning a Tree in R.srt |
11.80KB |
| 18. Subset selection in R.mp4 |
63.54MB |
| 18. Subset selection in R.srt |
8.37KB |
| 18. Variable transformation in R.mp4 |
55.42MB |
| 18. Variable transformation in R.srt |
10.18KB |
| 19. Non-usable variables.mp4 |
20.24MB |
| 19. Non-usable variables.srt |
6.27KB |
| 19. Shrinkage methods Ridge and Lasso.mp4 |
33.34MB |
| 19. Shrinkage methods Ridge and Lasso.srt |
9.41KB |
| 2.1 Classification preprocessed data Python.csv |
40.97KB |
| 2. ARIMA model - Basics.mp4 |
21.36MB |
| 2. ARIMA model - Basics.srt |
5.25KB |
| 2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 |
43.38MB |
| 2. Basic Equations and Ordinary Least Squares (OLS) method.srt |
12.66KB |
| 2. Basics of Decision Trees.mp4 |
42.65MB |
| 2. Basics of Decision Trees.srt |
13.19KB |
| 2. Basics of R and R studio.mp4 |
38.84MB |
| 2. Basics of R and R studio.srt |
14.35KB |
| 2. Building a Machine Learning Model.mp4 |
39.48MB |
| 2. Building a Machine Learning Model.srt |
10.22KB |
| 2. CNN model in Python - structure and Compile.mp4 |
43.26MB |
| 2. CNN model in Python - structure and Compile.srt |
7.53KB |
| 2. CNN Project in R - Structure and Compile.mp4 |
46.11MB |
| 2. CNN Project in R - Structure and Compile.srt |
5.82KB |
| 2. Course Resources.html |
370B |
| 2. Data Exploration.mp4 |
20.12MB |
| 2. Data Exploration.srt |
3.82KB |
| 2. Data for the project.html |
232B |
| 2. Data Normalization and Test-Train Split.mp4 |
111.78MB |
| 2. Data Normalization and Test-Train Split.srt |
13.36KB |
| 2. Data Preprocessing.mp4 |
67.02MB |
| 2. Data Preprocessing.srt |
7.76KB |
| 2. Ensemble technique 1 - Bagging in Python.mp4 |
77.31MB |
| 2. Ensemble technique 1 - Bagging in Python.srt |
12.61KB |
| 2. Ensemble technique 2 - Random Forests in Python.mp4 |
46.70MB |
| 2. Ensemble technique 2 - Random Forests in Python.srt |
6.90KB |
| 2. Ensemble technique 3a - Boosting in Python.mp4 |
39.87MB |
| 2. Ensemble technique 3a - Boosting in Python.srt |
5.61KB |
| 2. Gradient Descent.mp4 |
60.34MB |
| 2. Gradient Descent.srt |
13.24KB |
| 2. Importing and preprocessing data in Python.mp4 |
26.45MB |
| 2. Importing and preprocessing data in Python.srt |
4.51KB |
| 2. Importing the data into Python.mp4 |
6.86MB |
| 2. Importing the data into Python.srt |
1.68KB |
| 2. Installing Tensorflow and Keras.mp4 |
20.06MB |
| 2. Installing Tensorflow and Keras.srt |
4.28KB |
| 2. LDA in Python.mp4 |
11.40MB |
| 2. LDA in Python.srt |
2.58KB |
| 2. LeNET.mp4 |
7.00MB |
| 2. LeNET.srt |
1.91KB |
| 2. Limitations of Support Vector Classifiers.mp4 |
10.81MB |
| 2. Limitations of Support Vector Classifiers.srt |
1.90KB |
| 2. More about test-train split.html |
559B |
| 2. Naive (Persistence) model in Python.mp4 |
43.37MB |
| 2. Naive (Persistence) model in Python.srt |
8.33KB |
| 2. Perceptron.mp4 |
44.76MB |
| 2. Perceptron.srt |
10.69KB |
| 2. Project - Data Augmentation Training and Results.mp4 |
53.05MB |
| 2. Project - Data Augmentation Training and Results.srt |
6.99KB |
| 2. Project - Transfer Learning - VGG16 (Performance).mp4 |
64.12MB |
| 2. Project - Transfer Learning - VGG16 (Performance).srt |
9.15KB |
| 2. Random Walk.mp4 |
21.16MB |
| 2. Random Walk.srt |
4.77KB |
| 2. SARIMA model in Python.mp4 |
66.23MB |
| 2. SARIMA model in Python.srt |
12.11KB |
| 2. Stride.mp4 |
16.59MB |
| 2. Stride.srt |
3.11KB |
| 2. Summary of the three models.mp4 |
22.21MB |
| 2. Summary of the three models.srt |
6.20KB |
| 2. Test-Train Split in Python.mp4 |
33.10MB |
| 2. Test-Train Split in Python.srt |
7.60KB |
| 2. The Concept of a Hyperplane.mp4 |
29.42MB |
| 2. The Concept of a Hyperplane.srt |
6.22KB |
| 2. The Data set for Classification problem.mp4 |
18.57MB |
| 2. The Data set for Classification problem.srt |
2.36KB |
| 2. This is a milestone!.mp4 |
20.66MB |
| 2. This is a milestone!.srt |
3.94KB |
| 2. Time Series Forecasting - Use cases.mp4 |
25.91MB |
| 2. Time Series Forecasting - Use cases.srt |
2.59KB |
| 2. Time Series - Visualization Basics.mp4 |
63.72MB |
| 2. Time Series - Visualization Basics.srt |
10.57KB |
| 2. Training a Simple Logistic Model in Python.mp4 |
47.87MB |
| 2. Training a Simple Logistic Model in Python.srt |
10.76KB |
| 2. Types of Statistics.mp4 |
10.93MB |
| 2. Types of Statistics.srt |
3.30KB |
| 20. Dummy variable creation Handling qualitative data.mp4 |
36.80MB |
| 20. Dummy variable creation Handling qualitative data.srt |
5.53KB |
| 20. Ridge regression and Lasso in Python.mp4 |
128.84MB |
| 20. Ridge regression and Lasso in Python.srt |
21.52KB |
| 21. Dummy variable creation in Python.mp4 |
26.53MB |
| 21. Dummy variable creation in Python.srt |
6.45KB |
| 21. Ridge regression and Lasso in R.mp4 |
103.43MB |
| 21. Ridge regression and Lasso in R.srt |
13.00KB |
| 22. Dummy variable creation in R.mp4 |
43.98MB |
| 22. Dummy variable creation in R.srt |
6.33KB |
| 22. Heteroscedasticity.mp4 |
14.49MB |
| 22. Heteroscedasticity.srt |
3.16KB |
| 23. Correlation Analysis.mp4 |
71.60MB |
| 23. Correlation Analysis.srt |
11.83KB |
| 24. Correlation Analysis in Python.mp4 |
55.30MB |
| 24. Correlation Analysis in Python.srt |
7.18KB |
| 25. Correlation Matrix in R.mp4 |
83.14MB |
| 25. Correlation Matrix in R.srt |
10.01KB |
| 26. Quiz.html |
170B |
| 3.1 Classification preprocessed data R.csv |
40.97KB |
| 3. Activation Functions.mp4 |
34.61MB |
| 3. Activation Functions.srt |
8.51KB |
| 3. ARIMA model in Python.mp4 |
74.43MB |
| 3. ARIMA model in Python.srt |
14.67KB |
| 3. Assessing accuracy of predicted coefficients.mp4 |
92.11MB |
| 3. Assessing accuracy of predicted coefficients.srt |
19.92KB |
| 3. Auto Regression Model - Basics.mp4 |
16.89MB |
| 3. Auto Regression Model - Basics.srt |
3.71KB |
| 3. Back Propagation.mp4 |
122.20MB |
| 3. Back Propagation.srt |
25.88KB |
| 3. Bagging in R.mp4 |
58.96MB |
| 3. Bagging in R.srt |
8.16KB |
| 3. Building,Compiling and Training.mp4 |
130.73MB |
| 3. Building,Compiling and Training.srt |
16.92KB |
| 3. Classification SVM model using Linear Kernel.mp4 |
139.16MB |
| 3. Classification SVM model using Linear Kernel.srt |
18.39KB |
| 3. Classification tree in Python Preprocessing.mp4 |
45.39MB |
| 3. Classification tree in Python Preprocessing.srt |
9.15KB |
| 3. CNN model in Python - Training and results.mp4 |
55.15MB |
| 3. CNN model in Python - Training and results.srt |
6.59KB |
| 3. Creating Model Architecture.mp4 |
71.60MB |
| 3. Creating Model Architecture.srt |
6.55KB |
| 3. Dataset for classification.mp4 |
56.19MB |
| 3. Dataset for classification.srt |
8.16KB |
| 3. Decomposing Time Series in Python.mp4 |
59.84MB |
| 3. Decomposing Time Series in Python.srt |
10.69KB |
| 3. Describing data Graphically.mp4 |
65.39MB |
| 3. Describing data Graphically.srt |
13.22KB |
| 3. Forecasting model creation - Steps.mp4 |
10.11MB |
| 3. Forecasting model creation - Steps.srt |
3.01KB |
| 3. Gradient Boosting in R.mp4 |
69.09MB |
| 3. Gradient Boosting in R.srt |
9.62KB |
| 3. Importing the data into R.mp4 |
8.82MB |
| 3. Importing the data into R.srt |
1.45KB |
| 3. Linear Discriminant Analysis in R.mp4 |
74.35MB |
| 3. Linear Discriminant Analysis in R.srt |
10.50KB |
| 3. Maximum Margin Classifier.mp4 |
22.49MB |
| 3. Maximum Margin Classifier.srt |
4.41KB |
| 3. Opening Jupyter Notebook.mp4 |
65.19MB |
| 3. Opening Jupyter Notebook.srt |
10.08KB |
| 3. Packages in R.mp4 |
82.94MB |
| 3. Packages in R.srt |
14.60KB |
| 3. Padding.mp4 |
31.64MB |
| 3. Padding.srt |
5.11KB |
| 3. Project - Data Preprocessing in Python.mp4 |
71.83MB |
| 3. Project - Data Preprocessing in Python.srt |
9.44KB |
| 3. Project in R - Training.mp4 |
24.59MB |
| 3. Project in R - Training.srt |
3.24KB |
| 3. Standardizing the data.mp4 |
38.41MB |
| 3. Standardizing the data.srt |
6.69KB |
| 3. Stationary time Series.mp4 |
5.58MB |
| 3. Stationary time Series.srt |
1.74KB |
| 3. Test-Train Split in R.mp4 |
74.23MB |
| 3. Test-Train Split in R.srt |
10.27KB |
| 3. The Dataset and the Data Dictionary.mp4 |
69.28MB |
| 3. The Dataset and the Data Dictionary.srt |
8.47KB |
| 3. Time Series - Visualization in Python.mp4 |
165.19MB |
| 3. Time Series - Visualization in Python.srt |
30.37KB |
| 3. Training a Simple Logistic model in R.mp4 |
25.57MB |
| 3. Training a Simple Logistic model in R.srt |
4.31KB |
| 3. Understanding a Regression Tree.mp4 |
43.73MB |
| 3. Understanding a Regression Tree.srt |
13.97KB |
| 3. Using Grid Search in Python.mp4 |
80.66MB |
| 3. Using Grid Search in Python.srt |
14.05KB |
| 3. VGG16NET.mp4 |
10.35MB |
| 3. VGG16NET.srt |
2.02KB |
| 4. ARIMA model with Walk Forward Validation in Python.mp4 |
32.15MB |
| 4. ARIMA model with Walk Forward Validation in Python.srt |
6.34KB |
| 4. Assessing Model Accuracy RSE and R squared.mp4 |
43.59MB |
| 4. Assessing Model Accuracy RSE and R squared.srt |
9.80KB |
| 4. Auto Regression Model creation in Python.mp4 |
53.49MB |
| 4. Auto Regression Model creation in Python.srt |
10.42KB |
| 4. Classification tree in Python Training.mp4 |
82.71MB |
| 4. Classification tree in Python Training.srt |
14.88KB |
| 4. Comparison - Pooling vs Without Pooling in Python.mp4 |
57.97MB |
| 4. Comparison - Pooling vs Without Pooling in Python.srt |
5.77KB |
| 4. Compiling and training.mp4 |
32.21MB |
| 4. Compiling and training.srt |
3.27KB |
| 4. Differencing.mp4 |
32.35MB |
| 4. Differencing.srt |
6.87KB |
| 4. Ensemble technique 3b - AdaBoost in Python.mp4 |
30.53MB |
| 4. Ensemble technique 3b - AdaBoost in Python.srt |
4.55KB |
| 4. Evaluating and Predicting.mp4 |
99.28MB |
| 4. Evaluating and Predicting.srt |
10.52KB |
| 4. Filters and Feature maps.mp4 |
52.71MB |
| 4. Filters and Feature maps.srt |
7.87KB |
| 4. Forecasting model creation - Steps 1 (Goal).mp4 |
34.51MB |
| 4. Forecasting model creation - Steps 1 (Goal).srt |
6.66KB |
| 4. GoogLeNet.mp4 |
21.37MB |
| 4. GoogLeNet.srt |
3.35KB |
| 4. Hyperparameter Tuning for Linear Kernel.mp4 |
60.50MB |
| 4. Hyperparameter Tuning for Linear Kernel.srt |
7.16KB |
| 4. Importing Data in Python.mp4 |
27.84MB |
| 4. Importing Data in Python.srt |
6.61KB |
| 4. Inputting data part 1 Inbuilt datasets of R.mp4 |
40.74MB |
| 4. Inputting data part 1 Inbuilt datasets of R.srt |
5.61KB |
| 4. Introduction to Jupyter.mp4 |
40.91MB |
| 4. Introduction to Jupyter.srt |
15.54KB |
| 4. K-Nearest Neighbors classifier.mp4 |
75.42MB |
| 4. K-Nearest Neighbors classifier.srt |
10.33KB |
| 4. Limitations of Maximum Margin Classifier.mp4 |
10.60MB |
| 4. Limitations of Maximum Margin Classifier.srt |
3.12KB |
| 4. Measures of Centers.mp4 |
38.57MB |
| 4. Measures of Centers.srt |
8.08KB |
| 4. Normalization and Test-Train split.mp4 |
44.20MB |
| 4. Normalization and Test-Train split.srt |
6.32KB |
| 4. Project in R - Model Performance.mp4 |
23.18MB |
| 4. Project in R - Model Performance.srt |
2.58KB |
| 4. Project - Training CNN model in Python.mp4 |
65.98MB |
| 4. Project - Training CNN model in Python.srt |
9.39KB |
| 4. Python - Creating Perceptron model.mp4 |
86.56MB |
| 4. Python - Creating Perceptron model.srt |
16.21KB |
| 4. Random Forest in R.mp4 |
30.72MB |
| 4. Random Forest in R.srt |
5.58KB |
| 4. Result of Simple Logistic Regression.mp4 |
26.93MB |
| 4. Result of Simple Logistic Regression.srt |
6.06KB |
| 4. Some Important Concepts.mp4 |
62.18MB |
| 4. Some Important Concepts.srt |
14.23KB |
| 4. SVM based Regression Model in Python.mp4 |
67.63MB |
| 4. SVM based Regression Model in Python.srt |
10.64KB |
| 4. The final milestone!.mp4 |
11.84MB |
| 4. The final milestone!.srt |
1.79KB |
| 4. The problem statements.mp4 |
17.08MB |
| 4. The problem statements.srt |
1.86KB |
| 4. The stopping criteria for controlling tree growth.mp4 |
13.97MB |
| 4. The stopping criteria for controlling tree growth.srt |
4.29KB |
| 4. Time Series - Feature Engineering Basics.mp4 |
59.47MB |
| 4. Time Series - Feature Engineering Basics.srt |
12.25KB |
| 5. AdaBoosting in R.mp4 |
88.68MB |
| 5. AdaBoosting in R.srt |
12.24KB |
| 5. ANN with NeuralNets Package.mp4 |
84.42MB |
| 5. ANN with NeuralNets Package.srt |
8.78KB |
| 5. Arithmetic operators in Python Python Basics.mp4 |
12.74MB |
| 5. Arithmetic operators in Python Python Basics.srt |
4.62KB |
| 5. Auto Regression with Walk Forward validation in Python.mp4 |
49.59MB |
| 5. Auto Regression with Walk Forward validation in Python.srt |
9.02KB |
| 5. Building a classification Tree in R.mp4 |
85.11MB |
| 5. Building a classification Tree in R.srt |
11.88KB |
| 5. Channels.mp4 |
67.77MB |
| 5. Channels.srt |
6.48KB |
| 5. Classification model - Preprocessing.mp4 |
45.37MB |
| 5. Classification model - Preprocessing.srt |
9.17KB |
| 5. Differencing in Python.mp4 |
113.00MB |
| 5. Differencing in Python.srt |
16.20KB |
| 5. Different ways to create ANN using Keras.mp4 |
10.81MB |
| 5. Different ways to create ANN using Keras.srt |
2.02KB |
| 5. Hyperparameter.mp4 |
45.36MB |
| 5. Hyperparameter.srt |
9.66KB |
| 5. Importing the Data set into Python.mp4 |
15.86MB |
| 5. Importing the Data set into Python.srt |
3.12KB |
| 5. Importing the dataset into R.mp4 |
13.11MB |
| 5. Importing the dataset into R.srt |
2.87KB |
| 5. Inputting data part 2 Manual data entry.mp4 |
25.52MB |
| 5. Inputting data part 2 Manual data entry.srt |
3.68KB |
| 5. K-Nearest Neighbors in Python Part 1.mp4 |
37.23MB |
| 5. K-Nearest Neighbors in Python Part 1.srt |
5.85KB |
| 5. Logistic with multiple predictors.mp4 |
8.54MB |
| 5. Logistic with multiple predictors.srt |
3.08KB |
| 5. Measures of Dispersion.mp4 |
22.86MB |
| 5. Measures of Dispersion.srt |
5.26KB |
| 5. Model Performance.mp4 |
68.08MB |
| 5. Model Performance.srt |
6.83KB |
| 5. Polynomial Kernel with Hyperparameter Tuning.mp4 |
83.14MB |
| 5. Polynomial Kernel with Hyperparameter Tuning.srt |
11.84KB |
| 5. Project in Python - model results.mp4 |
21.02MB |
| 5. Project in Python - model results.srt |
2.95KB |
| 5. Project in R - Data Augmentation.mp4 |
56.38MB |
| 5. Project in R - Data Augmentation.srt |
8.16KB |
| 5. Simple Linear Regression in Python.mp4 |
63.43MB |
| 5. Simple Linear Regression in Python.srt |
13.42KB |
| 5. Time Series - Basic Notations.mp4 |
62.49MB |
| 5. Time Series - Basic Notations.srt |
9.87KB |
| 5. Time Series - Feature Engineering in Python.mp4 |
112.69MB |
| 5. Time Series - Feature Engineering in Python.srt |
20.17KB |
| 5. Transfer Learning.mp4 |
29.99MB |
| 5. Transfer Learning.srt |
5.64KB |
| 5. Why can't we use Linear Regression.mp4 |
16.94MB |
| 5. Why can't we use Linear Regression.srt |
5.69KB |
| 6. Advantages and Disadvantages of Decision Trees.mp4 |
6.86MB |
| 6. Advantages and Disadvantages of Decision Trees.srt |
2.16KB |
| 6. Building Regression Model with Functional API.mp4 |
131.13MB |
| 6. Building Regression Model with Functional API.srt |
14.16KB |
| 6. Building the Neural Network using Keras.mp4 |
79.11MB |
| 6. Building the Neural Network using Keras.srt |
13.32KB |
| 6. Classification model - Standardizing the data.mp4 |
9.72MB |
| 6. Classification model - Standardizing the data.srt |
1.94KB |
| 6. Comparison - Pooling vs Without Pooling in R.mp4 |
44.60MB |
| 6. Comparison - Pooling vs Without Pooling in R.srt |
4.31KB |
| 6. Ensemble technique 3c - XGBoost in Python.mp4 |
75.00MB |
| 6. Ensemble technique 3c - XGBoost in Python.srt |
11.63KB |
| 6. Importing the Data set into R.mp4 |
43.70MB |
| 6. Importing the Data set into R.srt |
8.75KB |
| 6. Inputting data part 3 Importing from CSV or Text files.mp4 |
60.10MB |
| 6. Inputting data part 3 Importing from CSV or Text files.srt |
8.38KB |
| 6. K-Nearest Neighbors in Python Part 2.mp4 |
42.36MB |
| 6. K-Nearest Neighbors in Python Part 2.srt |
6.90KB |
| 6. Moving Average model -Basics.mp4 |
24.10MB |
| 6. Moving Average model -Basics.srt |
5.20KB |
| 6. PoolingLayer.mp4 |
46.87MB |
| 6. PoolingLayer.srt |
6.12KB |
| 6. Project in R - Validation Performance.mp4 |
23.69MB |
| 6. Project in R - Validation Performance.srt |
2.66KB |
| 6. Project - Transfer Learning - VGG16.mp4 |
129.09MB |
| 6. Project - Transfer Learning - VGG16.srt |
21.40KB |
| 6. Radial Kernel with Hyperparameter Tuning.mp4 |
56.68MB |
| 6. Radial Kernel with Hyperparameter Tuning.srt |
7.36KB |
| 6. Simple Linear Regression in R.mp4 |
40.83MB |
| 6. Simple Linear Regression in R.srt |
9.55KB |
| 6. Strings in Python Python Basics.mp4 |
64.43MB |
| 6. Strings in Python Python Basics.srt |
18.58KB |
| 6. Time Series - Upsampling and Downsampling.mp4 |
16.95MB |
| 6. Time Series - Upsampling and Downsampling.srt |
4.45KB |
| 6. Training multiple predictor Logistic model in Python.mp4 |
26.25MB |
| 6. Training multiple predictor Logistic model in Python.srt |
6.25KB |
| 6. Univariate analysis and EDD.mp4 |
24.18MB |
| 6. Univariate analysis and EDD.srt |
3.76KB |
| 7. Compiling and Training the Neural Network model.mp4 |
81.63MB |
| 7. Compiling and Training the Neural Network model.srt |
10.37KB |
| 7. Complex Architectures using Functional API.mp4 |
79.57MB |
| 7. Complex Architectures using Functional API.srt |
9.17KB |
| 7. Creating Barplots in R.mp4 |
96.73MB |
| 7. Creating Barplots in R.srt |
18.34KB |
| 7. EDD in Python.mp4 |
61.80MB |
| 7. EDD in Python.srt |
11.84KB |
| 7. K-Nearest Neighbors in R.mp4 |
64.85MB |
| 7. K-Nearest Neighbors in R.srt |
9.36KB |
| 7. Lists, Tuples and Directories Python Basics.mp4 |
60.32MB |
| 7. Lists, Tuples and Directories Python Basics.srt |
22.17KB |
| 7. Missing value treatment in Python.mp4 |
12.94MB |
| 7. Missing value treatment in Python.srt |
2.32KB |
| 7. Moving Average model in Python.mp4 |
56.66MB |
| 7. Moving Average model in Python.srt |
9.76KB |
| 7. Multiple Linear Regression.mp4 |
34.31MB |
| 7. Multiple Linear Regression.srt |
7.38KB |
| 7. SVM Based classification model.mp4 |
64.13MB |
| 7. SVM Based classification model.srt |
12.70KB |
| 7. SVM based Regression Model in R.mp4 |
106.13MB |
| 7. SVM based Regression Model in R.srt |
12.51KB |
| 7. Time Series - Upsampling and Downsampling in Python.mp4 |
100.67MB |
| 7. Time Series - Upsampling and Downsampling in Python.srt |
18.29KB |
| 7. Training multiple predictor Logistic model in R.mp4 |
15.78MB |
| 7. Training multiple predictor Logistic model in R.srt |
2.08KB |
| 7. XGBoosting in R.mp4 |
161.30MB |
| 7. XGBoosting in R.srt |
21.10KB |
| 8. Confusion Matrix.mp4 |
21.10MB |
| 8. Confusion Matrix.srt |
5.17KB |
| 8. Creating Histograms in R.mp4 |
42.02MB |
| 8. Creating Histograms in R.srt |
7.58KB |
| 8. Dummy Variable creation in Python.mp4 |
24.57MB |
| 8. Dummy Variable creation in Python.srt |
4.49KB |
| 8. EDD in R.mp4 |
96.98MB |
| 8. EDD in R.srt |
13.73KB |
| 8. Evaluating performance and Predicting using Keras.mp4 |
69.91MB |
| 8. Evaluating performance and Predicting using Keras.srt |
10.11KB |
| 8. Hyper Parameter Tuning.mp4 |
57.74MB |
| 8. Hyper Parameter Tuning.srt |
10.91KB |
| 8. Saving - Restoring Models and Using Callbacks.mp4 |
216.04MB |
| 8. Saving - Restoring Models and Using Callbacks.srt |
22.28KB |
| 8. The F - statistic.mp4 |
55.98MB |
| 8. The F - statistic.srt |
11.45KB |
| 8. Time Series - Power Transformation.mp4 |
14.85MB |
| 8. Time Series - Power Transformation.srt |
2.77KB |
| 8. Working with Numpy Library of Python.mp4 |
43.87MB |
| 8. Working with Numpy Library of Python.srt |
12.84KB |
| 9. Building Neural Network for Regression Problem.mp4 |
155.91MB |
| 9. Building Neural Network for Regression Problem.srt |
24.71KB |
| 9. Creating Confusion Matrix in Python.mp4 |
51.25MB |
| 9. Creating Confusion Matrix in Python.srt |
11.10KB |
| 9. Dependent- Independent Data split in Python.mp4 |
16.87MB |
| 9. Dependent- Independent Data split in Python.srt |
3.82KB |
| 9. Interpreting results of Categorical variables.mp4 |
22.51MB |
| 9. Interpreting results of Categorical variables.srt |
6.93KB |
| 9. Moving Average.mp4 |
38.70MB |
| 9. Moving Average.srt |
8.10KB |
| 9. Outlier Treatment.mp4 |
27.26MB |
| 9. Outlier Treatment.srt |
4.92KB |
| 9. Polynomial Kernel with Hyperparameter Tuning.mp4 |
22.92MB |
| 9. Polynomial Kernel with Hyperparameter Tuning.srt |
4.39KB |
| 9. Working with Pandas Library of Python.mp4 |
46.88MB |
| 9. Working with Pandas Library of Python.srt |
10.33KB |