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[FreeCourseSite.com] Udemy - Machine Learning & Deep Learning in Python & R
大小 13.15 GB
文件数 565
Info Hash: 29DA54F31388519725497A24A3C2763BF269786E
收录时间 2026-02-13 01:04:31
更新时间 2026-02-13 01:04:31
文件列表 (565)
0. Websites you may like/[CourseClub.ME].url
122 B
0. Websites you may like/[FCS Forum].url
133 B
0. Websites you may like/[FreeCourseSite.com].url
127 B
0. Websites you may like/[GigaCourse.Com].url
49 B
1. Introduction/1. Introduction.mp4
29.4 MB
1. Introduction/1. Introduction.srt
4.49 KB
1. Introduction/2. Course Resources.html
370 B
10. Logistic Regression/1. Logistic Regression.mp4
32.93 MB
10. Logistic Regression/1. Logistic Regression.srt
8.64 KB
10. Logistic Regression/10. Evaluating performance of model.mp4
35.17 MB
10. Logistic Regression/10. Evaluating performance of model.srt
9.38 KB
10. Logistic Regression/11. Evaluating model performance in Python.mp4
9.02 MB
10. Logistic Regression/11. Evaluating model performance in Python.srt
2.66 KB
10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4
55.7 MB
10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt
7.41 KB
10. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4
47.87 MB
10. Logistic Regression/2. Training a Simple Logistic Model in Python.srt
10.63 KB
10. Logistic Regression/3. Training a Simple Logistic model in R.mp4
25.57 MB
10. Logistic Regression/3. Training a Simple Logistic model in R.srt
4.21 KB
10. Logistic Regression/4. Result of Simple Logistic Regression.mp4
26.94 MB
10. Logistic Regression/4. Result of Simple Logistic Regression.srt
5.9 KB
10. Logistic Regression/5. Logistic with multiple predictors.mp4
8.53 MB
10. Logistic Regression/5. Logistic with multiple predictors.srt
2.96 KB
10. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp4
26.25 MB
10. Logistic Regression/6. Training multiple predictor Logistic model in Python.srt
6.01 KB
10. Logistic Regression/7. Training multiple predictor Logistic model in R.mp4
15.78 MB
10. Logistic Regression/7. Training multiple predictor Logistic model in R.srt
2.02 KB
10. Logistic Regression/8. Confusion Matrix.mp4
21.1 MB
10. Logistic Regression/8. Confusion Matrix.srt
4.91 KB
10. Logistic Regression/9. Creating Confusion Matrix in Python.mp4
51.25 MB
10. Logistic Regression/9. Creating Confusion Matrix in Python.srt
10.85 KB
11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4
40.96 MB
11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.srt
11.89 KB
11. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4
11.4 MB
11. Linear Discriminant Analysis (LDA)/2. LDA in Python.srt
2.57 KB
11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp4
74.36 MB
11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.srt
10.22 KB
12. K-Nearest Neighbors classifier/1. Test-Train Split.mp4
39.3 MB
12. K-Nearest Neighbors classifier/1. Test-Train Split.srt
10.59 KB
12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp4
33.1 MB
12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.srt
7.39 KB
12. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp4
74.23 MB
12. K-Nearest Neighbors classifier/3. Test-Train Split in R.srt
9.81 KB
12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp4
75.42 MB
12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.srt
9.98 KB
12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp4
37.23 MB
12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.srt
5.83 KB
12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp4
42.36 MB
12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.srt
6.93 KB
12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp4
64.85 MB
12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.srt
8.98 KB
13. Comparing results from 3 models/1. Understanding the results of classification models.mp4
41.64 MB
13. Comparing results from 3 models/1. Understanding the results of classification models.srt
7.52 KB
13. Comparing results from 3 models/2. Summary of the three models.mp4
22.22 MB
13. Comparing results from 3 models/2. Summary of the three models.srt
5.96 KB
14. Simple Decision Trees/1. Basics of Decision Trees.mp4
42.64 MB
14. Simple Decision Trees/1. Basics of Decision Trees.srt
11.27 KB
14. Simple Decision Trees/10. Test-Train split in Python.mp4
24.87 MB
14. Simple Decision Trees/10. Test-Train split in Python.srt
6.17 KB
14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp4
43.98 MB
14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.srt
5.83 KB
14. Simple Decision Trees/12. Creating Decision tree in Python.mp4
17.87 MB
14. Simple Decision Trees/12. Creating Decision tree in Python.srt
4.31 KB
14. Simple Decision Trees/13. Building a Regression Tree in R.mp4
103.34 MB
14. Simple Decision Trees/13. Building a Regression Tree in R.srt
15.5 KB
14. Simple Decision Trees/14. Evaluating model performance in Python.mp4
16.44 MB
14. Simple Decision Trees/14. Evaluating model performance in Python.srt
4.73 KB
14. Simple Decision Trees/15. Plotting decision tree in Python.mp4
21.48 MB
14. Simple Decision Trees/15. Plotting decision tree in Python.srt
5.29 KB
14. Simple Decision Trees/16. Pruning a tree.mp4
18.46 MB
14. Simple Decision Trees/16. Pruning a tree.srt
4.54 KB
14. Simple Decision Trees/17. Pruning a tree in Python.mp4
73.5 MB
14. Simple Decision Trees/17. Pruning a tree in Python.srt
10.72 KB
14. Simple Decision Trees/18. Pruning a Tree in R.mp4
82.1 MB
14. Simple Decision Trees/18. Pruning a Tree in R.srt
9.66 KB
14. Simple Decision Trees/2. Understanding a Regression Tree.mp4
43.72 MB
14. Simple Decision Trees/2. Understanding a Regression Tree.srt
11.91 KB
14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.mp4
13.98 MB
14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.srt
3.51 KB
14. Simple Decision Trees/4. The Data set for this part.mp4
37.26 MB
14. Simple Decision Trees/4. The Data set for this part.srt
3.28 KB
14. Simple Decision Trees/5. Importing the Data set into Python.mp4
25.85 MB
14. Simple Decision Trees/5. Importing the Data set into Python.srt
5.88 KB
14. Simple Decision Trees/6. Importing the Data set into R.mp4
43.7 MB
14. Simple Decision Trees/6. Importing the Data set into R.srt
7.24 KB
14. Simple Decision Trees/7. Missing value treatment in Python.mp4
17.93 MB
14. Simple Decision Trees/7. Missing value treatment in Python.srt
3.73 KB
14. Simple Decision Trees/8. Dummy Variable creation in Python.mp4
24.94 MB
14. Simple Decision Trees/8. Dummy Variable creation in Python.srt
5.34 KB
14. Simple Decision Trees/9. Dependent- Independent Data split in Python.mp4
15.18 MB
14. Simple Decision Trees/9. Dependent- Independent Data split in Python.srt
4.24 KB
15. Simple Classification Tree/1. Classification tree.mp4
28.2 MB
15. Simple Classification Tree/1. Classification tree.srt
6.72 KB
15. Simple Classification Tree/2. The Data set for Classification problem.mp4
18.57 MB
15. Simple Classification Tree/2. The Data set for Classification problem.srt
1.91 KB
15. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp4
45.38 MB
15. Simple Classification Tree/3. Classification tree in Python Preprocessing.srt
8.92 KB
15. Simple Classification Tree/4. Classification tree in Python Training.mp4
82.72 MB
15. Simple Classification Tree/4. Classification tree in Python Training.srt
14.51 KB
15. Simple Classification Tree/5. Building a classification Tree in R.mp4
85.1 MB
15. Simple Classification Tree/5. Building a classification Tree in R.srt
10.13 KB
15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.mp4
6.86 MB
15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.srt
1.7 KB
16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp4
28.14 MB
16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.srt
7.27 KB
16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4
77.3 MB
16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.srt
12.28 KB
16. Ensemble technique 1 - Bagging/3. Bagging in R.mp4
58.96 MB
16. Ensemble technique 1 - Bagging/3. Bagging in R.srt
7.13 KB
17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp4
18.2 MB
17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.srt
4.59 KB
17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp4
46.7 MB
17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.srt
6.69 KB
17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4
80.67 MB
17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.srt
13.69 KB
17. Ensemble technique 2 - Random Forests/4. Random Forest in R.mp4
30.72 MB
17. Ensemble technique 2 - Random Forests/4. Random Forest in R.srt
4.77 KB
18. Ensemble technique 3 - Boosting/1. Boosting.mp4
30.58 MB
18. Ensemble technique 3 - Boosting/1. Boosting.srt
7.81 KB
18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp4
39.88 MB
18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.srt
5.44 KB
18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp4
69.09 MB
18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.srt
8.55 KB
18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.mp4
30.54 MB
18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.srt
4.42 KB
18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp4
88.67 MB
18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.srt
10.51 KB
18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp4
75.01 MB
18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.srt
11.43 KB
18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4
161.3 MB
18. Ensemble technique 3 - Boosting/7. XGBoosting in R.srt
18.43 KB
19. Maximum Margin Classifier/1. Content flow.mp4
8.64 MB
19. Maximum Margin Classifier/1. Content flow.srt
1.74 KB
19. Maximum Margin Classifier/2. The Concept of a Hyperplane.mp4
29.42 MB
19. Maximum Margin Classifier/2. The Concept of a Hyperplane.srt
5.31 KB
19. Maximum Margin Classifier/3. Maximum Margin Classifier.mp4
22.48 MB
19. Maximum Margin Classifier/3. Maximum Margin Classifier.srt
3.46 KB
19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.mp4
10.61 MB
19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.srt
2.64 KB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4
16.27 MB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt
2.65 KB
2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.mp4
40.37 MB
2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.srt
8.24 KB
2. Setting up Python and Jupyter Notebook/2. This is a milestone!.mp4
20.66 MB
2. Setting up Python and Jupyter Notebook/2. This is a milestone!.srt
3.78 KB
2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.mp4
65.19 MB
2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.srt
9.84 KB
2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.mp4
40.92 MB
2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.srt
13.2 KB
2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.mp4
12.74 MB
2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.srt
4.44 KB
2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.mp4
64.44 MB
2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.srt
17.97 KB
2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.mp4
60.33 MB
2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.srt
20.11 KB
2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.mp4
43.88 MB
2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.srt
11.85 KB
2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.mp4
46.88 MB
2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.srt
10.12 KB
20. Support Vector Classifier/1. Support Vector classifiers.mp4
56.17 MB
20. Support Vector Classifier/1. Support Vector classifiers.srt
10.85 KB
20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp4
10.8 MB
20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.srt
1.62 KB
21. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4
40.12 MB
21. Support Vector Machines/1. Kernel Based Support Vector Machines.srt
6.71 KB
22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp4
4.04 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
9.72 MB
22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.srt
1.89 KB
22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.mp4
64.13 MB
22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.srt
12.39 KB
22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.mp4
57.74 MB
22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.srt
10.79 KB
22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.mp4
22.92 MB
22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.srt
4.49 KB
22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.mp4
37.21 MB
22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.srt
7.26 KB
22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.mp4
37.2 MB
22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.srt
3.28 KB
22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.mp4
25.84 MB
22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.srt
5.88 KB
22. Creating Support Vector Machine Model in Python/4. X-y Split.mp4
15.18 MB
22. Creating Support Vector Machine Model in Python/4. X-y Split.srt
4.24 KB
22. Creating Support Vector Machine Model in Python/5. Test-Train Split.mp4
24.87 MB
22. Creating Support Vector Machine Model in Python/5. Test-Train Split.srt
6.17 KB
22. Creating Support Vector Machine Model in Python/6. Standardizing the data.mp4
38.41 MB
22. Creating Support Vector Machine Model in Python/6. Standardizing the data.srt
6.51 KB
22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.mp4
67.64 MB
22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.srt
10.45 KB
22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.mp4
18.56 MB
22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.srt
1.91 KB
22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.mp4
45.38 MB
22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.srt
8.92 KB
23. Creating Support Vector Machine Model in R/1. Importing Data into R.mp4
53.67 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
50.48 MB
23. Creating Support Vector Machine Model in R/2. Test-Train Split.srt
6.04 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
139.16 MB
23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.srt
17.75 KB
23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.mp4
60.5 MB
23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.srt
6.95 KB
23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.mp4
83.14 MB
23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.srt
11.49 KB
23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.mp4
56.68 MB
23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.srt
7.19 KB
23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.mp4
106.12 MB
23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.srt
12.05 KB
24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp4
29.07 MB
24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.srt
4.77 KB
24. Introduction - Deep Learning/2. Perceptron.mp4
44.75 MB
24. Introduction - Deep Learning/2. Perceptron.srt
10.22 KB
24. Introduction - Deep Learning/3. Activation Functions.mp4
34.62 MB
24. Introduction - Deep Learning/3. Activation Functions.srt
8.17 KB
24. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp4
86.56 MB
24. Introduction - Deep Learning/4. Python - Creating Perceptron model.srt
15.71 KB
25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4
40.42 MB
25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt
10.81 KB
25. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4
60.34 MB
25. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt
12.7 KB
25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4
122.2 MB
25. Neural Networks - Stacking cells to create network/3. Back Propagation.srt
24.77 KB
25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp4
62.18 MB
25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.srt
13.65 KB
25. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp4
45.36 MB
25. Neural Networks - Stacking cells to create network/5. Hyperparameter.srt
9.32 KB
26. ANN in Python/1. Keras and Tensorflow.mp4
14.92 MB
26. ANN in Python/1. Keras and Tensorflow.srt
3.78 KB
26. ANN in Python/10. Using Functional API for complex architectures.mp4
92.11 MB
26. ANN in Python/10. Using Functional API for complex architectures.srt
12.95 KB
26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4
151.59 MB
26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.srt
20.83 KB
26. ANN in Python/12. Hyperparameter Tuning.mp4
60.63 MB
26. ANN in Python/12. Hyperparameter Tuning.srt
9.81 KB
26. ANN in Python/2. Installing Tensorflow and Keras.mp4
20.06 MB
26. ANN in Python/2. Installing Tensorflow and Keras.srt
4.14 KB
26. ANN in Python/3. Dataset for classification.mp4
56.19 MB
26. ANN in Python/3. Dataset for classification.srt
7.9 KB
26. ANN in Python/4. Normalization and Test-Train split.mp4
44.2 MB
26. ANN in Python/4. Normalization and Test-Train split.srt
6.12 KB
26. ANN in Python/5. Different ways to create ANN using Keras.mp4
10.82 MB
26. ANN in Python/5. Different ways to create ANN using Keras.srt
1.98 KB
26. ANN in Python/6. Building the Neural Network using Keras.mp4
79.11 MB
26. ANN in Python/6. Building the Neural Network using Keras.srt
12.92 KB
26. ANN in Python/7. Compiling and Training the Neural Network model.mp4
81.63 MB
26. ANN in Python/7. Compiling and Training the Neural Network model.srt
10.03 KB
26. ANN in Python/8. Evaluating performance and Predicting using Keras.mp4
69.91 MB
26. ANN in Python/8. Evaluating performance and Predicting using Keras.srt
9.81 KB
26. ANN in Python/9. Building Neural Network for Regression Problem.mp4
155.9 MB
26. ANN in Python/9. Building Neural Network for Regression Problem.srt
23.75 KB
27. ANN in R/1. Installing Keras and Tensorflow.mp4
22.79 MB
27. ANN in R/1. Installing Keras and Tensorflow.srt
3.01 KB
27. ANN in R/2. Data Normalization and Test-Train Split.mp4
111.78 MB
27. ANN in R/2. Data Normalization and Test-Train Split.srt
12.87 KB
27. ANN in R/3. Building,Compiling and Training.mp4
130.74 MB
27. ANN in R/3. Building,Compiling and Training.srt
16.27 KB
27. ANN in R/4. Evaluating and Predicting.mp4
99.28 MB
27. ANN in R/4. Evaluating and Predicting.srt
10.11 KB
27. ANN in R/5. ANN with NeuralNets Package.mp4
84.42 MB
27. ANN in R/5. ANN with NeuralNets Package.srt
8.44 KB
27. ANN in R/6. Building Regression Model with Functional API.mp4
131.13 MB
27. ANN in R/6. Building Regression Model with Functional API.srt
13.54 KB
27. ANN in R/7. Complex Architectures using Functional API.mp4
79.57 MB
27. ANN in R/7. Complex Architectures using Functional API.srt
8.87 KB
27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4
216.03 MB
27. ANN in R/8. Saving - Restoring Models and Using Callbacks.srt
21.38 KB
28. CNN - Basics/1. CNN Introduction.mp4
51.16 MB
28. CNN - Basics/1. CNN Introduction.srt
8.13 KB
28. CNN - Basics/2. Stride.mp4
16.58 MB
28. CNN - Basics/2. Stride.srt
3.01 KB
28. CNN - Basics/3. Padding.mp4
31.63 MB
28. CNN - Basics/3. Padding.srt
4.95 KB
28. CNN - Basics/4. Filters and Feature maps.mp4
52.71 MB
28. CNN - Basics/4. Filters and Feature maps.srt
7.58 KB
28. CNN - Basics/5. Channels.mp4
67.77 MB
28. CNN - Basics/5. Channels.srt
6.24 KB
28. CNN - Basics/6. PoolingLayer.mp4
46.88 MB
28. CNN - Basics/6. PoolingLayer.srt
5.85 KB
29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp4
40.63 MB
29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.srt
5.74 KB
29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp4
43.26 MB
29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.srt
7.27 KB
29. Creating CNN model in Python/3. CNN model in Python - Training and results.mp4
55.15 MB
29. Creating CNN model in Python/3. CNN model in Python - Training and results.srt
6.41 KB
29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp4
57.97 MB
29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.srt
5.56 KB
3. Setting up R Studio and R crash course/1. Installing R and R studio.mp4
35.71 MB
3. Setting up R Studio and R crash course/1. Installing R and R studio.srt
6.79 KB
3. Setting up R Studio and R crash course/2. Basics of R and R studio.mp4
38.85 MB
3. Setting up R Studio and R crash course/2. Basics of R and R studio.srt
11.97 KB
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/3. Packages in R.srt
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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/4. Inputting data part 1 Inbuilt datasets of R.srt
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3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp4
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3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.srt
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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/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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3. Setting up R Studio and R crash course/7. Creating Barplots in R.srt
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3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp4
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3. Setting up R Studio and R crash course/8. Creating Histograms in R.srt
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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/2. Data Preprocessing.srt
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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/3. Creating Model Architecture.srt
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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/4. Compiling and training.srt
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30. Creating CNN model in R/5. Model Performance.mp4
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30. Creating CNN model in R/5. Model Performance.srt
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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/6. Comparison - Pooling vs Without Pooling in R.srt
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31. Project Creating CNN model from scratch in Python/1. Project - Introduction.mp4
49.39 MB
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
71.83 MB
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
87.76 MB
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
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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
41.42 MB
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
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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
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34. Transfer Learning Basics/1. ILSVRC.srt
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34. Transfer Learning Basics/2. LeNET.mp4
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34. Transfer Learning Basics/2. LeNET.srt
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34. Transfer Learning Basics/3. VGG16NET.mp4
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34. Transfer Learning Basics/3. VGG16NET.srt
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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/5. Transfer Learning.srt
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34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4
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34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.srt
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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/1. Project - Transfer Learning - VGG16 (Implementation).srt
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35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp4
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35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).srt
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36. Time Series Analysis and Forecasting/1. Introduction.mp4
12.27 MB
36. Time Series Analysis and Forecasting/1. Introduction.srt
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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/2. Time Series Forecasting - Use cases.srt
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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/4. Forecasting model creation - Steps 1 (Goal).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
108.87 MB
37. Time Series - Preprocessing in Python/1. Data Loading in Python.srt
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37. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp4
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37. Time Series - Preprocessing in Python/10. Exponential Smoothing.srt
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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/2. Time Series - Visualization Basics.srt
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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/6. Time Series - Upsampling and Downsampling.srt
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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/7. Time Series - Upsampling and Downsampling in Python.srt
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37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp4
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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
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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
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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
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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
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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
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39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.srt
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39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4
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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
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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
56.65 MB
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4. Basics of Statistics/1. Types of Data.mp4
21.76 MB
4. Basics of Statistics/1. Types of Data.srt
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4. Basics of Statistics/2. Types of Statistics.mp4
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4. Basics of Statistics/3. Describing data Graphically.mp4
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4. Basics of Statistics/4. Measures of Centers.mp4
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4. Basics of Statistics/5. Measures of Dispersion.mp4
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4. Basics of Statistics/5. Measures of Dispersion.srt
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40. Time Series - ARIMA model/1. ACF and PACF.mp4
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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
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40. Time Series - ARIMA model/3. ARIMA model in Python.srt
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40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4
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41. Time Series - SARIMA model/1. SARIMA model.mp4
39.03 MB
41. Time Series - SARIMA model/1. SARIMA model.srt
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41. Time Series - SARIMA model/2. SARIMA model in Python.mp4
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41. Time Series - SARIMA model/2. SARIMA model in Python.srt
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41. Time Series - SARIMA model/3. Stationary time Series.mp4
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41. Time Series - SARIMA model/3. Stationary time Series.srt
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42. Bonus Section/1. The final milestone!.mp4
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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
109.18 MB
5. Introduction to Machine Learning/1. Introduction to Machine Learning.srt
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5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4
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6. Data Preprocessing/1. Gathering Business Knowledge.mp4
22.29 MB
6. Data Preprocessing/1. Gathering Business Knowledge.srt
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6. Data Preprocessing/10. Outlier Treatment in Python.mp4
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6. Data Preprocessing/10. Outlier Treatment in Python.srt
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6. Data Preprocessing/11. Outlier Treatment in R.mp4
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6. Data Preprocessing/12. Missing Value Imputation.mp4
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6. Data Preprocessing/13. Missing Value Imputation in Python.mp4
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6. Data Preprocessing/14. Missing Value imputation in R.mp4
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6. Data Preprocessing/15. Seasonality in Data.mp4
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6. Data Preprocessing/15. Seasonality in Data.srt
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6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4
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6. Data Preprocessing/17. Variable transformation and deletion in Python.mp4
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6. Data Preprocessing/17. Variable transformation and deletion in Python.srt
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6. Data Preprocessing/18. Variable transformation in R.mp4
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6. Data Preprocessing/18. Variable transformation in R.srt
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6. Data Preprocessing/19. Non-usable variables.mp4
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6. Data Preprocessing/19. Non-usable variables.srt
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6. Data Preprocessing/2. Data Exploration.mp4
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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
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6. Data Preprocessing/22. Dummy variable creation in R.mp4
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6. Data Preprocessing/22. Dummy variable creation in R.srt
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6. Data Preprocessing/23. Correlation Analysis.mp4
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6. Data Preprocessing/23. Correlation Analysis.srt
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6. Data Preprocessing/24. Correlation Analysis in Python.mp4
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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/4. Importing Data in Python.srt
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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/6. Univariate analysis and EDD.srt
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6. Data Preprocessing/7. EDD in Python.mp4
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6. Data Preprocessing/8. EDD in R.mp4
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6. Data Preprocessing/9. Outlier Treatment.mp4
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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/10. Multiple Linear Regression in Python.srt
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7. Linear Regression/11. Multiple Linear Regression in R.mp4
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7. Linear Regression/11. Multiple Linear Regression in R.srt
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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/16. Regression models other than OLS.srt
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7. Linear Regression/17. Subset selection techniques.mp4
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7. Linear Regression/17. Subset selection techniques.srt
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7. Linear Regression/18. Subset selection in R.mp4
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7. Linear Regression/18. Subset selection in R.srt
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7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp4
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7. Linear Regression/19. Shrinkage methods Ridge and Lasso.srt
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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
128.85 MB
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7. Linear Regression/21. Ridge regression and Lasso in R.mp4
103.43 MB
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7. Linear Regression/22. Heteroscedasticity.mp4
14.49 MB
7. Linear Regression/22. Heteroscedasticity.srt
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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/4. Assessing Model Accuracy RSE and R squared.srt
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7. Linear Regression/5. Simple Linear Regression in Python.mp4
63.43 MB
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7. Linear Regression/6. Simple Linear Regression in R.mp4
40.83 MB
7. Linear Regression/6. Simple Linear Regression in R.srt
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7. Linear Regression/7. Multiple Linear Regression.mp4
34.32 MB
7. Linear Regression/7. Multiple Linear Regression.srt
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7. Linear Regression/8. The F - statistic.mp4
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7. Linear Regression/8. The F - statistic.srt
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7. Linear Regression/9. Interpreting results of Categorical variables.mp4
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8. Classification Models Data Preparation/1. The Data and the Data Dictionary.mp4
79.01 MB
8. Classification Models Data Preparation/1. The Data and the Data Dictionary.srt
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8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.mp4
29.26 MB
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8. Classification Models Data Preparation/11. Variable transformation in R.mp4
38.03 MB
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
26.37 MB
8. Classification Models Data Preparation/12. Dummy variable creation in Python.srt
6.15 KB
8. Classification Models Data Preparation/13. Dummy variable creation in R.mp4
44.36 MB
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8. Classification Models Data Preparation/2. Data Import in Python.mp4
22.06 MB
8. Classification Models Data Preparation/2. Data Import in Python.srt
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8. Classification Models Data Preparation/3. Importing the dataset into R.mp4
13.47 MB
8. Classification Models Data Preparation/3. Importing the dataset into R.srt
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8. Classification Models Data Preparation/4. EDD in Python.mp4
77.63 MB
8. Classification Models Data Preparation/4. EDD in Python.srt
17.77 KB
8. Classification Models Data Preparation/5. EDD in R.mp4
66.52 MB
8. Classification Models Data Preparation/5. EDD in R.srt
11.37 KB
8. Classification Models Data Preparation/6. Outlier treatment in Python.mp4
47.32 MB
8. Classification Models Data Preparation/6. Outlier treatment in Python.srt
9.55 KB
8. Classification Models Data Preparation/7. Outlier Treatment in R.mp4
25.37 MB
8. Classification Models Data Preparation/7. Outlier Treatment in R.srt
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8. Classification Models Data Preparation/8. Missing Value Imputation in Python.mp4
22.56 MB
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8. Classification Models Data Preparation/9. Missing Value imputation in R.mp4
19.05 MB
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9. The Three classification models/1. Three Classifiers and the problem statement.mp4
20.34 MB
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9. The Three classification models/2. Why can't we use Linear Regression.mp4
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