Udemy Machine Learning Deep Learning in Python R Giga Course
Torrent Info
| Category | Other |
| Added | Sunday 22nd October 2023 07:32:30 AM (3 years ago) |
| Size | 12.6 GB |
| Peers | 1 Peers (0 Seeders and 1 leechers) update |
| Downloaded | 267 times |
| Uploader | CourseClub Thank You (0) Report Torrent 00/0 |
| Comments | None |
| Uploader Comments | None |
| Tags | English |
| Seeders | 0 |
| Leechers | 1 |
Torrent Screenshots
There are no images posted for this torrent.
NFO
No NFO was added for this torrent.
| Filename | Size |
|---|---|
| [CourseClub.Me].url | 122 bytes |
| [GigaCourse.Com].url | 49 bytes |
| 1. Introduction.mp4 | 29.4 MB |
| 1. Introduction.srt | 4.6 KB |
| 2. Course Resources.html | 370 bytes |
| 1. Linear Discriminant Analysis.mp4 | 41.0 MB |
| 1. Linear Discriminant Analysis.srt | 12.3 KB |
| 2. LDA in Python.mp4 | 11.4 MB |
| 2. LDA in Python.srt | 2.6 KB |
| 3. Linear Discriminant Analysis in R.mp4 | 74.3 MB |
| 3. Linear Discriminant Analysis in R.srt | 10.5 KB |
| 1. Test-Train Split.mp4 | 39.3 MB |
| 1. Test-Train Split.srt | 11.0 KB |
| 2. Test-Train Split in Python.mp4 | 33.1 MB |
| 2. Test-Train Split in Python.srt | 7.6 KB |
| 3. Test-Train Split in R.mp4 | 74.2 MB |
| 3. Test-Train Split in R.srt | 10.3 KB |
| 4. K-Nearest Neighbors classifier.mp4 | 75.4 MB |
| 4. K-Nearest Neighbors classifier.srt | 10.3 KB |
| 5. K-Nearest Neighbors in Python Part 1.mp4 | 37.2 MB |
| 5. K-Nearest Neighbors in Python Part 1.srt | 5.9 KB |
| 6. K-Nearest Neighbors in Python Part 2.mp4 | 42.4 MB |
| 6. K-Nearest Neighbors in Python Part 2.srt | 6.9 KB |
| 7. K-Nearest Neighbors in R.mp4 | 64.9 MB |
| 7. K-Nearest Neighbors in R.srt | 9.4 KB |
| 1. Understanding the results of classification models.mp4 | 41.6 MB |
| 1. Understanding the results of classification models.srt | 7.8 KB |
| 2. Summary of the three models.mp4 | 22.2 MB |
| 2. Summary of the three models.srt | 6.2 KB |
| 1. Introduction to Decision trees.mp4 | 44.8 MB |
| 1. Introduction to Decision trees.srt | 4.7 KB |
| 10. Test-Train split in Python.mp4 | 25.6 MB |
| 10. Test-Train split in Python.srt | 5.3 KB |
| 11. Splitting Data into Test and Train Set in R.mp4 | 44.0 MB |
| 11. Splitting Data into Test and Train Set in R.srt | 7.3 KB |
| 12. Creating Decision tree in Python.mp4 | 17.9 MB |
| 12. Creating Decision tree in Python.srt | 4.3 KB |
| 13. Building a Regression Tree in R.mp4 | 103.3 MB |
| 13. Building a Regression Tree in R.srt | 18.9 KB |
| 14. Evaluating model performance in Python.mp4 | 16.4 MB |
| 14. Evaluating model performance in Python.srt | 4.8 KB |
| 15. Plotting decision tree in Python.mp4 | 21.5 MB |
| 15. Plotting decision tree in Python.srt | 5.5 KB |
| 16. Pruning a tree.mp4 | 18.5 MB |
| 16. Pruning a tree.srt | 5.4 KB |
| 17. Pruning a tree in Python.mp4 | 73.5 MB |
| 17. Pruning a tree in Python.srt | 11.1 KB |
| 18. Pruning a Tree in R.mp4 | 82.1 MB |
| 18. Pruning a Tree in R.srt | 11.8 KB |
| 2. Basics of Decision Trees.mp4 | 42.7 MB |
| 2. Basics of Decision Trees.srt | 13.2 KB |
| 3. Understanding a Regression Tree.mp4 | 43.7 MB |
| 3. Understanding a Regression Tree.srt | 14.0 KB |
| 4. The stopping criteria for controlling tree growth.mp4 | 14.0 MB |
| 4. The stopping criteria for controlling tree growth.srt | 4.3 KB |
| 5. Importing the Data set into Python.mp4 | 15.9 MB |
| 5. Importing the Data set into Python.srt | 3.1 KB |
| 6. Importing the Data set into R.mp4 | 43.7 MB |
| 6. Importing the Data set into R.srt | 8.8 KB |
| 7. Missing value treatment in Python.mp4 | 12.9 MB |
| 7. Missing value treatment in Python.srt | 2.3 KB |
| 8. Dummy Variable creation in Python.mp4 | 24.6 MB |
| 8. Dummy Variable creation in Python.srt | 4.5 KB |
| 9. Dependent- Independent Data split in Python.mp4 | 16.9 MB |
| 9. Dependent- Independent Data split in Python.srt | 3.8 KB |
| 1. Classification tree.mp4 | 28.2 MB |
| 1. Classification tree.srt | 8.1 KB |
| 2. The Data set for Classification problem.mp4 | 18.6 MB |
| 2. The Data set for Classification problem.srt | 2.4 KB |
| 3. Classification tree in Python Preprocessing.mp4 | 45.4 MB |
| 3. Classification tree in Python Preprocessing.srt | 9.1 KB |
| 4. Classification tree in Python Training.mp4 | 82.7 MB |
| 4. Classification tree in Python Training.srt | 14.9 KB |
| 5. Building a classification Tree in R.mp4 | 85.1 MB |
| 5. Building a classification Tree in R.srt | 11.9 KB |
| 6. Advantages and Disadvantages of Decision Trees.mp4 | 6.9 MB |
| 6. Advantages and Disadvantages of Decision Trees.srt | 2.2 KB |
| 1. Ensemble technique 1 - Bagging.mp4 | 28.1 MB |
| 1. Ensemble technique 1 - Bagging.srt | 7.6 KB |
| 2. Ensemble technique 1 - Bagging in Python.mp4 | 77.3 MB |
| 2. Ensemble technique 1 - Bagging in Python.srt | 12.6 KB |
| 3. Bagging in R.mp4 | 59.0 MB |
| 3. Bagging in R.srt | 8.2 KB |
| 1. Ensemble technique 2 - Random Forests.mp4 | 18.2 MB |
| 1. Ensemble technique 2 - Random Forests.srt | 5.1 KB |
| 2. Ensemble technique 2 - Random Forests in Python.mp4 | 46.7 MB |
| 2. Ensemble technique 2 - Random Forests in Python.srt | 6.9 KB |
| 3. Using Grid Search in Python.mp4 | 80.7 MB |
| 3. Using Grid Search in Python.srt | 14.0 KB |
| 4. Random Forest in R.mp4 | 30.7 MB |
| 4. Random Forest in R.srt | 5.6 KB |
| 1. Boosting.mp4 | 30.6 MB |
| 1. Boosting.srt | 9.6 KB |
| 2. Ensemble technique 3a - Boosting in Python.mp4 | 39.9 MB |
| 2. Ensemble technique 3a - Boosting in Python.srt | 5.6 KB |
| 3. Gradient Boosting in R.mp4 | 69.1 MB |
| 3. Gradient Boosting in R.srt | 9.6 KB |
| 4. Ensemble technique 3b - AdaBoost in Python.mp4 | 30.5 MB |
| 4. Ensemble technique 3b - AdaBoost in Python.srt | 4.5 KB |
| 5. AdaBoosting in R.mp4 | 88.7 MB |
| 5. AdaBoosting in R.srt | 12.2 KB |
| 6. Ensemble technique 3c - XGBoost in Python.mp4 | 75.0 MB |
| 6. Ensemble technique 3c - XGBoost in Python.srt | 11.6 KB |
| 7. XGBoosting in R.mp4 | 161.3 MB |
| 7. XGBoosting in R.srt | 21.1 KB |
| 1. Introduction to SVM's.mp4 | 21.6 MB |
| 1. Introduction to SVM's.srt | 3.3 KB |
| 2. The Concept of a Hyperplane.mp4 | 29.4 MB |
| 2. The Concept of a Hyperplane.srt | 6.2 KB |
| 3. Maximum Margin Classifier.mp4 | 22.5 MB |
| 3. Maximum Margin Classifier.srt | 4.4 KB |
| 4. Limitations of Maximum Margin Classifier.mp4 | 10.6 MB |
| 4. Limitations of Maximum Margin Classifier.srt | 3.1 KB |
| 1. Support Vector classifiers.mp4 | 56.2 MB |
| 1. Support Vector classifiers.srt | 12.5 KB |
| 2. Limitations of Support Vector Classifiers.mp4 | 10.8 MB |
| 2. Limitations of Support Vector Classifiers.srt | 1.9 KB |
| 1. Installing Python and Anaconda.mp4 | 16.3 MB |
| 1. Installing Python and Anaconda.srt | 2.7 KB |
| 10. Working with Seaborn Library of Python.mp4 | 40.4 MB |
| 10. Working with Seaborn Library of Python.srt | 9.1 KB |
| 2. This is a milestone!.mp4 | 20.7 MB |
| 2. This is a milestone!.srt | 3.9 KB |
| 3. Opening Jupyter Notebook.mp4 | 65.2 MB |
| 3. Opening Jupyter Notebook.srt | 10.1 KB |
| 4. Introduction to Jupyter.mp4 | 40.9 MB |
| 4. Introduction to Jupyter.srt | 15.5 KB |
| 5. Arithmetic operators in Python Python Basics.mp4 | 12.7 MB |
| 5. Arithmetic operators in Python Python Basics.srt | 4.6 KB |
| 6. Strings in Python Python Basics.mp4 | 64.4 MB |
| 6. Strings in Python Python Basics.srt | 18.6 KB |
| 7. Lists, Tuples and Directories Python Basics.mp4 | 60.3 MB |
| 7. Lists, Tuples and Directories Python Basics.srt | 22.2 KB |
| 8. Working with Numpy Library of Python.mp4 | 43.9 MB |
| 8. Working with Numpy Library of Python.srt | 12.8 KB |
| 9. Working with Pandas Library of Python.mp4 | 46.9 MB |
| 9. Working with Pandas Library of Python.srt | 10.3 KB |
| 1. Kernel Based Support Vector Machines.mp4 | 40.1 MB |
| 1. Kernel Based Support Vector Machines.srt | 8.5 KB |
| 1. Regression and Classification Models.mp4 | 4.0 MB |
| 1. Regression and Classification Models.srt | 817 bytes |
| 10. Radial Kernel with Hyperparameter Tuning.mp4 | 37.2 MB |
| 10. Radial Kernel with Hyperparameter Tuning.srt | 7.2 KB |
| 2. Importing and preprocessing data in Python.mp4 | 26.4 MB |
| 2. Importing and preprocessing data in Python.srt | 4.5 KB |
| 3. Standardizing the data.mp4 | 38.4 MB |
| 3. Standardizing the data.srt | 6.7 KB |
| 4. SVM based Regression Model in Python.mp4 | 67.6 MB |
| 4. SVM based Regression Model in Python.srt | 10.6 KB |
| 5. Classification model - Preprocessing.mp4 | 45.4 MB |
| 5. Classification model - Preprocessing.srt | 9.2 KB |
| 6. Classification model - Standardizing the data.mp4 | 9.7 MB |
| 6. Classification model - Standardizing the data.srt | 1.9 KB |
| 7. SVM Based classification model.mp4 | 64.1 MB |
| 7. SVM Based classification model.srt | 12.7 KB |
| 8. Hyper Parameter Tuning.mp4 | 57.7 MB |
| 8. Hyper Parameter Tuning.srt | 10.9 KB |
| 9. Polynomial Kernel with Hyperparameter Tuning.mp4 | 22.9 MB |
| 9. Polynomial Kernel with Hyperparameter Tuning.srt | 4.4 KB |
| 1. Importing and preprocessing data in R.mp4 | 25.0 MB |
| 1. Importing and preprocessing data in R.srt | 2.8 KB |
| 2. More about test-train split.html | 559 bytes |
| 3. Classification SVM model using Linear Kernel.mp4 | 139.2 MB |
| 3. Classification SVM model using Linear Kernel.srt | 18.4 KB |
| 4. Hyperparameter Tuning for Linear Kernel.mp4 | 60.5 MB |
| 4. Hyperparameter Tuning for Linear Kernel.srt | 7.2 KB |
| 5. Polynomial Kernel with Hyperparameter Tuning.mp4 | 83.1 MB |
| 5. Polynomial Kernel with Hyperparameter Tuning.srt | 11.8 KB |
| 6. Radial Kernel with Hyperparameter Tuning.mp4 | 56.7 MB |
| 6. Radial Kernel with Hyperparameter Tuning.srt | 7.4 KB |
| 7. SVM based Regression Model in R.mp4 | 106.1 MB |
| 7. SVM based Regression Model in R.srt | 12.5 KB |
| 1. Introduction to Neural Networks and Course flow.mp4 | 29.1 MB |
| 1. Introduction to Neural Networks and Course flow.srt | 5.0 KB |
| 2. Perceptron.mp4 | 44.8 MB |
| 2. Perceptron.srt | 10.7 KB |
| 3. Activation Functions.mp4 | 34.6 MB |
| 3. Activation Functions.srt | 8.5 KB |
| 4. Python - Creating Perceptron model.mp4 | 86.6 MB |
| 4. Python - Creating Perceptron model.srt | 16.2 KB |
| 1. Basic Terminologies.mp4 | 40.4 MB |
| 1. Basic Terminologies.srt | 11.3 KB |
| 2. Gradient Descent.mp4 | 60.3 MB |
| 2. Gradient Descent.srt | 13.2 KB |
| 3. Back Propagation.mp4 | 122.2 MB |
| 3. Back Propagation.srt | 25.9 KB |
| 4. Some Important Concepts.mp4 | 62.2 MB |
| 4. Some Important Concepts.srt | 14.2 KB |
| 5. Hyperparameter.mp4 | 45.4 MB |
| 5. Hyperparameter.srt | 9.7 KB |
| 1. Keras and Tensorflow.mp4 | 14.9 MB |
| 1. Keras and Tensorflow.srt | 3.9 KB |
| 10. Using Functional API for complex architectures.mp4 | 92.1 MB |
| 10. Using Functional API for complex architectures.srt | 13.4 KB |
| 11. Saving - Restoring Models and Using Callbacks.mp4 | 151.6 MB |
| 11. Saving - Restoring Models and Using Callbacks.srt | 21.6 KB |
| 12. Hyperparameter Tuning.mp4 | 60.6 MB |
| 12. Hyperparameter Tuning.srt | 10.2 KB |
| 2. Installing Tensorflow and Keras.mp4 | 20.1 MB |
| 2. Installing Tensorflow and Keras.srt | 4.3 KB |
| 3. Dataset for classification.mp4 | 56.2 MB |
| 3. Dataset for classification.srt | 8.2 KB |
| 4. Normalization and Test-Train split.mp4 | 44.2 MB |
| 4. Normalization and Test-Train split.srt | 6.3 KB |
| 5. Different ways to create ANN using Keras.mp4 | 10.8 MB |
| 5. Different ways to create ANN using Keras.srt | 2.0 KB |
| 6. Building the Neural Network using Keras.mp4 | 79.1 MB |
| 6. Building the Neural Network using Keras.srt | 13.3 KB |
| 7. Compiling and Training the Neural Network model.mp4 | 81.6 MB |
| 7. Compiling and Training the Neural Network model.srt | 10.4 KB |
| 8. Evaluating performance and Predicting using Keras.mp4 | 69.9 MB |
| 8. Evaluating performance and Predicting using Keras.srt | 10.1 KB |
| 9. Building Neural Network for Regression Problem.mp4 | 155.9 MB |
| 9. Building Neural Network for Regression Problem.srt | 24.7 KB |
| 1. Installing Keras and Tensorflow.mp4 | 22.8 MB |
| 1. Installing Keras and Tensorflow.srt | 3.1 KB |
| 2. Data Normalization and Test-Train Split.mp4 | 111.8 MB |
| 2. Data Normalization and Test-Train Split.srt | 13.4 KB |
| 3. Building,Compiling and Training.mp4 | 130.7 MB |
| 3. Building,Compiling and Training.srt | 16.9 KB |
| 4. Evaluating and Predicting.mp4 | 99.3 MB |
| 4. Evaluating and Predicting.srt | 10.5 KB |
| 5. ANN with NeuralNets Package.mp4 | 84.4 MB |
| 5. ANN with NeuralNets Package.srt | 8.8 KB |
| 6. Building Regression Model with Functional API.mp4 | 131.1 MB |
| 6. Building Regression Model with Functional API.srt | 14.2 KB |
| 7. Complex Architectures using Functional API.mp4 | 79.6 MB |
| 7. Complex Architectures using Functional API.srt | 9.2 KB |
| 8. Saving - Restoring Models and Using Callbacks.mp4 | 216.0 MB |
| 8. Saving - Restoring Models and Using Callbacks.srt | 22.3 KB |
| 1. CNN Introduction.mp4 | 56.8 MB |
| 1. CNN Introduction.srt | 8.3 KB |
| 2. Stride.mp4 | 16.6 MB |
| 2. Stride.srt | 3.1 KB |
| 3. Padding.mp4 | 31.6 MB |
| 3. Padding.srt | 5.1 KB |
| 4. Filters and Feature maps.mp4 | 52.7 MB |
| 4. Filters and Feature maps.srt | 7.9 KB |
| 5. Channels.mp4 | 67.8 MB |
| 5. Channels.srt | 6.5 KB |
| 6. PoolingLayer.mp4 | 46.9 MB |
| 6. PoolingLayer.srt | 6.1 KB |
| 1. CNN model in Python - Preprocessing.mp4 | 40.6 MB |
| 1. CNN model in Python - Preprocessing.srt | 5.9 KB |
| 2. CNN model in Python - structure and Compile.mp4 | 43.3 MB |
| 2. CNN model in Python - structure and Compile.srt | 7.5 KB |
| 3. CNN model in Python - Training and results.mp4 | 55.2 MB |
| 3. CNN model in Python - Training and results.srt | 6.6 KB |
| 4. Comparison - Pooling vs Without Pooling in Python.mp4 | 58.0 MB |
| 4. Comparison - Pooling vs Without Pooling in Python.srt | 5.8 KB |
| 1. CNN on MNIST Fashion Dataset - Model Architecture.mp4 | 7.4 MB |
| 1. CNN on MNIST Fashion Dataset - Model Architecture.srt | 2.5 KB |
| 2. Data Preprocessing.mp4 | 67.0 MB |
| 2. Data Preprocessing.srt | 7.8 KB |
| 3. Creating Model Architecture.mp4 | 71.6 MB |
| 3. Creating Model Architecture.srt | 6.5 KB |
| 4. Compiling and training.mp4 | 32.2 MB |
| 4. Compiling and training.srt | 3.3 KB |
| 5. Model Performance.mp4 | 68.1 MB |
| 5. Model Performance.srt | 6.8 KB |
| 6. Comparison - Pooling vs Without Pooling in R.mp4 | 44.6 MB |
| 6. Comparison - Pooling vs Without Pooling in R.srt | 4.3 KB |
| 1. Installing R and R studio.mp4 | 35.7 MB |
| 1. Installing R and R studio.srt | 7.4 KB |
| 2. Basics of R and R studio.mp4 | 38.8 MB |
| 2. Basics of R and R studio.srt | 14.4 KB |
| 3. Packages in R.mp4 | 82.9 MB |
| 3. Packages in R.srt | 14.6 KB |
| 4. Inputting data part 1 Inbuilt datasets of R.mp4 | 40.7 MB |
| 4. Inputting data part 1 Inbuilt datasets of R.srt | 5.6 KB |
| 5. Inputting data part 2 Manual data entry.mp4 | 25.5 MB |
| 5. Inputting data part 2 Manual data entry.srt | 3.7 KB |
| 6. Inputting data part 3 Importing from CSV or Text files.mp4 | 60.1 MB |
| 6. Inputting data part 3 Importing from CSV or Text files.srt | 8.4 KB |
| 7. Creating Barplots in R.mp4 | 96.7 MB |
| 7. Creating Barplots in R.srt | 18.3 KB |
| 8. Creating Histograms in R.mp4 | 42.0 MB |
| 8. Creating Histograms in R.srt | 7.6 KB |
| 1. Project - Introduction.mp4 | 49.4 MB |
| 1. Project - Introduction.srt | 7.8 KB |
| 2. Data for the project.html | 232 bytes |
| 3. Project - Data Preprocessing in Python.mp4 | 71.8 MB |
| 3. Project - Data Preprocessing in Python.srt | 9.4 KB |
| 4. Project - Training CNN model in Python.mp4 | 66.0 MB |
| 4. Project - Training CNN model in Python.srt | 9.4 KB |
| 5. Project in Python - model results.mp4 | 21.0 MB |
| 5. Project in Python - model results.srt | 3.0 KB |
| 1. Project in R - Data Preprocessing.mp4 | 87.8 MB |
| 1. Project in R - Data Preprocessing.srt | 12.5 KB |
| 2. CNN Project in R - Structure and Compile.mp4 | 46.1 MB |
| 2. CNN Project in R - Structure and Compile.srt | 5.8 KB |
| 3. Project in R - Training.mp4 | 24.6 MB |
| 3. Project in R - Training.srt | 3.2 KB |
| 4. Project in R - Model Performance.mp4 | 23.2 MB |
| 4. Project in R - Model Performance.srt | 2.6 KB |
| 5. Project in R - Data Augmentation.mp4 | 56.4 MB |
| 5. Project in R - Data Augmentation.srt | 8.2 KB |
| 6. Project in R - Validation Performance.mp4 | 23.7 MB |
| 6. Project in R - Validation Performance.srt | 2.7 KB |
| 1. Project - Data Augmentation Preprocessing.mp4 | 41.4 MB |
| 1. Project - Data Augmentation Preprocessing.srt | 7.5 KB |
| 2. Project - Data Augmentation Training and Results.mp4 | 53.0 MB |
| 2. Project - Data Augmentation Training and Results.srt | 7.0 KB |
| 1. ILSVRC.mp4 | 20.9 MB |
| 1. ILSVRC.srt | 4.7 KB |
| 2. LeNET.mp4 | 7.0 MB |
| 2. LeNET.srt | 1.9 KB |
| 3. VGG16NET.mp4 | 10.4 MB |
| 3. VGG16NET.srt | 2.0 KB |
| 4. GoogLeNet.mp4 | 21.4 MB |
| 4. GoogLeNet.srt | 3.3 KB |
| 5. Transfer Learning.mp4 | 30.0 MB |
| 5. Transfer Learning.srt | 5.6 KB |
| 6. Project - Transfer Learning - VGG16.mp4 | 129.1 MB |
| 6. Project - Transfer Learning - VGG16.srt | 21.4 KB |
| 1. Project - Transfer Learning - VGG16 (Implementation).mp4 | 101.6 MB |
| 1. Project - Transfer Learning - VGG16 (Implementation).srt | 14.8 KB |
| 2. Project - Transfer Learning - VGG16 (Performance).mp4 | 64.1 MB |
| 2. Project - Transfer Learning - VGG16 (Performance).srt | 9.1 KB |
| 1. Introduction.mp4 | 18.7 MB |
| 1. Introduction.srt | 2.9 KB |
| 2. Time Series Forecasting - Use cases.mp4 | 25.9 MB |
| 2. Time Series Forecasting - Use cases.srt | 2.6 KB |
| 3. Forecasting model creation - Steps.mp4 | 10.1 MB |
| 3. Forecasting model creation - Steps.srt | 3.0 KB |
| 4. Forecasting model creation - Steps 1 (Goal).mp4 | 34.5 MB |
| 4. Forecasting model creation - Steps 1 (Goal).srt | 6.7 KB |
| 5. Time Series - Basic Notations.mp4 | 62.5 MB |
| 5. Time Series - Basic Notations.srt | 9.9 KB |
| 1. Data Loading in Python.mp4 | 108.9 MB |
| 1. Data Loading in Python.srt | 18.5 KB |
| 10. Exponential Smoothing.mp4 | 8.4 MB |
| 10. Exponential Smoothing.srt | 2.2 KB |
| 2. Time Series - Visualization Basics.mp4 | 63.7 MB |
| 2. Time Series - Visualization Basics.srt | 10.6 KB |
| 3. Time Series - Visualization in Python.mp4 | 165.2 MB |
| 3. Time Series - Visualization in Python.srt | 30.4 KB |
| 4. Time Series - Feature Engineering Basics.mp4 | 59.5 MB |
| 4. Time Series - Feature Engineering Basics.srt | 12.2 KB |
| 5. Time Series - Feature Engineering in Python.mp4 | 112.7 MB |
| 5. Time Series - Feature Engineering in Python.srt | 20.2 KB |
| 6. Time Series - Upsampling and Downsampling.mp4 | 17.0 MB |
| 6. Time Series - Upsampling and Downsampling.srt | 4.4 KB |
| 7. Time Series - Upsampling and Downsampling in Python.mp4 | 100.7 MB |
| 7. Time Series - Upsampling and Downsampling in Python.srt | 18.3 KB |
| 8. Time Series - Power Transformation.mp4 | 14.8 MB |
| 8. Time Series - Power Transformation.srt | 2.8 KB |
| 9. Moving Average.mp4 | 38.7 MB |
| 9. Moving Average.srt | 8.1 KB |
| 1. White Noise.mp4 | 11.4 MB |
| 1. White Noise.srt | 2.6 KB |
| 2. Random Walk.mp4 | 21.2 MB |
| 2. Random Walk.srt | 4.8 KB |
| 3. Decomposing Time Series in Python.mp4 | 59.8 MB |
| 3. Decomposing Time Series in Python.srt | 10.7 KB |
| 4. Differencing.mp4 | 32.4 MB |
| 4. Differencing.srt | 6.9 KB |
| 5. Differencing in Python.mp4 | 113.0 MB |
| 5. Differencing in Python.srt | 16.2 KB |
| 1. Test Train Split in Python.mp4 | 57.4 MB |
| 1. Test Train Split in Python.srt | 12.3 KB |
| 2. Naive (Persistence) model in Python.mp4 | 43.4 MB |
| 2. Naive (Persistence) model in Python.srt | 8.3 KB |
| 3. Auto Regression Model - Basics.mp4 | 16.9 MB |
| 3. Auto Regression Model - Basics.srt | 3.7 KB |
| 4. Auto Regression Model creation in Python.mp4 | 53.5 MB |
| 4. Auto Regression Model creation in Python.srt | 10.4 KB |
| 5. Auto Regression with Walk Forward validation in Python.mp4 | 49.6 MB |
| 5. Auto Regression with Walk Forward validation in Python.srt | 9.0 KB |
| 6. Moving Average model -Basics.mp4 | 24.1 MB |
| 6. Moving Average model -Basics.srt | 5.2 KB |
| 7. Moving Average model in Python.mp4 | 56.7 MB |
| 7. Moving Average model in Python.srt | 9.8 KB |
| 1. ACF and PACF.mp4 | 41.2 MB |
| 1. ACF and PACF.srt | 8.9 KB |
| 2. ARIMA model - Basics.mp4 | 21.4 MB |
| 2. ARIMA model - Basics.srt | 5.2 KB |
| 3. ARIMA model in Python.mp4 | 74.4 MB |
| 3. ARIMA model in Python.srt | 14.7 KB |
| 4. ARIMA model with Walk Forward Validation in Python.mp4 | 32.2 MB |
| 4. ARIMA model with Walk Forward Validation in Python.srt | 6.3 KB |
| 1. Types of Data.mp4 | 21.8 MB |
| 1. Types of Data.srt | 5.2 KB |
| 2. Types of Statistics.mp4 | 10.9 MB |
| 2. Types of Statistics.srt | 3.3 KB |
| 3. Describing data Graphically.mp4 | 65.4 MB |
| 3. Describing data Graphically.srt | 13.2 KB |
| 4. Measures of Centers.mp4 | 38.6 MB |
| 4. Measures of Centers.srt | 8.1 KB |
| 5. Measures of Dispersion.mp4 | 22.9 MB |
| 5. Measures of Dispersion.srt | 5.3 KB |
| 1. SARIMA model.mp4 | 39.0 MB |
| 1. SARIMA model.srt | 8.2 KB |
| 2. SARIMA model in Python.mp4 | 66.2 MB |
| 2. SARIMA model in Python.srt | 12.1 KB |
| 3. Stationary time Series.mp4 | 5.6 MB |
| 3. Stationary time Series.srt | 1.7 KB |
| 4. The final milestone!.mp4 | 11.8 MB |
| 4. The final milestone!.srt | 1.8 KB |
| 1. Bonus Lecture.html | 2.3 KB |
| 1. Introduction to Machine Learning.mp4 | 109.2 MB |
| 1. Introduction to Machine Learning.srt | 19.4 KB |
| 2. Building a Machine Learning Model.mp4 | 39.5 MB |
| 2. Building a Machine Learning Model.srt | 10.2 KB |
| 1. Gathering Business Knowledge.mp4 | 14.5 MB |
| 1. Gathering Business Knowledge.srt | 3.8 KB |
| 10. Outlier Treatment in Python.mp4 | 70.3 MB |
| 10. Outlier Treatment in Python.srt | 14.5 KB |
| 11. Outlier Treatment in R.mp4 | 30.7 MB |
| 11. Outlier Treatment in R.srt | 4.9 KB |
| 12. Missing Value Imputation.mp4 | 23.1 MB |
| 12. Missing Value Imputation.srt | 4.2 KB |
| 13. Missing Value Imputation in Python.mp4 | 23.4 MB |
| 13. Missing Value Imputation in Python.srt | 4.7 KB |
| 14. Missing Value imputation in R.mp4 | 26.0 MB |
| 14. Missing Value imputation in R.srt | 4.1 KB |
| 15. Seasonality in Data.mp4 | 17.0 MB |
| 15. Seasonality in Data.srt | 4.1 KB |
| 16. Bi-variate analysis and Variable transformation.mp4 | 100.4 MB |
| 16. Bi-variate analysis and Variable transformation.srt | 20.2 KB |
| 17. Variable transformation and deletion in Python.mp4 | 44.1 MB |
| 17. Variable transformation and deletion in Python.srt | 9.3 KB |
| 18. Variable transformation in R.mp4 | 55.4 MB |
| 18. Variable transformation in R.srt | 10.2 KB |
| 19. Non-usable variables.mp4 | 20.2 MB |
| 19. Non-usable variables.srt | 6.3 KB |
| 2. Data Exploration.mp4 | 20.1 MB |
| 2. Data Exploration.srt | 3.8 KB |
| 20. Dummy variable creation Handling qualitative data.mp4 | 36.8 MB |
| 20. Dummy variable creation Handling qualitative data.srt | 5.5 KB |
| 21. Dummy variable creation in Python.mp4 | 26.5 MB |
| 21. Dummy variable creation in Python.srt | 6.4 KB |
| 22. Dummy variable creation in R.mp4 | 44.0 MB |
| 22. Dummy variable creation in R.srt | 6.3 KB |
| 23. Correlation Analysis.mp4 | 71.6 MB |
| 23. Correlation Analysis.srt | 11.8 KB |
| 24. Correlation Analysis in Python.mp4 | 55.3 MB |
| 24. Correlation Analysis in Python.srt | 7.2 KB |
| 25. Correlation Matrix in R.mp4 | 83.1 MB |
| 25. Correlation Matrix in R.srt | 10.0 KB |
| 26. Quiz.html | 170 bytes |
| 3. The Dataset and the Data Dictionary.mp4 | 69.3 MB |
| 3. The Dataset and the Data Dictionary.srt | 8.5 KB |
| 4. Importing Data in Python.mp4 | 27.8 MB |
| 4. Importing Data in Python.srt | 6.6 KB |
| 5. Importing the dataset into R.mp4 | 13.1 MB |
| 5. Importing the dataset into R.srt | 2.9 KB |
| 6. Univariate analysis and EDD.mp4 | 24.2 MB |
| 6. Univariate analysis and EDD.srt | 3.8 KB |
| 7. EDD in Python.mp4 | 61.8 MB |
| 7. EDD in Python.srt | 11.8 KB |
| 8. EDD in R.mp4 | 97.0 MB |
| 8. EDD in R.srt | 13.7 KB |
| 9. Outlier Treatment.mp4 | 27.3 MB |
| 9. Outlier Treatment.srt | 4.9 KB |
| 1. The Problem Statement.mp4 | 9.4 MB |
| 1. The Problem Statement.srt | 1.8 KB |
| 10. Multiple Linear Regression in Python.mp4 | 69.7 MB |
| 10. Multiple Linear Regression in Python.srt | 14.4 KB |
| 11. Multiple Linear Regression in R.mp4 | 62.4 MB |
| 11. Multiple Linear Regression in R.srt | 9.6 KB |
| 12. Test-train split.mp4 | 41.9 MB |
| 12. Test-train split.srt | 12.6 KB |
| 13. Bias Variance trade-off.mp4 | 25.1 MB |
| 13. Bias Variance trade-off.srt | 8.2 KB |
| 14. Test train split in Python.mp4 | 44.9 MB |
| 14. Test train split in Python.srt | 8.8 KB |
| 15. Test-Train Split in R.mp4 | 75.6 MB |
| 15. Test-Train Split in R.srt | 9.6 KB |
| 16. Regression models other than OLS.mp4 | 16.5 MB |
| 16. Regression models other than OLS.srt | 5.3 KB |
| 17. Subset selection techniques.mp4 | 79.1 MB |
| 17. Subset selection techniques.srt | 15.3 KB |
| 18. Subset selection in R.mp4 | 63.5 MB |
| 18. Subset selection in R.srt | 8.4 KB |
| 19. Shrinkage methods Ridge and Lasso.mp4 | 33.3 MB |
| 19. Shrinkage methods Ridge and Lasso.srt | 9.4 KB |
| 2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 | 43.4 MB |
| 2. Basic Equations and Ordinary Least Squares (OLS) method.srt | 12.7 KB |
| 20. Ridge regression and Lasso in Python.mp4 | 128.8 MB |
| 20. Ridge regression and Lasso in Python.srt | 21.5 KB |
| 21. Ridge regression and Lasso in R.mp4 | 103.4 MB |
| 21. Ridge regression and Lasso in R.srt | 13.0 KB |
| 22. Heteroscedasticity.mp4 | 14.5 MB |
| 22. Heteroscedasticity.srt | 3.2 KB |
| 3. Assessing accuracy of predicted coefficients.mp4 | 92.1 MB |
| 3. Assessing accuracy of predicted coefficients.srt | 19.9 KB |
| 4. Assessing Model Accuracy RSE and R squared.mp4 | 43.6 MB |
| 4. Assessing Model Accuracy RSE and R squared.srt | 9.8 KB |
| 5. Simple Linear Regression in Python.mp4 | 63.4 MB |
| 5. Simple Linear Regression in Python.srt | 13.4 KB |
| 6. Simple Linear Regression in R.mp4 | 40.8 MB |
| 6. Simple Linear Regression in R.srt | 9.6 KB |
| 7. Multiple Linear Regression.mp4 | 34.3 MB |
| 7. Multiple Linear Regression.srt | 7.4 KB |
| 8. The F - statistic.mp4 | 56.0 MB |
| 8. The F - statistic.srt | 11.5 KB |
| 9. Interpreting results of Categorical variables.mp4 | 22.5 MB |
| 9. Interpreting results of Categorical variables.srt | 6.9 KB |
| 1. Three classification models and Data set.mp4 | 52.3 MB |
| 1. Three classification models and Data set.srt | 6.9 KB |
| 1.1 Classification preprocessed data Python.csv | 41.0 KB |
| 1.2 Classification preprocessed data R.csv | 41.0 KB |
| 2. Importing the data into Python.mp4 | 6.9 MB |
| 2. Importing the data into Python.srt | 1.7 KB |
| 2.1 Classification preprocessed data Python.csv | 41.0 KB |
| 3. Importing the data into R.mp4 | 8.8 MB |
| 3. Importing the data into R.srt | 1.5 KB |
| 3.1 Classification preprocessed data R.csv | 41.0 KB |
| 4. The problem statements.mp4 | 17.1 MB |
| 4. The problem statements.srt | 1.9 KB |
| 5. Why can't we use Linear Regression.mp4 | 16.9 MB |
| 5. Why can't we use Linear Regression.srt | 5.7 KB |
| 1. Logistic Regression.mp4 | 32.9 MB |
| 1. Logistic Regression.srt | 8.9 KB |
| 10. Evaluating performance of model.mp4 | 35.2 MB |
| 10. Evaluating performance of model.srt | 9.7 KB |
| 11. Evaluating model performance in Python.mp4 | 9.0 MB |
| 11. Evaluating model performance in Python.srt | 2.6 KB |
| 12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 | 55.7 MB |
| 12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt | 7.7 KB |
| 2. Training a Simple Logistic Model in Python.mp4 | 47.9 MB |
| 2. Training a Simple Logistic Model in Python.srt | 10.8 KB |
| 3. Training a Simple Logistic model in R.mp4 | 25.6 MB |
| 3. Training a Simple Logistic model in R.srt | 4.3 KB |
| 4. Result of Simple Logistic Regression.mp4 | 26.9 MB |
| 4. Result of Simple Logistic Regression.srt | 6.1 KB |
| 5. Logistic with multiple predictors.mp4 | 8.5 MB |
| 5. Logistic with multiple predictors.srt | 3.1 KB |
| 6. Training multiple predictor Logistic model in Python.mp4 | 26.3 MB |
| 6. Training multiple predictor Logistic model in Python.srt | 6.2 KB |
| 7. Training multiple predictor Logistic model in R.mp4 | 15.8 MB |
| 7. Training multiple predictor Logistic model in R.srt | 2.1 KB |
| 8. Confusion Matrix.mp4 | 21.1 MB |
| 8. Confusion Matrix.srt | 5.2 KB |
| 9. Creating Confusion Matrix in Python.mp4 | 51.3 MB |
| 9. Creating Confusion Matrix in Python.srt | 11.1 KB |
| [CourseClub.Me].url | 122 bytes |
| [GigaCourse.Com].url | 49 bytes |
Comments
Similar torrents
| Torrent name | ||||||||
|---|---|---|---|---|---|---|---|---|
|
tutsgalaxy in
Other
::
Other
|
 |
2023-06-02 19:23:30 |
0 |
1.0 GB |
0 |
23 |
2 |
|
|
CourseClub in
Other
::
Other
|
|
2024-04-27 15:04:01 |
0 |
7.2 GB |
0 |
10 |
4 |
|
|
tutsgalaxy in
Other
::
Other
|
|
2023-06-02 13:54:01 |
0 |
7.4 GB |
0 |
7 |
2 |
|
|
freecoursewb in
Other
::
Other
|
|
2026-03-02 09:32:04 |
0 |
1.3 GB |
0 |
5 |
0 |
|
|
freecoursewb in
Other
::
Other
|
|
2026-03-02 09:06:03 |
0 |
2.2 GB |
0 |
4 |
0 |
|
|
tutsgalaxy in
Other
::
Other
|
|
2023-06-02 12:28:09 |
0 |
3.4 GB |
0 |
3 |
0 |
|
|
tutsgalaxy in
Other
::
Other
|
|
2023-06-02 11:31:29 |
0 |
5.1 GB |
0 |
3 |
0 |
|
|
2023-10-22 07:08:46 |
0 |
8.6 GB |
0 |
2 |
0 |
||
|
2023-08-05 17:43:43 |
0 |
10.5 GB |
0 |
2 |
2 |
||
|
2023-06-02 00:26:27 |
0 |
1.2 GB |
0 |
2 |
0 |
||