0. Websites you may like/[CourseClub.Me].url
122B
0. Websites you may like/[GigaCourse.Com].url
49B
1. Introduction/1. Introduction.mp4
29.39MB
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1. Introduction/1. Introduction.srt
4.64KB
1. Introduction/2. Course Resources.html
370B
10. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4
40.96MB
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10. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.srt
12.29KB
10. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4
11.4MB
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10. Linear Discriminant Analysis (LDA)/2. LDA in Python.srt
2.58KB
10. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp4
74.35MB
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10. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.srt
10.5KB
11. K-Nearest Neighbors classifier/1. Test-Train Split.mp4
39.3MB
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11. K-Nearest Neighbors classifier/1. Test-Train Split.srt
10.97KB
11. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp4
33.1MB
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11. K-Nearest Neighbors classifier/2. Test-Train Split in Python.srt
7.6KB
11. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp4
74.23MB
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11. K-Nearest Neighbors classifier/3. Test-Train Split in R.srt
10.27KB
11. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp4
75.42MB
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11. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.srt
10.33KB
11. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp4
37.23MB
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11. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.srt
5.85KB
11. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp4
42.36MB
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11. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.srt
6.9KB
11. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp4
64.85MB
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11. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.srt
9.36KB
12. Comparing results from 3 models/1. Understanding the results of classification models.mp4
41.64MB
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12. Comparing results from 3 models/1. Understanding the results of classification models.srt
7.8KB
12. Comparing results from 3 models/2. Summary of the three models.mp4
22.21MB
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12. Comparing results from 3 models/2. Summary of the three models.srt
6.2KB
13. Simple Decision Trees/1. Introduction to Decision trees.mp4
44.78MB
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13. Simple Decision Trees/1. Introduction to Decision trees.srt
4.74KB
13. Simple Decision Trees/10. Test-Train split in Python.mp4
25.62MB
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13. Simple Decision Trees/10. Test-Train split in Python.srt
5.29KB
13. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp4
43.97MB
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13. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.srt
7.29KB
13. Simple Decision Trees/12. Creating Decision tree in Python.mp4
17.87MB
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13. Simple Decision Trees/12. Creating Decision tree in Python.srt
4.34KB
13. Simple Decision Trees/13. Building a Regression Tree in R.mp4
103.33MB
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18.88KB
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13. Simple Decision Trees/2. Basics of Decision Trees.mp4
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13. Simple Decision Trees/3. Understanding a Regression Tree.mp4
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13. Simple Decision Trees/4. The stopping criteria for controlling tree growth.mp4
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13. Simple Decision Trees/5. Importing the Data set into Python.mp4
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13. Simple Decision Trees/6. Importing the Data set into R.mp4
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14. Simple Classification Tree/1. Classification tree.mp4
28.2MB
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14. Simple Classification Tree/4. Classification tree in Python Training.mp4
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14.88KB
14. Simple Classification Tree/5. Building a classification Tree in R.mp4
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14.05KB
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5.58KB
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30.58MB
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39.87MB
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5.61KB
17. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp4
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4.55KB
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12.24KB
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75MB
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11.63KB
17. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4
161.3MB
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21.1KB
18. Support Vector Machines/1. Introduction to SVM's.mp4
21.62MB
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3.26KB
18. Support Vector Machines/2. The Concept of a Hyperplane.mp4
29.42MB
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6.22KB
18. Support Vector Machines/3. Maximum Margin Classifier.mp4
22.49MB
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4.41KB
18. Support Vector Machines/4. Limitations of Maximum Margin Classifier.mp4
10.6MB
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3.12KB
19. Support Vector Classifier/1. Support Vector classifiers.mp4
56.16MB
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12.46KB
19. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp4
10.81MB
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1.9KB
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2. Setting up Python and Jupyter Notebook/2. This is a milestone!.mp4
20.66MB
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20. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4
40.12MB
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8.46KB
21. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp4
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21. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.srt
817B
21. Creating Support Vector Machine Model in Python/10. Radial Kernel with Hyperparameter Tuning.mp4
37.21MB
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21. Creating Support Vector Machine Model in Python/9. Polynomial Kernel with Hyperparameter Tuning.mp4
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22. Creating Support Vector Machine Model in R/1. Importing and preprocessing data in R.mp4
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139.16MB
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60.5MB
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7.16KB
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83.14MB
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11.84KB
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56.68MB
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7.36KB
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106.13MB
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12.51KB
23. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp4
29.07MB
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4.96KB
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44.76MB
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10.69KB
23. Introduction - Deep Learning/3. Activation Functions.mp4
34.61MB
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23. Introduction - Deep Learning/3. Activation Functions.srt
8.51KB
23. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp4
86.56MB
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23. Introduction - Deep Learning/4. Python - Creating Perceptron model.srt
16.21KB
24. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4
40.42MB
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24. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt
11.35KB
24. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4
60.34MB
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24. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt
13.24KB
24. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4
122.2MB
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24. Neural Networks - Stacking cells to create network/3. Back Propagation.srt
25.88KB
24. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp4
62.18MB
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24. Neural Networks - Stacking cells to create network/4. Some Important Concepts.srt
14.23KB
24. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp4
45.36MB
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24. Neural Networks - Stacking cells to create network/5. Hyperparameter.srt
9.66KB
25. ANN in Python/1. Keras and Tensorflow.mp4
14.91MB
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25. ANN in Python/1. Keras and Tensorflow.srt
3.89KB
25. ANN in Python/10. Using Functional API for complex architectures.mp4
92.1MB
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25. ANN in Python/10. Using Functional API for complex architectures.srt
13.37KB
25. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4
151.58MB
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25. ANN in Python/11. Saving - Restoring Models and Using Callbacks.srt
21.59KB
25. ANN in Python/12. Hyperparameter Tuning.mp4
60.63MB
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25. ANN in Python/12. Hyperparameter Tuning.srt
10.17KB
25. ANN in Python/2. Installing Tensorflow and Keras.mp4
20.06MB
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25. ANN in Python/2. Installing Tensorflow and Keras.srt
4.28KB
25. ANN in Python/3. Dataset for classification.mp4
56.19MB
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25. ANN in Python/3. Dataset for classification.srt
8.16KB
25. ANN in Python/4. Normalization and Test-Train split.mp4
44.2MB
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25. ANN in Python/4. Normalization and Test-Train split.srt
6.32KB
25. ANN in Python/5. Different ways to create ANN using Keras.mp4
10.81MB
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25. ANN in Python/5. Different ways to create ANN using Keras.srt
2.02KB
25. ANN in Python/6. Building the Neural Network using Keras.mp4
79.11MB
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25. ANN in Python/6. Building the Neural Network using Keras.srt
13.32KB
25. ANN in Python/7. Compiling and Training the Neural Network model.mp4
81.63MB
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25. ANN in Python/7. Compiling and Training the Neural Network model.srt
10.37KB
25. ANN in Python/8. Evaluating performance and Predicting using Keras.mp4
69.91MB
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25. ANN in Python/8. Evaluating performance and Predicting using Keras.srt
10.11KB
25. ANN in Python/9. Building Neural Network for Regression Problem.mp4
155.91MB
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25. ANN in Python/9. Building Neural Network for Regression Problem.srt
24.71KB
26. ANN in R/1. Installing Keras and Tensorflow.mp4
22.78MB
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26. ANN in R/1. Installing Keras and Tensorflow.srt
3.11KB
26. ANN in R/2. Data Normalization and Test-Train Split.mp4
111.78MB
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26. ANN in R/2. Data Normalization and Test-Train Split.srt
13.36KB
26. ANN in R/3. Building,Compiling and Training.mp4
130.73MB
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26. ANN in R/3. Building,Compiling and Training.srt
16.92KB
26. ANN in R/4. Evaluating and Predicting.mp4
99.28MB
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26. ANN in R/4. Evaluating and Predicting.srt
10.52KB
26. ANN in R/5. ANN with NeuralNets Package.mp4
84.42MB
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26. ANN in R/5. ANN with NeuralNets Package.srt
8.78KB
26. ANN in R/6. Building Regression Model with Functional API.mp4
131.13MB
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26. ANN in R/6. Building Regression Model with Functional API.srt
14.16KB
26. ANN in R/7. Complex Architectures using Functional API.mp4
79.57MB
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26. ANN in R/7. Complex Architectures using Functional API.srt
9.17KB
26. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4
216.04MB
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26. ANN in R/8. Saving - Restoring Models and Using Callbacks.srt
22.28KB
27. CNN - Basics/1. CNN Introduction.mp4
56.75MB
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27. CNN - Basics/1. CNN Introduction.srt
8.33KB
27. CNN - Basics/2. Stride.mp4
16.59MB
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27. CNN - Basics/2. Stride.srt
3.11KB
27. CNN - Basics/3. Padding.mp4
31.64MB
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27. CNN - Basics/3. Padding.srt
5.11KB
27. CNN - Basics/4. Filters and Feature maps.mp4
52.71MB
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27. CNN - Basics/4. Filters and Feature maps.srt
7.87KB
27. CNN - Basics/5. Channels.mp4
67.77MB
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27. CNN - Basics/5. Channels.srt
6.48KB
27. CNN - Basics/6. PoolingLayer.mp4
46.87MB
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27. CNN - Basics/6. PoolingLayer.srt
6.12KB
28. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp4
40.63MB
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28. Creating CNN model in Python/1. CNN model in Python - Preprocessing.srt
5.88KB
28. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp4
43.26MB
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28. Creating CNN model in Python/2. CNN model in Python - structure and Compile.srt
7.53KB
28. Creating CNN model in Python/3. CNN model in Python - Training and results.mp4
55.15MB
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6.59KB
28. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp4
57.97MB
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5.77KB
29. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp4
7.35MB
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2.47KB
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67.02MB
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3. Setting up R Studio and R crash course/1. Installing R and R studio.mp4
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60.1MB
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3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.srt
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3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp4
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18.34KB
3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp4
42.02MB
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7.58KB
30. Project Creating CNN model from scratch in Python/1. Project - Introduction.mp4
49.39MB
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7.75KB
30. Project Creating CNN model from scratch in Python/2. Data for the project.html
232B
30. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.mp4
71.83MB
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30. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.srt
9.44KB
30. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp4
65.98MB
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9.39KB
30. Project Creating CNN model from scratch in Python/5. Project in Python - model results.mp4
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30. Project Creating CNN model from scratch in Python/5. Project in Python - model results.srt
2.95KB
31. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp4
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31. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.srt
12.47KB
31. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.mp4
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5.82KB
31. Project Creating CNN model from scratch/3. Project in R - Training.mp4
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3.24KB
31. Project Creating CNN model from scratch/4. Project in R - Model Performance.mp4
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31. Project Creating CNN model from scratch/4. Project in R - Model Performance.srt
2.58KB
31. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp4
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31. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.srt
8.16KB
31. Project Creating CNN model from scratch/6. Project in R - Validation Performance.mp4
23.69MB
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31. Project Creating CNN model from scratch/6. Project in R - Validation Performance.srt
2.66KB
32. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.mp4
41.41MB
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32. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.srt
7.53KB
32. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.mp4
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32. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.srt
6.99KB
33. Transfer Learning Basics/1. ILSVRC.mp4
20.92MB
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33. Transfer Learning Basics/1. ILSVRC.srt
4.73KB
33. Transfer Learning Basics/2. LeNET.mp4
7MB
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33. Transfer Learning Basics/2. LeNET.srt
1.91KB
33. Transfer Learning Basics/3. VGG16NET.mp4
10.35MB
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33. Transfer Learning Basics/3. VGG16NET.srt
2.02KB
33. Transfer Learning Basics/4. GoogLeNet.mp4
21.37MB
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33. Transfer Learning Basics/4. GoogLeNet.srt
3.35KB
33. Transfer Learning Basics/5. Transfer Learning.mp4
29.99MB
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33. Transfer Learning Basics/5. Transfer Learning.srt
5.64KB
33. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4
129.09MB
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33. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.srt
21.4KB
34. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4
101.58MB
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14.81KB
34. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp4
64.12MB
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9.15KB
35. Time Series Analysis and Forecasting/1. Introduction.mp4
18.68MB
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2.88KB
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25.91MB
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2.59KB
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10.11MB
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3.01KB
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34.51MB
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6.66KB
35. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp4
62.49MB
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9.87KB
36. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4
108.86MB
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36. Time Series - Preprocessing in Python/1. Data Loading in Python.srt
18.51KB
36. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp4
8.38MB
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36. Time Series - Preprocessing in Python/10. Exponential Smoothing.srt
2.17KB
36. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp4
63.72MB
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10.57KB
36. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4
165.19MB
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36. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.srt
30.37KB
36. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp4
59.47MB
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12.25KB
36. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4
112.69MB
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36. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.srt
20.17KB
36. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.mp4
16.95MB
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36. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.srt
4.45KB
36. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4
100.67MB
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36. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.srt
18.29KB
36. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp4
14.85MB
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2.77KB
36. Time Series - Preprocessing in Python/9. Moving Average.mp4
38.7MB
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36. Time Series - Preprocessing in Python/9. Moving Average.srt
8.1KB
37. Time Series - Important Concepts/1. White Noise.mp4
11.37MB
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37. Time Series - Important Concepts/1. White Noise.srt
2.61KB
37. Time Series - Important Concepts/2. Random Walk.mp4
21.16MB
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37. Time Series - Important Concepts/2. Random Walk.srt
4.77KB
37. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp4
59.84MB
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37. Time Series - Important Concepts/3. Decomposing Time Series in Python.srt
10.69KB
37. Time Series - Important Concepts/4. Differencing.mp4
32.35MB
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37. Time Series - Important Concepts/4. Differencing.srt
6.87KB
37. Time Series - Important Concepts/5. Differencing in Python.mp4
113MB
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37. Time Series - Important Concepts/5. Differencing in Python.srt
16.2KB
38. Time Series - Implementation in Python/1. Test Train Split in Python.mp4
57.41MB
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38. Time Series - Implementation in Python/1. Test Train Split in Python.srt
12.33KB
38. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp4
43.37MB
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38. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.srt
8.33KB
38. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp4
16.89MB
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38. Time Series - Implementation in Python/3. Auto Regression Model - Basics.srt
3.71KB
38. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp4
53.49MB
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38. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.srt
10.42KB
38. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4
49.59MB
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38. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.srt
9.02KB
38. Time Series - Implementation in Python/6. Moving Average model -Basics.mp4
24.1MB
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38. Time Series - Implementation in Python/6. Moving Average model -Basics.srt
5.2KB
38. Time Series - Implementation in Python/7. Moving Average model in Python.mp4
56.66MB
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38. Time Series - Implementation in Python/7. Moving Average model in Python.srt
9.76KB
39. Time Series - ARIMA model/1. ACF and PACF.mp4
41.23MB
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39. Time Series - ARIMA model/1. ACF and PACF.srt
8.92KB
39. Time Series - ARIMA model/2. ARIMA model - Basics.mp4
21.36MB
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39. Time Series - ARIMA model/2. ARIMA model - Basics.srt
5.25KB
39. Time Series - ARIMA model/3. ARIMA model in Python.mp4
74.43MB
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39. Time Series - ARIMA model/3. ARIMA model in Python.srt
14.67KB
39. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4
32.15MB
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39. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.srt
6.34KB
4. Basics of Statistics/1. Types of Data.mp4
21.77MB
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4. Basics of Statistics/1. Types of Data.srt
5.2KB
4. Basics of Statistics/2. Types of Statistics.mp4
10.93MB
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4. Basics of Statistics/2. Types of Statistics.srt
3.3KB
4. Basics of Statistics/3. Describing data Graphically.mp4
65.39MB
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13.22KB
4. Basics of Statistics/4. Measures of Centers.mp4
38.57MB
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8.08KB
4. Basics of Statistics/5. Measures of Dispersion.mp4
22.86MB
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5.26KB
40. Time Series - SARIMA model/1. SARIMA model.mp4
39.02MB
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8.17KB
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66.23MB
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12.11KB
40. Time Series - SARIMA model/3. Stationary time Series.mp4
5.58MB
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1.74KB
40. Time Series - SARIMA model/4. The final milestone!.mp4
11.84MB
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40. Time Series - SARIMA model/4. The final milestone!.srt
1.79KB
41. Congratulations & About your certificate/1. Bonus Lecture.html
2.32KB
5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4
109.17MB
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5. Introduction to Machine Learning/1. Introduction to Machine Learning.srt
19.38KB
5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4
39.48MB
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10.22KB
6. Data Preprocessing/1. Gathering Business Knowledge.mp4
14.52MB
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3.8KB
6. Data Preprocessing/10. Outlier Treatment in Python.mp4
70.25MB
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14.49KB
6. Data Preprocessing/11. Outlier Treatment in R.mp4
30.75MB
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6. Data Preprocessing/12. Missing Value Imputation.mp4
23.15MB
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4.25KB
6. Data Preprocessing/13. Missing Value Imputation in Python.mp4
23.42MB
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6. Data Preprocessing/14. Missing Value imputation in R.mp4
26MB
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6. Data Preprocessing/15. Seasonality in Data.mp4
17.02MB
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6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4
100.39MB
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20.2KB
6. Data Preprocessing/17. Variable transformation and deletion in Python.mp4
44.11MB
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9.32KB
6. Data Preprocessing/18. Variable transformation in R.mp4
55.42MB
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10.18KB
6. Data Preprocessing/19. Non-usable variables.mp4
20.24MB
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6.27KB
6. Data Preprocessing/2. Data Exploration.mp4
20.12MB
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3.82KB
6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4
36.8MB
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5.53KB
6. Data Preprocessing/21. Dummy variable creation in Python.mp4
26.53MB
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6. Data Preprocessing/21. Dummy variable creation in Python.srt
6.45KB
6. Data Preprocessing/22. Dummy variable creation in R.mp4
43.98MB
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6.33KB
6. Data Preprocessing/23. Correlation Analysis.mp4
71.6MB
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11.83KB
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55.3MB
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7.18KB
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83.14MB
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10.01KB
6. Data Preprocessing/26. Quiz.html
170B
6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4
69.28MB
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8.47KB
6. Data Preprocessing/4. Importing Data in Python.mp4
27.84MB
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6.61KB
6. Data Preprocessing/5. Importing the dataset into R.mp4
13.11MB
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2.87KB
6. Data Preprocessing/6. Univariate analysis and EDD.mp4
24.18MB
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6. Data Preprocessing/6. Univariate analysis and EDD.srt
3.76KB
6. Data Preprocessing/7. EDD in Python.mp4
61.8MB
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6. Data Preprocessing/7. EDD in Python.srt
11.84KB
6. Data Preprocessing/8. EDD in R.mp4
96.98MB
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6. Data Preprocessing/8. EDD in R.srt
13.73KB
6. Data Preprocessing/9. Outlier Treatment.mp4
27.26MB
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6. Data Preprocessing/9. Outlier Treatment.srt
4.92KB
7. Linear Regression/1. The Problem Statement.mp4
9.38MB
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7. Linear Regression/1. The Problem Statement.srt
1.84KB
7. Linear Regression/10. Multiple Linear Regression in Python.mp4
69.73MB
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7. Linear Regression/10. Multiple Linear Regression in Python.srt
14.42KB
7. Linear Regression/11. Multiple Linear Regression in R.mp4
62.37MB
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7. Linear Regression/11. Multiple Linear Regression in R.srt
9.56KB
7. Linear Regression/12. Test-train split.mp4
41.89MB
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7. Linear Regression/12. Test-train split.srt
12.64KB
7. Linear Regression/13. Bias Variance trade-off.mp4
25.1MB
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7. Linear Regression/13. Bias Variance trade-off.srt
8.2KB
7. Linear Regression/14. Test train split in Python.mp4
44.89MB
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7. Linear Regression/14. Test train split in Python.srt
8.82KB
7. Linear Regression/15. Test-Train Split in R.mp4
75.6MB
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7. Linear Regression/15. Test-Train Split in R.srt
9.61KB
7. Linear Regression/16. Regression models other than OLS.mp4
16.55MB
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7. Linear Regression/16. Regression models other than OLS.srt
5.28KB
7. Linear Regression/17. Subset selection techniques.mp4
79.06MB
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7. Linear Regression/17. Subset selection techniques.srt
15.28KB
7. Linear Regression/18. Subset selection in R.mp4
63.54MB
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7. Linear Regression/18. Subset selection in R.srt
8.37KB
7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp4
33.34MB
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7. Linear Regression/19. Shrinkage methods Ridge and Lasso.srt
9.41KB
7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4
43.38MB
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7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt
12.66KB
7. Linear Regression/20. Ridge regression and Lasso in Python.mp4
128.84MB
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7. Linear Regression/20. Ridge regression and Lasso in Python.srt
21.52KB
7. Linear Regression/21. Ridge regression and Lasso in R.mp4
103.43MB
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7. Linear Regression/21. Ridge regression and Lasso in R.srt
13KB
7. Linear Regression/22. Heteroscedasticity.mp4
14.49MB
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7. Linear Regression/22. Heteroscedasticity.srt
3.16KB
7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4
92.11MB
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7. Linear Regression/3. Assessing accuracy of predicted coefficients.srt
19.92KB
7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4
43.59MB
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7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt
9.8KB
7. Linear Regression/5. Simple Linear Regression in Python.mp4
63.43MB
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7. Linear Regression/5. Simple Linear Regression in Python.srt
13.42KB
7. Linear Regression/6. Simple Linear Regression in R.mp4
40.83MB
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7. Linear Regression/6. Simple Linear Regression in R.srt
9.55KB
7. Linear Regression/7. Multiple Linear Regression.mp4
34.31MB
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7. Linear Regression/7. Multiple Linear Regression.srt
7.38KB
7. Linear Regression/8. The F - statistic.mp4
55.98MB
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7. Linear Regression/8. The F - statistic.srt
11.45KB
7. Linear Regression/9. Interpreting results of Categorical variables.mp4
22.51MB
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7. Linear Regression/9. Interpreting results of Categorical variables.srt
6.93KB
8. Introduction to the classification Models/1. Three classification models and Data set.mp4
52.27MB
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8. Introduction to the classification Models/1. Three classification models and Data set.srt
6.93KB
8. Introduction to the classification Models/1.1 Classification preprocessed data Python.csv
40.97KB
8. Introduction to the classification Models/1.2 Classification preprocessed data R.csv
40.97KB
8. Introduction to the classification Models/2. Importing the data into Python.mp4
6.86MB
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8. Introduction to the classification Models/2. Importing the data into Python.srt
1.68KB
8. Introduction to the classification Models/2.1 Classification preprocessed data Python.csv
40.97KB
8. Introduction to the classification Models/3. Importing the data into R.mp4
8.82MB
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8. Introduction to the classification Models/3. Importing the data into R.srt
1.45KB
8. Introduction to the classification Models/3.1 Classification preprocessed data R.csv
40.97KB
8. Introduction to the classification Models/4. The problem statements.mp4
17.08MB
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8. Introduction to the classification Models/4. The problem statements.srt
1.86KB
8. Introduction to the classification Models/5. Why can't we use Linear Regression.mp4
16.94MB
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8. Introduction to the classification Models/5. Why can't we use Linear Regression.srt
5.69KB
9. Logistic Regression/1. Logistic Regression.mp4
32.92MB
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9. Logistic Regression/1. Logistic Regression.srt
8.92KB
9. Logistic Regression/10. Evaluating performance of model.mp4
35.16MB
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9. Logistic Regression/10. Evaluating performance of model.srt
9.67KB
9. Logistic Regression/11. Evaluating model performance in Python.mp4
9.01MB
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9. Logistic Regression/11. Evaluating model performance in Python.srt
2.62KB
9. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4
55.7MB
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9. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt
7.65KB
9. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4
47.87MB
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9. Logistic Regression/2. Training a Simple Logistic Model in Python.srt
10.76KB
9. Logistic Regression/3. Training a Simple Logistic model in R.mp4
25.57MB
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9. Logistic Regression/3. Training a Simple Logistic model in R.srt
4.31KB
9. Logistic Regression/4. Result of Simple Logistic Regression.mp4
26.93MB
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6.06KB
9. Logistic Regression/5. Logistic with multiple predictors.mp4
8.54MB
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3.08KB
9. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp4
26.25MB
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9. Logistic Regression/6. Training multiple predictor Logistic model in Python.srt
6.25KB
9. Logistic Regression/7. Training multiple predictor Logistic model in R.mp4
15.78MB
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9. Logistic Regression/7. Training multiple predictor Logistic model in R.srt
2.08KB
9. Logistic Regression/8. Confusion Matrix.mp4
21.1MB
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9. Logistic Regression/8. Confusion Matrix.srt
5.17KB
9. Logistic Regression/9. Creating Confusion Matrix in Python.mp4
51.25MB
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9. Logistic Regression/9. Creating Confusion Matrix in Python.srt
11.1KB