文件列表539
- 26. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4 216.04 MB
- 36. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4 165.19 MB
- 17. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4 161.3 MB
- 25. ANN in Python/9. Building Neural Network for Regression Problem.mp4 155.91 MB
- 25. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4 151.58 MB
- 22. Creating Support Vector Machine Model in R/3. Classification SVM model using Linear Kernel.mp4 139.16 MB
- 26. ANN in R/6. Building Regression Model with Functional API.mp4 131.13 MB
- 26. ANN in R/3. Building,Compiling and Training.mp4 130.73 MB
- 33. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4 129.09 MB
- 7. Linear Regression/20. Ridge regression and Lasso in Python.mp4 128.84 MB
- 24. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 122.2 MB
- 37. Time Series - Important Concepts/5. Differencing in Python.mp4 113 MB
- 36. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4 112.69 MB
- 26. ANN in R/2. Data Normalization and Test-Train Split.mp4 111.78 MB
- 5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 109.17 MB
- 36. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4 108.86 MB
- 22. Creating Support Vector Machine Model in R/7. SVM based Regression Model in R.mp4 106.13 MB
- 7. Linear Regression/21. Ridge regression and Lasso in R.mp4 103.43 MB
- 13. Simple Decision Trees/13. Building a Regression Tree in R.mp4 103.33 MB
- 34. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4 101.58 MB
- 36. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4 100.67 MB
- 6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4 100.39 MB
- 26. ANN in R/4. Evaluating and Predicting.mp4 99.28 MB
- 6. Data Preprocessing/8. EDD in R.mp4 96.98 MB
- 3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp4 96.73 MB
- 7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 92.11 MB
- 25. ANN in Python/10. Using Functional API for complex architectures.mp4 92.1 MB
- 17. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp4 88.68 MB
- 31. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp4 87.76 MB
- 23. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp4 86.56 MB
- 14. Simple Classification Tree/5. Building a classification Tree in R.mp4 85.11 MB
- 26. ANN in R/5. ANN with NeuralNets Package.mp4 84.42 MB
- 6. Data Preprocessing/25. Correlation Matrix in R.mp4 83.14 MB
- 22. Creating Support Vector Machine Model in R/5. Polynomial Kernel with Hyperparameter Tuning.mp4 83.14 MB
- 3. Setting up R Studio and R crash course/3. Packages in R.mp4 82.94 MB
- 14. Simple Classification Tree/4. Classification tree in Python Training.mp4 82.71 MB
- 13. Simple Decision Trees/18. Pruning a Tree in R.mp4 82.09 MB
- 25. ANN in Python/7. Compiling and Training the Neural Network model.mp4 81.63 MB
- 16. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4 80.66 MB
- 26. ANN in R/7. Complex Architectures using Functional API.mp4 79.57 MB
- 25. ANN in Python/6. Building the Neural Network using Keras.mp4 79.11 MB
- 7. Linear Regression/17. Subset selection techniques.mp4 79.06 MB
- 15. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4 77.31 MB
- 7. Linear Regression/15. Test-Train Split in R.mp4 75.6 MB
- 11. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp4 75.42 MB
- 17. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp4 75 MB
- 39. Time Series - ARIMA model/3. ARIMA model in Python.mp4 74.43 MB
- 10. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp4 74.35 MB
- 11. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp4 74.23 MB
- 13. Simple Decision Trees/17. Pruning a tree in Python.mp4 73.5 MB
