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

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File Name

Size

0. Websites you may like/[CourseClub.ME].url

122B

0. Websites you may like/[GigaCourse.Com].url

49B

1. Introduction/1. Introduction.mp4

29.4MB

1. Introduction/1. Introduction.srt

4.49KB

1. Introduction/2. Course Resources.html

370B

10. Logistic Regression/1. Logistic Regression.mp4

32.93MB

10. Logistic Regression/1. Logistic Regression.srt

8.64KB

10. Logistic Regression/10. Evaluating performance of model.mp4

35.17MB

10. Logistic Regression/10. Evaluating performance of model.srt

9.38KB

10. Logistic Regression/11. Evaluating model performance in Python.mp4

9.02MB

10. Logistic Regression/11. Evaluating model performance in Python.srt

2.66KB

10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4

55.7MB

10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt

7.41KB

10. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4

47.87MB

10. Logistic Regression/2. Training a Simple Logistic Model in Python.srt

10.63KB

10. Logistic Regression/3. Training a Simple Logistic model in R.mp4

25.57MB

10. Logistic Regression/3. Training a Simple Logistic model in R.srt

4.21KB

10. Logistic Regression/4. Result of Simple Logistic Regression.mp4

26.94MB

10. Logistic Regression/4. Result of Simple Logistic Regression.srt

5.9KB

10. Logistic Regression/5. Logistic with multiple predictors.mp4

8.53MB

10. Logistic Regression/5. Logistic with multiple predictors.srt

2.96KB

10. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp4

26.25MB

10. Logistic Regression/6. Training multiple predictor Logistic model in Python.srt

6.01KB

10. Logistic Regression/7. Training multiple predictor Logistic model in R.mp4

15.78MB

10. Logistic Regression/7. Training multiple predictor Logistic model in R.srt

2.02KB

10. Logistic Regression/8. Confusion Matrix.mp4

21.1MB

10. Logistic Regression/8. Confusion Matrix.srt

4.91KB

10. Logistic Regression/9. Creating Confusion Matrix in Python.mp4

51.25MB

10. Logistic Regression/9. Creating Confusion Matrix in Python.srt

10.85KB

11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4

40.96MB

11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.srt

11.89KB

11. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4

11.4MB

11. Linear Discriminant Analysis (LDA)/2. LDA in Python.srt

2.57KB

11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp4

74.36MB

11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.srt

10.22KB

12. K-Nearest Neighbors classifier/1. Test-Train Split.mp4

39.3MB

12. K-Nearest Neighbors classifier/1. Test-Train Split.srt

10.59KB

12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp4

33.1MB

12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.srt

7.39KB

12. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp4

74.23MB

12. K-Nearest Neighbors classifier/3. Test-Train Split in R.srt

9.81KB

12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp4

75.42MB

12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.srt

9.98KB

12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp4

37.23MB

12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.srt

5.83KB

12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp4

42.36MB

12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.srt

6.93KB

12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp4

64.85MB

12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.srt

8.98KB

13. Comparing results from 3 models/1. Understanding the results of classification models.mp4

41.64MB

13. Comparing results from 3 models/1. Understanding the results of classification models.srt

7.52KB

13. Comparing results from 3 models/2. Summary of the three models.mp4

22.22MB

13. Comparing results from 3 models/2. Summary of the three models.srt

5.96KB

14. Simple Decision Trees/1. Basics of Decision Trees.mp4

42.64MB

14. Simple Decision Trees/1. Basics of Decision Trees.srt

11.27KB

14. Simple Decision Trees/10. Test-Train split in Python.mp4

24.87MB

14. Simple Decision Trees/10. Test-Train split in Python.srt

6.17KB

14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp4

43.98MB

14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.srt

5.83KB

14. Simple Decision Trees/12. Creating Decision tree in Python.mp4

17.87MB

14. Simple Decision Trees/12. Creating Decision tree in Python.srt

4.31KB

14. Simple Decision Trees/13. Building a Regression Tree in R.mp4

103.34MB

14. Simple Decision Trees/13. Building a Regression Tree in R.srt

15.5KB

14. Simple Decision Trees/14. Evaluating model performance in Python.mp4

16.44MB

14. Simple Decision Trees/14. Evaluating model performance in Python.srt

4.73KB

14. Simple Decision Trees/15. Plotting decision tree in Python.mp4

21.48MB

14. Simple Decision Trees/15. Plotting decision tree in Python.srt

5.29KB

14. Simple Decision Trees/16. Pruning a tree.mp4

18.46MB

14. Simple Decision Trees/16. Pruning a tree.srt

4.54KB

14. Simple Decision Trees/17. Pruning a tree in Python.mp4

73.5MB

14. Simple Decision Trees/17. Pruning a tree in Python.srt

10.72KB

14. Simple Decision Trees/18. Pruning a Tree in R.mp4

82.1MB

14. Simple Decision Trees/18. Pruning a Tree in R.srt

9.66KB

14. Simple Decision Trees/2. Understanding a Regression Tree.mp4

43.72MB

14. Simple Decision Trees/2. Understanding a Regression Tree.srt

11.91KB

14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.mp4

13.98MB

14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.srt

3.51KB

14. Simple Decision Trees/4. The Data set for this part.mp4

37.26MB

14. Simple Decision Trees/4. The Data set for this part.srt

3.28KB

14. Simple Decision Trees/5. Importing the Data set into Python.mp4

25.85MB

14. Simple Decision Trees/5. Importing the Data set into Python.srt

5.88KB

14. Simple Decision Trees/6. Importing the Data set into R.mp4

43.7MB

14. Simple Decision Trees/6. Importing the Data set into R.srt

7.24KB

14. Simple Decision Trees/7. Missing value treatment in Python.mp4

17.93MB

14. Simple Decision Trees/7. Missing value treatment in Python.srt

3.73KB

14. Simple Decision Trees/8. Dummy Variable creation in Python.mp4

24.94MB

14. Simple Decision Trees/8. Dummy Variable creation in Python.srt

5.34KB

14. Simple Decision Trees/9. Dependent- Independent Data split in Python.mp4

15.18MB

14. Simple Decision Trees/9. Dependent- Independent Data split in Python.srt

4.24KB

15. Simple Classification Tree/1. Classification tree.mp4

28.2MB

15. Simple Classification Tree/1. Classification tree.srt

6.72KB

15. Simple Classification Tree/2. The Data set for Classification problem.mp4

18.57MB

15. Simple Classification Tree/2. The Data set for Classification problem.srt

1.91KB

15. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp4

45.38MB

15. Simple Classification Tree/3. Classification tree in Python Preprocessing.srt

8.92KB

15. Simple Classification Tree/4. Classification tree in Python Training.mp4

82.72MB

15. Simple Classification Tree/4. Classification tree in Python Training.srt

14.51KB

15. Simple Classification Tree/5. Building a classification Tree in R.mp4

85.1MB

15. Simple Classification Tree/5. Building a classification Tree in R.srt

10.13KB

15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.mp4

6.86MB

15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.srt

1.7KB

15. Simple Classification Tree/[CourseClub.Me].url

122B

15. Simple Classification Tree/[GigaCourse.Com].url

49B

16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp4

28.14MB

16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.srt

7.27KB

16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4

77.3MB

16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.srt

12.28KB

16. Ensemble technique 1 - Bagging/3. Bagging in R.mp4

58.96MB

16. Ensemble technique 1 - Bagging/3. Bagging in R.srt

7.13KB

17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp4

18.2MB

17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.srt

4.59KB

17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp4

46.7MB

17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.srt

6.69KB

17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4

80.67MB

17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.srt

13.69KB

17. Ensemble technique 2 - Random Forests/4. Random Forest in R.mp4

30.72MB

17. Ensemble technique 2 - Random Forests/4. Random Forest in R.srt

4.77KB

18. Ensemble technique 3 - Boosting/1. Boosting.mp4

30.58MB

18. Ensemble technique 3 - Boosting/1. Boosting.srt

7.81KB

18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp4

39.88MB

18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.srt

5.44KB

18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp4

69.09MB

18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.srt

8.55KB

18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.mp4

30.54MB

18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.srt

4.42KB

18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp4

88.67MB

18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.srt

10.51KB

18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp4

75.01MB

18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.srt

11.43KB

18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4

161.3MB

18. Ensemble technique 3 - Boosting/7. XGBoosting in R.srt

18.43KB

19. Maximum Margin Classifier/1. Content flow.mp4

8.64MB

19. Maximum Margin Classifier/1. Content flow.srt

1.74KB

19. Maximum Margin Classifier/2. The Concept of a Hyperplane.mp4

29.42MB

19. Maximum Margin Classifier/2. The Concept of a Hyperplane.srt

5.31KB

19. Maximum Margin Classifier/3. Maximum Margin Classifier.mp4

22.48MB

19. Maximum Margin Classifier/3. Maximum Margin Classifier.srt

3.46KB

19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.mp4

10.61MB

19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.srt

2.64KB

2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4

16.27MB

2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt

2.65KB

2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.mp4

40.37MB

2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.srt

8.24KB

2. Setting up Python and Jupyter Notebook/2. This is a milestone!.mp4

20.66MB

2. Setting up Python and Jupyter Notebook/2. This is a milestone!.srt

3.78KB

2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.mp4

65.19MB

2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.srt

9.84KB

2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.mp4

40.92MB

2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.srt

13.2KB

2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.mp4

12.74MB

2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.srt

4.44KB

2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.mp4

64.44MB

2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.srt

17.97KB

2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.mp4

60.33MB

2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.srt

20.11KB

2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.mp4

43.88MB

2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.srt

11.85KB

2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.mp4

46.88MB

2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.srt

10.12KB

20. Support Vector Classifier/1. Support Vector classifiers.mp4

56.17MB

20. Support Vector Classifier/1. Support Vector classifiers.srt

10.85KB

20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp4

10.8MB

20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.srt

1.62KB

21. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4

40.12MB

21. Support Vector Machines/1. Kernel Based Support Vector Machines.srt

6.71KB

22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp4

4.04MB

22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.srt

810B

22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.mp4

9.72MB

22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.srt

1.89KB

22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.mp4

64.13MB

22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.srt

12.39KB

22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.mp4

57.74MB

22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.srt

10.79KB

22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.mp4

22.92MB

22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.srt

4.49KB

22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.mp4

37.21MB

22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.srt

7.26KB

22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.mp4

37.2MB

22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.srt

3.28KB

22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.mp4

25.84MB

22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.srt

5.88KB

22. Creating Support Vector Machine Model in Python/4. X-y Split.mp4

15.18MB

22. Creating Support Vector Machine Model in Python/4. X-y Split.srt

4.24KB

22. Creating Support Vector Machine Model in Python/5. Test-Train Split.mp4

24.87MB

22. Creating Support Vector Machine Model in Python/5. Test-Train Split.srt

6.17KB

22. Creating Support Vector Machine Model in Python/6. Standardizing the data.mp4

38.41MB

22. Creating Support Vector Machine Model in Python/6. Standardizing the data.srt

6.51KB

22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.mp4

67.64MB

22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.srt

10.45KB

22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.mp4

18.56MB

22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.srt

1.91KB

22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.mp4

45.38MB

22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.srt

8.92KB

23. Creating Support Vector Machine Model in R/1. Importing Data into R.mp4

53.67MB

23. Creating Support Vector Machine Model in R/1. Importing Data into R.srt

8.9KB

23. Creating Support Vector Machine Model in R/2. Test-Train Split.mp4

50.48MB

23. Creating Support Vector Machine Model in R/2. Test-Train Split.srt

6.04KB

23. Creating Support Vector Machine Model in R/3. More about test-train split.html

559B

23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.mp4

139.16MB

23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.srt

17.75KB

23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.mp4

60.5MB

23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.srt

6.95KB

23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.mp4

83.14MB

23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.srt

11.49KB

23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.mp4

56.68MB

23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.srt

7.19KB

23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.mp4

106.12MB

23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.srt

12.05KB

24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp4

29.07MB

24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.srt

4.77KB

24. Introduction - Deep Learning/2. Perceptron.mp4

44.75MB

24. Introduction - Deep Learning/2. Perceptron.srt

10.22KB

24. Introduction - Deep Learning/3. Activation Functions.mp4

34.62MB

24. Introduction - Deep Learning/3. Activation Functions.srt

8.17KB

24. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp4

86.56MB

24. Introduction - Deep Learning/4. Python - Creating Perceptron model.srt

15.71KB

25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4

40.42MB

25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt

10.81KB

25. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4

60.34MB

25. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt

12.7KB

25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4

122.2MB

25. Neural Networks - Stacking cells to create network/3. Back Propagation.srt

24.77KB

25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp4

62.18MB

25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.srt

13.65KB

25. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp4

45.36MB

25. Neural Networks - Stacking cells to create network/5. Hyperparameter.srt

9.32KB

26. ANN in Python/1. Keras and Tensorflow.mp4

14.92MB

26. ANN in Python/1. Keras and Tensorflow.srt

3.78KB

26. ANN in Python/10. Using Functional API for complex architectures.mp4

92.11MB

26. ANN in Python/10. Using Functional API for complex architectures.srt

12.95KB

26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4

151.59MB

26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.srt

20.83KB

26. ANN in Python/12. Hyperparameter Tuning.mp4

60.63MB

26. ANN in Python/12. Hyperparameter Tuning.srt

9.81KB

26. ANN in Python/2. Installing Tensorflow and Keras.mp4

20.06MB

26. ANN in Python/2. Installing Tensorflow and Keras.srt

4.14KB

26. ANN in Python/3. Dataset for classification.mp4

56.19MB

26. ANN in Python/3. Dataset for classification.srt

7.9KB

26. ANN in Python/4. Normalization and Test-Train split.mp4

44.2MB

26. ANN in Python/4. Normalization and Test-Train split.srt

6.12KB

26. ANN in Python/5. Different ways to create ANN using Keras.mp4

10.82MB

26. ANN in Python/5. Different ways to create ANN using Keras.srt

1.98KB

26. ANN in Python/6. Building the Neural Network using Keras.mp4

79.11MB

26. ANN in Python/6. Building the Neural Network using Keras.srt

12.92KB

26. ANN in Python/7. Compiling and Training the Neural Network model.mp4

81.63MB

26. ANN in Python/7. Compiling and Training the Neural Network model.srt

10.03KB

26. ANN in Python/8. Evaluating performance and Predicting using Keras.mp4

69.91MB

26. ANN in Python/8. Evaluating performance and Predicting using Keras.srt

9.81KB

26. ANN in Python/9. Building Neural Network for Regression Problem.mp4

155.9MB

26. ANN in Python/9. Building Neural Network for Regression Problem.srt

23.75KB

27. ANN in R/1. Installing Keras and Tensorflow.mp4

22.79MB

27. ANN in R/1. Installing Keras and Tensorflow.srt

3.01KB

27. ANN in R/2. Data Normalization and Test-Train Split.mp4

111.78MB

27. ANN in R/2. Data Normalization and Test-Train Split.srt

12.87KB

27. ANN in R/3. Building,Compiling and Training.mp4

130.74MB

27. ANN in R/3. Building,Compiling and Training.srt

16.27KB

27. ANN in R/4. Evaluating and Predicting.mp4

99.28MB

27. ANN in R/4. Evaluating and Predicting.srt

10.11KB

27. ANN in R/5. ANN with NeuralNets Package.mp4

84.42MB

27. ANN in R/5. ANN with NeuralNets Package.srt

8.44KB

27. ANN in R/6. Building Regression Model with Functional API.mp4

131.13MB

27. ANN in R/6. Building Regression Model with Functional API.srt

13.54KB

27. ANN in R/7. Complex Architectures using Functional API.mp4

79.57MB

27. ANN in R/7. Complex Architectures using Functional API.srt

8.87KB

27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4

216.03MB

27. ANN in R/8. Saving - Restoring Models and Using Callbacks.srt

21.38KB

28. CNN - Basics/1. CNN Introduction.mp4

51.16MB

28. CNN - Basics/1. CNN Introduction.srt

8.13KB

28. CNN - Basics/2. Stride.mp4

16.58MB

28. CNN - Basics/2. Stride.srt

3.01KB

28. CNN - Basics/3. Padding.mp4

31.63MB

28. CNN - Basics/3. Padding.srt

4.95KB

28. CNN - Basics/4. Filters and Feature maps.mp4

52.71MB

28. CNN - Basics/4. Filters and Feature maps.srt

7.58KB

28. CNN - Basics/5. Channels.mp4

67.77MB

28. CNN - Basics/5. Channels.srt

6.24KB

28. CNN - Basics/6. PoolingLayer.mp4

46.88MB

28. CNN - Basics/6. PoolingLayer.srt

5.85KB

29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp4

40.63MB

29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.srt

5.74KB

29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp4

43.26MB

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29. Creating CNN model in Python/3. CNN model in Python - Training and results.mp4

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29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp4

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29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.srt

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3. Setting up R Studio and R crash course/1. Installing R and R studio.mp4

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3. Setting up R Studio and R crash course/1. Installing R and R studio.srt

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3. Setting up R Studio and R crash course/2. Basics of R and R studio.mp4

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3. Setting up R Studio and R crash course/3. Packages in R.mp4

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3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp4

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3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp4

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3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp4

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3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp4

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30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp4

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30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.srt

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30. Creating CNN model in R/2. Data Preprocessing.mp4

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30. Creating CNN model in R/3. Creating Model Architecture.mp4

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30. Creating CNN model in R/4. Compiling and training.mp4

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30. Creating CNN model in R/5. Model Performance.mp4

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30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.mp4

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30. Creating CNN model in R/[CourseClub.Me].url

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31. Project Creating CNN model from scratch in Python/1. Project - Introduction.mp4

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31. Project Creating CNN model from scratch in Python/1. Project - Introduction.srt

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31. Project Creating CNN model from scratch in Python/2. Data for the project.html

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31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.mp4

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31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.srt

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31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp4

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31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.srt

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31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.mp4

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31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.srt

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32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp4

87.76MB

32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.srt

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32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.mp4

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32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.srt

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32. Project Creating CNN model from scratch/3. Project in R - Training.mp4

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32. Project Creating CNN model from scratch/3. Project in R - Training.srt

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32. Project Creating CNN model from scratch/4. Project in R - Model Performance.mp4

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32. Project Creating CNN model from scratch/4. Project in R - Model Performance.srt

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32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp4

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32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.srt

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32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.mp4

23.69MB

32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.srt

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33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.mp4

41.42MB

33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.srt

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33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.mp4

53.04MB

33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.srt

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34. Transfer Learning Basics/1. ILSVRC.mp4

20.93MB

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34. Transfer Learning Basics/2. LeNET.mp4

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34. Transfer Learning Basics/3. VGG16NET.mp4

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34. Transfer Learning Basics/4. GoogLeNet.mp4

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34. Transfer Learning Basics/5. Transfer Learning.mp4

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34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4

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35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4

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35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp4

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36. Time Series Analysis and Forecasting/1. Introduction.mp4

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36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp4

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36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp4

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36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp4

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37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4

108.87MB

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37. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp4

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37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp4

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37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4

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37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.srt

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37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp4

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37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.srt

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37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4

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37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.srt

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37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.mp4

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37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4

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37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp4

14.86MB

37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.srt

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37. Time Series - Preprocessing in Python/9. Moving Average.mp4

38.71MB

37. Time Series - Preprocessing in Python/9. Moving Average.srt

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38. Time Series - Important Concepts/1. White Noise.mp4

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38. Time Series - Important Concepts/1. White Noise.srt

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38. Time Series - Important Concepts/2. Random Walk.mp4

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38. Time Series - Important Concepts/2. Random Walk.srt

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38. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp4

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38. Time Series - Important Concepts/3. Decomposing Time Series in Python.srt

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38. Time Series - Important Concepts/4. Differencing.mp4

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38. Time Series - Important Concepts/4. Differencing.srt

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38. Time Series - Important Concepts/5. Differencing in Python.mp4

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38. Time Series - Important Concepts/5. Differencing in Python.srt

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39. Time Series - Implementation in Python/1. Test Train Split in Python.mp4

57.42MB

39. Time Series - Implementation in Python/1. Test Train Split in Python.srt

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39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp4

43.38MB

39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.srt

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39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp4

16.89MB

39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.srt

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39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp4

53.49MB

39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.srt

10.2KB

39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4

49.6MB

39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.srt

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39. Time Series - Implementation in Python/6. Moving Average model -Basics.mp4

24.1MB

39. Time Series - Implementation in Python/6. Moving Average model -Basics.srt

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39. Time Series - Implementation in Python/7. Moving Average model in Python.mp4

56.65MB

39. Time Series - Implementation in Python/7. Moving Average model in Python.srt

9.59KB

4. Basics of Statistics/1. Types of Data.mp4

21.76MB

4. Basics of Statistics/1. Types of Data.srt

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4. Basics of Statistics/2. Types of Statistics.mp4

10.94MB

4. Basics of Statistics/2. Types of Statistics.srt

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4. Basics of Statistics/3. Describing data Graphically.mp4

65.4MB

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4. Basics of Statistics/4. Measures of Centers.mp4

38.58MB

4. Basics of Statistics/4. Measures of Centers.srt

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4. Basics of Statistics/5. Measures of Dispersion.mp4

22.85MB

4. Basics of Statistics/5. Measures of Dispersion.srt

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40. Time Series - ARIMA model/1. ACF and PACF.mp4

41.23MB

40. Time Series - ARIMA model/1. ACF and PACF.srt

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40. Time Series - ARIMA model/2. ARIMA model - Basics.mp4

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40. Time Series - ARIMA model/2. ARIMA model - Basics.srt

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40. Time Series - ARIMA model/3. ARIMA model in Python.mp4

74.44MB

40. Time Series - ARIMA model/3. ARIMA model in Python.srt

14.3KB

40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4

32.15MB

40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.srt

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40. Time Series - ARIMA model/[CourseClub.Me].url

122B

40. Time Series - ARIMA model/[GigaCourse.Com].url

49B

41. Time Series - SARIMA model/1. SARIMA model.mp4

39.03MB

41. Time Series - SARIMA model/1. SARIMA model.srt

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41. Time Series - SARIMA model/2. SARIMA model in Python.mp4

66.23MB

41. Time Series - SARIMA model/2. SARIMA model in Python.srt

11.58KB

41. Time Series - SARIMA model/3. Stationary time Series.mp4

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41. Time Series - SARIMA model/3. Stationary time Series.srt

1.7KB

42. Bonus Section/1. The final milestone!.mp4

11.85MB

42. Bonus Section/1. The final milestone!.srt

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42. Bonus Section/2. Congratulations & About your certificate.html

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5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4

109.18MB

5. Introduction to Machine Learning/1. Introduction to Machine Learning.srt

19.73KB

5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4

39.48MB

5. Introduction to Machine Learning/2. Building a Machine Learning Model.srt

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5. Introduction to Machine Learning/[CourseClub.Me].url

122B

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6. Data Preprocessing/1. Gathering Business Knowledge.mp4

22.29MB

6. Data Preprocessing/1. Gathering Business Knowledge.srt

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6. Data Preprocessing/10. Outlier Treatment in Python.mp4

70.26MB

6. Data Preprocessing/10. Outlier Treatment in Python.srt

14.12KB

6. Data Preprocessing/11. Outlier Treatment in R.mp4

30.74MB

6. Data Preprocessing/11. Outlier Treatment in R.srt

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6. Data Preprocessing/12. Missing Value Imputation.mp4

25MB

6. Data Preprocessing/12. Missing Value Imputation.srt

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6. Data Preprocessing/13. Missing Value Imputation in Python.mp4

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6. Data Preprocessing/13. Missing Value Imputation in Python.srt

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6. Data Preprocessing/14. Missing Value imputation in R.mp4

26.01MB

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6. Data Preprocessing/15. Seasonality in Data.mp4

17.02MB

6. Data Preprocessing/15. Seasonality in Data.srt

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6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4

100.4MB

6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.srt

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6. Data Preprocessing/17. Variable transformation and deletion in Python.mp4

44.12MB

6. Data Preprocessing/17. Variable transformation and deletion in Python.srt

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6. Data Preprocessing/18. Variable transformation in R.mp4

55.43MB

6. Data Preprocessing/18. Variable transformation in R.srt

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6. Data Preprocessing/19. Non-usable variables.mp4

20.25MB

6. Data Preprocessing/19. Non-usable variables.srt

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6. Data Preprocessing/2. Data Exploration.mp4

20.51MB

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6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4

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6. Data Preprocessing/21. Dummy variable creation in Python.mp4

26.53MB

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6. Data Preprocessing/22. Dummy variable creation in R.mp4

43.99MB

6. Data Preprocessing/22. Dummy variable creation in R.srt

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6. Data Preprocessing/23. Correlation Analysis.mp4

71.6MB

6. Data Preprocessing/23. Correlation Analysis.srt

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6. Data Preprocessing/24. Correlation Analysis in Python.mp4

55.3MB

6. Data Preprocessing/24. Correlation Analysis in Python.srt

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6. Data Preprocessing/25. Correlation Matrix in R.mp4

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6. Data Preprocessing/25. Correlation Matrix in R.srt

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6. Data Preprocessing/26. Quiz.html

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6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4

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6. Data Preprocessing/4. Importing Data in Python.mp4

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6. Data Preprocessing/5. Importing the dataset into R.mp4

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6. Data Preprocessing/6. Univariate analysis and EDD.mp4

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6. Data Preprocessing/7. EDD in Python.mp4

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6. Data Preprocessing/8. EDD in R.mp4

96.98MB

6. Data Preprocessing/8. EDD in R.srt

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6. Data Preprocessing/9. Outlier Treatment.mp4

24.5MB

6. Data Preprocessing/9. Outlier Treatment.srt

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7. Linear Regression/1. The Problem Statement.mp4

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7. Linear Regression/10. Multiple Linear Regression in Python.mp4

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7. Linear Regression/11. Multiple Linear Regression in R.mp4

62.38MB

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7. Linear Regression/12. Test-train split.mp4

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7. Linear Regression/13. Bias Variance trade-off.mp4

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7. Linear Regression/14. Test train split in Python.mp4

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7. Linear Regression/15. Test-Train Split in R.mp4

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7. Linear Regression/16. Regression models other than OLS.mp4

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7. Linear Regression/17. Subset selection techniques.mp4

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7. Linear Regression/18. Subset selection in R.mp4

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7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp4

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7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4

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7. Linear Regression/20. Ridge regression and Lasso in Python.mp4

128.85MB

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7. Linear Regression/21. Ridge regression and Lasso in R.mp4

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7. Linear Regression/22. Heteroscedasticity.mp4

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7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4

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7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4

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7. Linear Regression/5. Simple Linear Regression in Python.mp4

63.43MB

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7. Linear Regression/6. Simple Linear Regression in R.mp4

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7. Linear Regression/7. Multiple Linear Regression.mp4

34.32MB

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6.3KB

7. Linear Regression/8. The F - statistic.mp4

55.99MB

7. Linear Regression/8. The F - statistic.srt

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7. Linear Regression/9. Interpreting results of Categorical variables.mp4

22.5MB

7. Linear Regression/9. Interpreting results of Categorical variables.srt

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8. Classification Models Data Preparation/1. The Data and the Data Dictionary.mp4

79.01MB

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8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.mp4

29.26MB

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4.31KB

8. Classification Models Data Preparation/11. Variable transformation in R.mp4

38.03MB

8. Classification Models Data Preparation/11. Variable transformation in R.srt

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8. Classification Models Data Preparation/12. Dummy variable creation in Python.mp4

26.37MB

8. Classification Models Data Preparation/12. Dummy variable creation in Python.srt

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8. Classification Models Data Preparation/13. Dummy variable creation in R.mp4

44.36MB

8. Classification Models Data Preparation/13. Dummy variable creation in R.srt

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8. Classification Models Data Preparation/2. Data Import in Python.mp4

22.06MB

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8. Classification Models Data Preparation/3. Importing the dataset into R.mp4

13.47MB

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8. Classification Models Data Preparation/4. EDD in Python.mp4

77.63MB

8. Classification Models Data Preparation/4. EDD in Python.srt

17.77KB

8. Classification Models Data Preparation/5. EDD in R.mp4

66.52MB

8. Classification Models Data Preparation/5. EDD in R.srt

11.37KB

8. Classification Models Data Preparation/6. Outlier treatment in Python.mp4

47.32MB

8. Classification Models Data Preparation/6. Outlier treatment in Python.srt

9.55KB

8. Classification Models Data Preparation/7. Outlier Treatment in R.mp4

25.37MB

8. Classification Models Data Preparation/7. Outlier Treatment in R.srt

4.8KB

8. Classification Models Data Preparation/8. Missing Value Imputation in Python.mp4

22.56MB

8. Classification Models Data Preparation/8. Missing Value Imputation in Python.srt

4.83KB

8. Classification Models Data Preparation/9. Missing Value imputation in R.mp4

19.05MB

8. Classification Models Data Preparation/9. Missing Value imputation in R.srt

4.1KB

9. The Three classification models/1. Three Classifiers and the problem statement.mp4

20.34MB

9. The Three classification models/1. Three Classifiers and the problem statement.srt

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9. The Three classification models/2. Why can't we use Linear Regression.mp4

16.94MB

9. The Three classification models/2. Why can't we use Linear Regression.srt

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[CourseClub.Me].url

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[GigaCourse.Com].url

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