TorBT - Torrents and Magnet Links Search Engine
Latest Searches: tcd, digitalplayground evermore, 我要出彩, 阿朱啊, solidworks, god of war, lana, stuttering, трейси, future

[GigaCourse.Com] Udemy - Machine Learning & Deep Learning in Python & R

Magnet Download Download Torrent
File Name
Size
0. Websites you may like/[CourseClub.ME].url
122 B
0. Websites you may like/[GigaCourse.Com].url
49 B
1. Introduction/1. Introduction.mp4
29 MB
1. Introduction/1. Introduction.srt
4.5 kB
1. Introduction/2. Course Resources.html
370 B
10. Logistic Regression/1. Logistic Regression.mp4
33 MB
10. Logistic Regression/1. Logistic Regression.srt
8.6 kB
10. Logistic Regression/10. Evaluating performance of model.mp4
35 MB
10. Logistic Regression/10. Evaluating performance of model.srt
9.4 kB
10. Logistic Regression/11. Evaluating model performance in Python.mp4
9.0 MB
10. Logistic Regression/11. Evaluating model performance in Python.srt
2.7 kB
10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4
56 MB
10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt
7.4 kB
10. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4
48 MB
10. Logistic Regression/2. Training a Simple Logistic Model in Python.srt
11 kB
10. Logistic Regression/3. Training a Simple Logistic model in R.mp4
26 MB
10. Logistic Regression/3. Training a Simple Logistic model in R.srt
4.2 kB
10. Logistic Regression/4. Result of Simple Logistic Regression.mp4
27 MB
10. Logistic Regression/4. Result of Simple Logistic Regression.srt
5.9 kB
10. Logistic Regression/5. Logistic with multiple predictors.mp4
8.5 MB
10. Logistic Regression/5. Logistic with multiple predictors.srt
3.0 kB
10. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp4
26 MB
10. Logistic Regression/6. Training multiple predictor Logistic model in Python.srt
6.0 kB
10. Logistic Regression/7. Training multiple predictor Logistic model in R.mp4
16 MB
10. Logistic Regression/7. Training multiple predictor Logistic model in R.srt
2.0 kB
10. Logistic Regression/8. Confusion Matrix.mp4
21 MB
10. Logistic Regression/8. Confusion Matrix.srt
4.9 kB
10. Logistic Regression/9. Creating Confusion Matrix in Python.mp4
51 MB
10. Logistic Regression/9. Creating Confusion Matrix in Python.srt
11 kB
11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4
41 MB
11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.srt
12 kB
11. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4
11 MB
11. Linear Discriminant Analysis (LDA)/2. LDA in Python.srt
2.6 kB
11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp4
74 MB
11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.srt
10 kB
12. K-Nearest Neighbors classifier/1. Test-Train Split.mp4
39 MB
12. K-Nearest Neighbors classifier/1. Test-Train Split.srt
11 kB
12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp4
33 MB
12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.srt
7.4 kB
12. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp4
74 MB
12. K-Nearest Neighbors classifier/3. Test-Train Split in R.srt
9.8 kB
12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp4
75 MB
12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.srt
10 kB
12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp4
37 MB
12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.srt
5.8 kB
12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp4
42 MB
12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.srt
6.9 kB
12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp4
65 MB
12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.srt
9.0 kB
13. Comparing results from 3 models/1. Understanding the results of classification models.mp4
42 MB
13. Comparing results from 3 models/1. Understanding the results of classification models.srt
7.5 kB
13. Comparing results from 3 models/2. Summary of the three models.mp4
22 MB
13. Comparing results from 3 models/2. Summary of the three models.srt
6.0 kB
14. Simple Decision Trees/1. Basics of Decision Trees.mp4
43 MB
14. Simple Decision Trees/1. Basics of Decision Trees.srt
11 kB
14. Simple Decision Trees/10. Test-Train split in Python.mp4
25 MB
14. Simple Decision Trees/10. Test-Train split in Python.srt
6.2 kB
14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp4
44 MB
14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.srt
5.8 kB
14. Simple Decision Trees/12. Creating Decision tree in Python.mp4
18 MB
14. Simple Decision Trees/12. Creating Decision tree in Python.srt
4.3 kB
14. Simple Decision Trees/13. Building a Regression Tree in R.mp4
103 MB
14. Simple Decision Trees/13. Building a Regression Tree in R.srt
16 kB
14. Simple Decision Trees/14. Evaluating model performance in Python.mp4
16 MB
14. Simple Decision Trees/14. Evaluating model performance in Python.srt
4.7 kB
14. Simple Decision Trees/15. Plotting decision tree in Python.mp4
22 MB
14. Simple Decision Trees/15. Plotting decision tree in Python.srt
5.3 kB
14. Simple Decision Trees/16. Pruning a tree.mp4
18 MB
14. Simple Decision Trees/16. Pruning a tree.srt
4.5 kB
14. Simple Decision Trees/17. Pruning a tree in Python.mp4
74 MB
14. Simple Decision Trees/17. Pruning a tree in Python.srt
11 kB
14. Simple Decision Trees/18. Pruning a Tree in R.mp4
82 MB
14. Simple Decision Trees/18. Pruning a Tree in R.srt
9.7 kB
14. Simple Decision Trees/2. Understanding a Regression Tree.mp4
44 MB
14. Simple Decision Trees/2. Understanding a Regression Tree.srt
12 kB
14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.mp4
14 MB
14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.srt
3.5 kB
14. Simple Decision Trees/4. The Data set for this part.mp4
37 MB
14. Simple Decision Trees/4. The Data set for this part.srt
3.3 kB
14. Simple Decision Trees/5. Importing the Data set into Python.mp4
26 MB
14. Simple Decision Trees/5. Importing the Data set into Python.srt
5.9 kB
14. Simple Decision Trees/6. Importing the Data set into R.mp4
44 MB
14. Simple Decision Trees/6. Importing the Data set into R.srt
7.2 kB
14. Simple Decision Trees/7. Missing value treatment in Python.mp4
18 MB
14. Simple Decision Trees/7. Missing value treatment in Python.srt
3.7 kB
14. Simple Decision Trees/8. Dummy Variable creation in Python.mp4
25 MB
14. Simple Decision Trees/8. Dummy Variable creation in Python.srt
5.3 kB
14. Simple Decision Trees/9. Dependent- Independent Data split in Python.mp4
15 MB
14. Simple Decision Trees/9. Dependent- Independent Data split in Python.srt
4.2 kB
15. Simple Classification Tree/1. Classification tree.mp4
28 MB
15. Simple Classification Tree/1. Classification tree.srt
6.7 kB
15. Simple Classification Tree/2. The Data set for Classification problem.mp4
19 MB
15. Simple Classification Tree/2. The Data set for Classification problem.srt
1.9 kB
15. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp4
45 MB
15. Simple Classification Tree/3. Classification tree in Python Preprocessing.srt
8.9 kB
15. Simple Classification Tree/4. Classification tree in Python Training.mp4
83 MB
15. Simple Classification Tree/4. Classification tree in Python Training.srt
14 kB
15. Simple Classification Tree/5. Building a classification Tree in R.mp4
85 MB
15. Simple Classification Tree/5. Building a classification Tree in R.srt
10 kB
15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.mp4
6.9 MB
15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.srt
1.7 kB
15. Simple Classification Tree/[CourseClub.Me].url
122 B
15. Simple Classification Tree/[GigaCourse.Com].url
49 B
16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp4
28 MB
16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.srt
7.3 kB
16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4
77 MB
16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.srt
12 kB
16. Ensemble technique 1 - Bagging/3. Bagging in R.mp4
59 MB
16. Ensemble technique 1 - Bagging/3. Bagging in R.srt
7.1 kB
17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp4
18 MB
17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.srt
4.6 kB
17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp4
47 MB
17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.srt
6.7 kB
17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4
81 MB
17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.srt
14 kB
17. Ensemble technique 2 - Random Forests/4. Random Forest in R.mp4
31 MB
17. Ensemble technique 2 - Random Forests/4. Random Forest in R.srt
4.8 kB
18. Ensemble technique 3 - Boosting/1. Boosting.mp4
31 MB
18. Ensemble technique 3 - Boosting/1. Boosting.srt
7.8 kB
18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp4
40 MB
18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.srt
5.4 kB
18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp4
69 MB
18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.srt
8.5 kB
18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.mp4
30 MB
18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.srt
4.4 kB
18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp4
89 MB
18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.srt
10 kB
18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp4
75 MB
18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.srt
11 kB
18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4
161 MB
18. Ensemble technique 3 - Boosting/7. XGBoosting in R.srt
18 kB
19. Maximum Margin Classifier/1. Content flow.mp4
8.6 MB
19. Maximum Margin Classifier/1. Content flow.srt
1.7 kB
19. Maximum Margin Classifier/2. The Concept of a Hyperplane.mp4
29 MB
19. Maximum Margin Classifier/2. The Concept of a Hyperplane.srt
5.3 kB
19. Maximum Margin Classifier/3. Maximum Margin Classifier.mp4
22 MB
19. Maximum Margin Classifier/3. Maximum Margin Classifier.srt
3.5 kB
19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.mp4
11 MB
19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.srt
2.6 kB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4
16 MB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt
2.6 kB
2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.mp4
40 MB
2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.srt
8.2 kB
2. Setting up Python and Jupyter Notebook/2. This is a milestone!.mp4
21 MB
2. Setting up Python and Jupyter Notebook/2. This is a milestone!.srt
3.8 kB
2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.mp4
65 MB
2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.srt
9.8 kB
2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.mp4
41 MB
2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.srt
13 kB
2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.mp4
13 MB
2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.srt
4.4 kB
2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.mp4
64 MB
2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.srt
18 kB
2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.mp4
60 MB
2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.srt
20 kB
2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.mp4
44 MB
2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.srt
12 kB
2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.mp4
47 MB
2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.srt
10 kB
20. Support Vector Classifier/1. Support Vector classifiers.mp4
56 MB
20. Support Vector Classifier/1. Support Vector classifiers.srt
11 kB
20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp4
11 MB
20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.srt
1.6 kB
21. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4
40 MB
21. Support Vector Machines/1. Kernel Based Support Vector Machines.srt
6.7 kB
22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp4
4.0 MB
22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.srt
810 B
22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.mp4
9.7 MB
22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.srt
1.9 kB
22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.mp4
64 MB
22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.srt
12 kB
22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.mp4
58 MB
22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.srt
11 kB
22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.mp4
23 MB
22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.srt
4.5 kB
22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.mp4
37 MB
22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.srt
7.3 kB
22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.mp4
37 MB
22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.srt
3.3 kB
22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.mp4
26 MB
22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.srt
5.9 kB
22. Creating Support Vector Machine Model in Python/4. X-y Split.mp4
15 MB
22. Creating Support Vector Machine Model in Python/4. X-y Split.srt
4.2 kB
22. Creating Support Vector Machine Model in Python/5. Test-Train Split.mp4
25 MB
22. Creating Support Vector Machine Model in Python/5. Test-Train Split.srt
6.2 kB
22. Creating Support Vector Machine Model in Python/6. Standardizing the data.mp4
38 MB
22. Creating Support Vector Machine Model in Python/6. Standardizing the data.srt
6.5 kB
22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.mp4
68 MB
22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.srt
10 kB
22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.mp4
19 MB
22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.srt
1.9 kB
22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.mp4
45 MB
22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.srt
8.9 kB
23. Creating Support Vector Machine Model in R/1. Importing Data into R.mp4
54 MB
23. Creating Support Vector Machine Model in R/1. Importing Data into R.srt
8.9 kB
23. Creating Support Vector Machine Model in R/2. Test-Train Split.mp4
50 MB
23. Creating Support Vector Machine Model in R/2. Test-Train Split.srt
6.0 kB
23. Creating Support Vector Machine Model in R/3. More about test-train split.html
559 B
23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.mp4
139 MB
23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.srt
18 kB
23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.mp4
60 MB
23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.srt
6.9 kB
23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.mp4
83 MB
23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.srt
12 kB
23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.mp4
57 MB
23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.srt
7.2 kB
23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.mp4
106 MB
23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.srt
12 kB
24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp4
29 MB
24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.srt
4.8 kB
24. Introduction - Deep Learning/2. Perceptron.mp4
45 MB
24. Introduction - Deep Learning/2. Perceptron.srt
10 kB
24. Introduction - Deep Learning/3. Activation Functions.mp4
35 MB
24. Introduction - Deep Learning/3. Activation Functions.srt
8.2 kB
24. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp4
87 MB
24. Introduction - Deep Learning/4. Python - Creating Perceptron model.srt
16 kB
25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4
40 MB
25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt
11 kB
25. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4
60 MB
25. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt
13 kB
25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4
122 MB
25. Neural Networks - Stacking cells to create network/3. Back Propagation.srt
25 kB
25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp4
62 MB
25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.srt
14 kB
25. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp4
45 MB
25. Neural Networks - Stacking cells to create network/5. Hyperparameter.srt
9.3 kB
26. ANN in Python/1. Keras and Tensorflow.mp4
15 MB
26. ANN in Python/1. Keras and Tensorflow.srt
3.8 kB
26. ANN in Python/10. Using Functional API for complex architectures.mp4
92 MB
26. ANN in Python/10. Using Functional API for complex architectures.srt
13 kB
26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4
152 MB
26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.srt
21 kB
26. ANN in Python/12. Hyperparameter Tuning.mp4
61 MB
26. ANN in Python/12. Hyperparameter Tuning.srt
9.8 kB
26. ANN in Python/2. Installing Tensorflow and Keras.mp4
20 MB
26. ANN in Python/2. Installing Tensorflow and Keras.srt
4.1 kB
26. ANN in Python/3. Dataset for classification.mp4
56 MB
26. ANN in Python/3. Dataset for classification.srt
7.9 kB
26. ANN in Python/4. Normalization and Test-Train split.mp4
44 MB
26. ANN in Python/4. Normalization and Test-Train split.srt
6.1 kB
26. ANN in Python/5. Different ways to create ANN using Keras.mp4
11 MB
26. ANN in Python/5. Different ways to create ANN using Keras.srt
2.0 kB
26. ANN in Python/6. Building the Neural Network using Keras.mp4
79 MB
26. ANN in Python/6. Building the Neural Network using Keras.srt
13 kB
26. ANN in Python/7. Compiling and Training the Neural Network model.mp4
82 MB
26. ANN in Python/7. Compiling and Training the Neural Network model.srt
10 kB
26. ANN in Python/8. Evaluating performance and Predicting using Keras.mp4
70 MB
26. ANN in Python/8. Evaluating performance and Predicting using Keras.srt
9.8 kB
26. ANN in Python/9. Building Neural Network for Regression Problem.mp4
156 MB
26. ANN in Python/9. Building Neural Network for Regression Problem.srt
24 kB
27. ANN in R/1. Installing Keras and Tensorflow.mp4
23 MB
27. ANN in R/1. Installing Keras and Tensorflow.srt
3.0 kB
27. ANN in R/2. Data Normalization and Test-Train Split.mp4
112 MB
27. ANN in R/2. Data Normalization and Test-Train Split.srt
13 kB
27. ANN in R/3. Building,Compiling and Training.mp4
131 MB
27. ANN in R/3. Building,Compiling and Training.srt
16 kB
27. ANN in R/4. Evaluating and Predicting.mp4
99 MB
27. ANN in R/4. Evaluating and Predicting.srt
10 kB
27. ANN in R/5. ANN with NeuralNets Package.mp4
84 MB
27. ANN in R/5. ANN with NeuralNets Package.srt
8.4 kB
27. ANN in R/6. Building Regression Model with Functional API.mp4
131 MB
27. ANN in R/6. Building Regression Model with Functional API.srt
14 kB
27. ANN in R/7. Complex Architectures using Functional API.mp4
80 MB
27. ANN in R/7. Complex Architectures using Functional API.srt
8.9 kB
27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4
216 MB
27. ANN in R/8. Saving - Restoring Models and Using Callbacks.srt
21 kB
28. CNN - Basics/1. CNN Introduction.mp4
51 MB
28. CNN - Basics/1. CNN Introduction.srt
8.1 kB
28. CNN - Basics/2. Stride.mp4
17 MB
28. CNN - Basics/2. Stride.srt
3.0 kB
28. CNN - Basics/3. Padding.mp4
32 MB
28. CNN - Basics/3. Padding.srt
4.9 kB
28. CNN - Basics/4. Filters and Feature maps.mp4
53 MB
28. CNN - Basics/4. Filters and Feature maps.srt
7.6 kB
28. CNN - Basics/5. Channels.mp4
68 MB
28. CNN - Basics/5. Channels.srt
6.2 kB
28. CNN - Basics/6. PoolingLayer.mp4
47 MB
28. CNN - Basics/6. PoolingLayer.srt
5.8 kB
29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp4
41 MB
29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.srt
5.7 kB
29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp4
43 MB
29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.srt
7.3 kB
29. Creating CNN model in Python/3. CNN model in Python - Training and results.mp4
55 MB
29. Creating CNN model in Python/3. CNN model in Python - Training and results.srt
6.4 kB
29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp4
58 MB
29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.srt
5.6 kB
3. Setting up R Studio and R crash course/1. Installing R and R studio.mp4
36 MB
3. Setting up R Studio and R crash course/1. Installing R and R studio.srt
6.8 kB
3. Setting up R Studio and R crash course/2. Basics of R and R studio.mp4
39 MB
3. Setting up R Studio and R crash course/2. Basics of R and R studio.srt
12 kB
3. Setting up R Studio and R crash course/3. Packages in R.mp4
83 MB
3. Setting up R Studio and R crash course/3. Packages in R.srt
12 kB
3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp4
41 MB
3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.srt
4.6 kB
3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp4
26 MB
3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.srt
3.3 kB
3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp4
60 MB
3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.srt
7.0 kB
3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp4
97 MB
3. Setting up R Studio and R crash course/7. Creating Barplots in R.srt
15 kB
3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp4
42 MB
3. Setting up R Studio and R crash course/8. Creating Histograms in R.srt
6.1 kB
30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp4
7.4 MB
30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.srt
2.4 kB
30. Creating CNN model in R/2. Data Preprocessing.mp4
67 MB
30. Creating CNN model in R/2. Data Preprocessing.srt
7.5 kB
30. Creating CNN model in R/3. Creating Model Architecture.mp4
72 MB
30. Creating CNN model in R/3. Creating Model Architecture.srt
6.3 kB
30. Creating CNN model in R/4. Compiling and training.mp4
32 MB
30. Creating CNN model in R/4. Compiling and training.srt
3.1 kB
30. Creating CNN model in R/5. Model Performance.mp4
68 MB
30. Creating CNN model in R/5. Model Performance.srt
6.6 kB
30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.mp4
45 MB
30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.srt
4.2 kB
30. Creating CNN model in R/[CourseClub.Me].url
122 B
30. Creating CNN model in R/[GigaCourse.Com].url
49 B
31. Project Creating CNN model from scratch in Python/1. Project - Introduction.mp4
49 MB
31. Project Creating CNN model from scratch in Python/1. Project - Introduction.srt
7.5 kB
31. Project Creating CNN model from scratch in Python/2. Data for the project.html
232 B
31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.mp4
72 MB
31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.srt
9.2 kB
31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp4
66 MB
31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.srt
9.2 kB
31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.mp4
21 MB
31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.srt
2.9 kB
32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp4
88 MB
32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.srt
12 kB
32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.mp4
46 MB
32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.srt
5.6 kB
32. Project Creating CNN model from scratch/3. Project in R - Training.mp4
25 MB
32. Project Creating CNN model from scratch/3. Project in R - Training.srt
3.2 kB
32. Project Creating CNN model from scratch/4. Project in R - Model Performance.mp4
23 MB
32. Project Creating CNN model from scratch/4. Project in R - Model Performance.srt
2.5 kB
32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp4
56 MB
32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.srt
7.9 kB
32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.mp4
24 MB
32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.srt
2.6 kB
33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.mp4
41 MB
33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.srt
7.2 kB
33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.mp4
53 MB
33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.srt
6.8 kB
34. Transfer Learning Basics/1. ILSVRC.mp4
21 MB
34. Transfer Learning Basics/1. ILSVRC.srt
4.6 kB
34. Transfer Learning Basics/2. LeNET.mp4
7.0 MB
34. Transfer Learning Basics/2. LeNET.srt
1.8 kB
34. Transfer Learning Basics/3. VGG16NET.mp4
10 MB
34. Transfer Learning Basics/3. VGG16NET.srt
2.0 kB
34. Transfer Learning Basics/4. GoogLeNet.mp4
21 MB
34. Transfer Learning Basics/4. GoogLeNet.srt
3.2 kB
34. Transfer Learning Basics/5. Transfer Learning.mp4
30 MB
34. Transfer Learning Basics/5. Transfer Learning.srt
5.4 kB
34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4
129 MB
34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.srt
20 kB
35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4
102 MB
35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).srt
14 kB
35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp4
64 MB
35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).srt
8.8 kB
36. Time Series Analysis and Forecasting/1. Introduction.mp4
12 MB
36. Time Series Analysis and Forecasting/1. Introduction.srt
2.2 kB
36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp4
26 MB
36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.srt
2.5 kB
36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp4
10 MB
36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.srt
2.9 kB
36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp4
34 MB
36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).srt
6.4 kB
36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp4
62 MB
36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.srt
9.6 kB
37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4
109 MB
37. Time Series - Preprocessing in Python/1. Data Loading in Python.srt
18 kB
37. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp4
8.4 MB
37. Time Series - Preprocessing in Python/10. Exponential Smoothing.srt
2.1 kB
37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp4
64 MB
37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.srt
10 kB
37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4
165 MB
37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.srt
29 kB
37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp4
60 MB
37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.srt
12 kB
37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4
113 MB
37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.srt
19 kB
37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.mp4
17 MB
37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.srt
4.3 kB
37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4
101 MB
37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.srt
18 kB
37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp4
15 MB
37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.srt
2.7 kB
37. Time Series - Preprocessing in Python/9. Moving Average.mp4
39 MB
37. Time Series - Preprocessing in Python/9. Moving Average.srt
7.8 kB
38. Time Series - Important Concepts/1. White Noise.mp4
11 MB
38. Time Series - Important Concepts/1. White Noise.srt
2.5 kB
38. Time Series - Important Concepts/2. Random Walk.mp4
21 MB
38. Time Series - Important Concepts/2. Random Walk.srt
4.6 kB
38. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp4
60 MB
38. Time Series - Important Concepts/3. Decomposing Time Series in Python.srt
10 kB
38. Time Series - Important Concepts/4. Differencing.mp4
32 MB
38. Time Series - Important Concepts/4. Differencing.srt
6.7 kB
38. Time Series - Important Concepts/5. Differencing in Python.mp4
113 MB
38. Time Series - Important Concepts/5. Differencing in Python.srt
16 kB
39. Time Series - Implementation in Python/1. Test Train Split in Python.mp4
57 MB
39. Time Series - Implementation in Python/1. Test Train Split in Python.srt
12 kB
39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp4
43 MB
39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.srt
8.2 kB
39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp4
17 MB
39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.srt
3.6 kB
39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp4
54 MB
39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.srt
10 kB
39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4
50 MB
39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.srt
8.8 kB
39. Time Series - Implementation in Python/6. Moving Average model -Basics.mp4
24 MB
39. Time Series - Implementation in Python/6. Moving Average model -Basics.srt
5.0 kB
39. Time Series - Implementation in Python/7. Moving Average model in Python.mp4
57 MB
39. Time Series - Implementation in Python/7. Moving Average model in Python.srt
9.6 kB
4. Basics of Statistics/1. Types of Data.mp4
22 MB
4. Basics of Statistics/1. Types of Data.srt
5.0 kB
4. Basics of Statistics/2. Types of Statistics.mp4
11 MB
4. Basics of Statistics/2. Types of Statistics.srt
3.2 kB
4. Basics of Statistics/3. Describing data Graphically.mp4
65 MB
4. Basics of Statistics/3. Describing data Graphically.srt
13 kB
4. Basics of Statistics/4. Measures of Centers.mp4
39 MB
4. Basics of Statistics/4. Measures of Centers.srt
7.9 kB
4. Basics of Statistics/5. Measures of Dispersion.mp4
23 MB
4. Basics of Statistics/5. Measures of Dispersion.srt
5.2 kB
40. Time Series - ARIMA model/1. ACF and PACF.mp4
41 MB
40. Time Series - ARIMA model/1. ACF and PACF.srt
8.6 kB
40. Time Series - ARIMA model/2. ARIMA model - Basics.mp4
21 MB
40. Time Series - ARIMA model/2. ARIMA model - Basics.srt
5.1 kB
40. Time Series - ARIMA model/3. ARIMA model in Python.mp4
74 MB
40. Time Series - ARIMA model/3. ARIMA model in Python.srt
14 kB
40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4
32 MB
40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.srt
6.2 kB
40. Time Series - ARIMA model/[CourseClub.Me].url
122 B
40. Time Series - ARIMA model/[GigaCourse.Com].url
49 B
41. Time Series - SARIMA model/1. SARIMA model.mp4
39 MB
41. Time Series - SARIMA model/1. SARIMA model.srt
7.9 kB
41. Time Series - SARIMA model/2. SARIMA model in Python.mp4
66 MB
41. Time Series - SARIMA model/2. SARIMA model in Python.srt
12 kB
41. Time Series - SARIMA model/3. Stationary time Series.mp4
5.6 MB
41. Time Series - SARIMA model/3. Stationary time Series.srt
1.7 kB
42. Bonus Section/1. The final milestone!.mp4
12 MB
42. Bonus Section/1. The final milestone!.srt
1.7 kB
42. Bonus Section/2. Congratulations & About your certificate.html
1.6 kB
5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4
109 MB
5. Introduction to Machine Learning/1. Introduction to Machine Learning.srt
20 kB
5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4
40 MB
5. Introduction to Machine Learning/2. Building a Machine Learning Model.srt
10 kB
5. Introduction to Machine Learning/[CourseClub.Me].url
122 B
5. Introduction to Machine Learning/[GigaCourse.Com].url
49 B
6. Data Preprocessing/1. Gathering Business Knowledge.mp4
22 MB
6. Data Preprocessing/1. Gathering Business Knowledge.srt
4.1 kB
6. Data Preprocessing/10. Outlier Treatment in Python.mp4
70 MB
6. Data Preprocessing/10. Outlier Treatment in Python.srt
14 kB
6. Data Preprocessing/11. Outlier Treatment in R.mp4
31 MB
6. Data Preprocessing/11. Outlier Treatment in R.srt
4.9 kB
6. Data Preprocessing/12. Missing Value Imputation.mp4
25 MB
6. Data Preprocessing/12. Missing Value Imputation.srt
4.2 kB
6. Data Preprocessing/13. Missing Value Imputation in Python.mp4
23 MB
6. Data Preprocessing/13. Missing Value Imputation in Python.srt
4.8 kB
6. Data Preprocessing/14. Missing Value imputation in R.mp4
26 MB
6. Data Preprocessing/14. Missing Value imputation in R.srt
4.1 kB
6. Data Preprocessing/15. Seasonality in Data.mp4
17 MB
6. Data Preprocessing/15. Seasonality in Data.srt
4.0 kB
6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4
100 MB
6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.srt
19 kB
6. Data Preprocessing/17. Variable transformation and deletion in Python.mp4
44 MB
6. Data Preprocessing/17. Variable transformation and deletion in Python.srt
9.0 kB
6. Data Preprocessing/18. Variable transformation in R.mp4
55 MB
6. Data Preprocessing/18. Variable transformation in R.srt
9.9 kB
6. Data Preprocessing/19. Non-usable variables.mp4
20 MB
6. Data Preprocessing/19. Non-usable variables.srt
6.0 kB
6. Data Preprocessing/2. Data Exploration.mp4
20 MB
6. Data Preprocessing/2. Data Exploration.srt
3.9 kB
6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4
37 MB
6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.srt
5.8 kB
6. Data Preprocessing/21. Dummy variable creation in Python.mp4
26 MB
6. Data Preprocessing/21. Dummy variable creation in Python.srt
6.2 kB
6. Data Preprocessing/22. Dummy variable creation in R.mp4
44 MB
6. Data Preprocessing/22. Dummy variable creation in R.srt
6.1 kB
6. Data Preprocessing/23. Correlation Analysis.mp4
72 MB
6. Data Preprocessing/23. Correlation Analysis.srt
12 kB
6. Data Preprocessing/24. Correlation Analysis in Python.mp4
55 MB
6. Data Preprocessing/24. Correlation Analysis in Python.srt
7.0 kB
6. Data Preprocessing/25. Correlation Matrix in R.mp4
83 MB
6. Data Preprocessing/25. Correlation Matrix in R.srt
9.6 kB
6. Data Preprocessing/26. Quiz.html
170 B
6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4
69 MB
6. Data Preprocessing/3. The Dataset and the Data Dictionary.srt
8.8 kB
6. Data Preprocessing/4. Importing Data in Python.mp4
28 MB
6. Data Preprocessing/4. Importing Data in Python.srt
6.5 kB
6. Data Preprocessing/5. Importing the dataset into R.mp4
13 MB
6. Data Preprocessing/5. Importing the dataset into R.srt
2.8 kB
6. Data Preprocessing/6. Univariate analysis and EDD.mp4
24 MB
6. Data Preprocessing/6. Univariate analysis and EDD.srt
4.0 kB
6. Data Preprocessing/7. EDD in Python.mp4
62 MB
6. Data Preprocessing/7. EDD in Python.srt
12 kB
6. Data Preprocessing/8. EDD in R.mp4
97 MB
6. Data Preprocessing/8. EDD in R.srt
13 kB
6. Data Preprocessing/9. Outlier Treatment.mp4
24 MB
6. Data Preprocessing/9. Outlier Treatment.srt
5.1 kB
7. Linear Regression/1. The Problem Statement.mp4
9.4 MB
7. Linear Regression/1. The Problem Statement.srt
1.7 kB
7. Linear Regression/10. Multiple Linear Regression in Python.mp4
70 MB
7. Linear Regression/10. Multiple Linear Regression in Python.srt
14 kB
7. Linear Regression/11. Multiple Linear Regression in R.mp4
62 MB
7. Linear Regression/11. Multiple Linear Regression in R.srt
9.2 kB
7. Linear Regression/12. Test-train split.mp4
42 MB
7. Linear Regression/12. Test-train split.srt
11 kB
7. Linear Regression/13. Bias Variance trade-off.mp4
25 MB
7. Linear Regression/13. Bias Variance trade-off.srt
7.0 kB
7. Linear Regression/14. Test train split in Python.mp4
45 MB
7. Linear Regression/14. Test train split in Python.srt
8.7 kB
7. Linear Regression/15. Test-Train Split in R.mp4
76 MB
7. Linear Regression/15. Test-Train Split in R.srt
9.4 kB
7. Linear Regression/16. Regression models other than OLS.mp4
16 MB
7. Linear Regression/16. Regression models other than OLS.srt
4.8 kB
7. Linear Regression/17. Subset selection techniques.mp4
79 MB
7. Linear Regression/17. Subset selection techniques.srt
14 kB
7. Linear Regression/18. Subset selection in R.mp4
64 MB
7. Linear Regression/18. Subset selection in R.srt
8.2 kB
7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp4
33 MB
7. Linear Regression/19. Shrinkage methods Ridge and Lasso.srt
9.0 kB
7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4
43 MB
7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt
10 kB
7. Linear Regression/20. Ridge regression and Lasso in Python.mp4
129 MB
7. Linear Regression/20. Ridge regression and Lasso in Python.srt
21 kB
7. Linear Regression/21. Ridge regression and Lasso in R.mp4
103 MB
7. Linear Regression/21. Ridge regression and Lasso in R.srt
12 kB
7. Linear Regression/22. Heteroscedasticity.mp4
14 MB
7. Linear Regression/22. Heteroscedasticity.srt
2.8 kB
7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4
92 MB
7. Linear Regression/3. Assessing accuracy of predicted coefficients.srt
17 kB
7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4
44 MB
7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt
8.4 kB
7. Linear Regression/5. Simple Linear Regression in Python.mp4
63 MB
7. Linear Regression/5. Simple Linear Regression in Python.srt
13 kB
7. Linear Regression/6. Simple Linear Regression in R.mp4
41 MB
7. Linear Regression/6. Simple Linear Regression in R.srt
9.3 kB
7. Linear Regression/7. Multiple Linear Regression.mp4
34 MB
7. Linear Regression/7. Multiple Linear Regression.srt
6.3 kB
7. Linear Regression/8. The F - statistic.mp4
56 MB
7. Linear Regression/8. The F - statistic.srt
9.7 kB
7. Linear Regression/9. Interpreting results of Categorical variables.mp4
22 MB
7. Linear Regression/9. Interpreting results of Categorical variables.srt
5.9 kB
8. Classification Models Data Preparation/1. The Data and the Data Dictionary.mp4
79 MB
8. Classification Models Data Preparation/1. The Data and the Data Dictionary.srt
9.3 kB
8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.mp4
29 MB
8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.srt
4.3 kB
8. Classification Models Data Preparation/11. Variable transformation in R.mp4
38 MB
8. Classification Models Data Preparation/11. Variable transformation in R.srt
6.8 kB
8. Classification Models Data Preparation/12. Dummy variable creation in Python.mp4
26 MB
8. Classification Models Data Preparation/12. Dummy variable creation in Python.srt
6.1 kB
8. Classification Models Data Preparation/13. Dummy variable creation in R.mp4
44 MB
8. Classification Models Data Preparation/13. Dummy variable creation in R.srt
6.5 kB
8. Classification Models Data Preparation/2. Data Import in Python.mp4
22 MB
8. Classification Models Data Preparation/2. Data Import in Python.srt
5.3 kB
8. Classification Models Data Preparation/3. Importing the dataset into R.mp4
14 MB
8. Classification Models Data Preparation/3. Importing the dataset into R.srt
2.8 kB
8. Classification Models Data Preparation/4. EDD in Python.mp4
78 MB
8. Classification Models Data Preparation/4. EDD in Python.srt
18 kB
8. Classification Models Data Preparation/5. EDD in R.mp4
66 MB
8. Classification Models Data Preparation/5. EDD in R.srt
11 kB
8. Classification Models Data Preparation/6. Outlier treatment in Python.mp4
47 MB
8. Classification Models Data Preparation/6. Outlier treatment in Python.srt
9.6 kB
8. Classification Models Data Preparation/7. Outlier Treatment in R.mp4
25 MB
8. Classification Models Data Preparation/7. Outlier Treatment in R.srt
4.8 kB
8. Classification Models Data Preparation/8. Missing Value Imputation in Python.mp4
23 MB
8. Classification Models Data Preparation/8. Missing Value Imputation in Python.srt
4.8 kB
8. Classification Models Data Preparation/9. Missing Value imputation in R.mp4
19 MB
8. Classification Models Data Preparation/9. Missing Value imputation in R.srt
4.1 kB
9. The Three classification models/1. Three Classifiers and the problem statement.mp4
20 MB
9. The Three classification models/1. Three Classifiers and the problem statement.srt
3.9 kB
9. The Three classification models/2. Why can't we use Linear Regression.mp4
17 MB
9. The Three classification models/2. Why can't we use Linear Regression.srt
5.5 kB
[CourseClub.Me].url
122 B
[GigaCourse.Com].url
49 B