[FreeCourseSite.com] Udemy - Machine Learning & Deep Learning in Python & R

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[FreeCourseSite.com] Udemy - Machine Learning & Deep Learning in Python & R

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文件列表

  • 27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4 226.5 MB
  • 37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4 173.2 MB
  • 18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4 169.1 MB
  • 26. ANN in Python/9. Building Neural Network for Regression Problem.mp4 163.5 MB
  • 26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4 158.9 MB
  • 23. Creating Support Vector Machine Model in R/3. Classification SVM model using Linear Kernel.mp4 145.9 MB
  • 27. ANN in R/6. Building Regression Model with Functional API.mp4 137.5 MB
  • 27. ANN in R/3. Building,Compiling and Training.mp4 137.1 MB
  • 34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4 135.4 MB
  • 7. Linear Regression/20. Ridge regression and Lasso in Python.mp4 135.1 MB
  • 25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 128.1 MB
  • 38. Time Series - Important Concepts/5. Differencing in Python.mp4 118.5 MB
  • 37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4 118.2 MB
  • 27. ANN in R/2. Data Normalization and Test-Train Split.mp4 117.2 MB
  • 5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 114.5 MB
  • 37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4 114.1 MB
  • 23. Creating Support Vector Machine Model in R/7. SVM based Regression Model in R.mp4 111.3 MB
  • 7. Linear Regression/21. Ridge regression and Lasso in R.mp4 108.5 MB
  • 14. Simple Decision Trees/13. Building a Regression Tree in R.mp4 108.4 MB
  • 35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4 106.5 MB
  • 37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4 105.6 MB
  • 6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4 105.3 MB
  • 27. ANN in R/4. Evaluating and Predicting.mp4 104.1 MB
  • 6. Data Preprocessing/8. EDD in R.mp4 101.7 MB
  • 3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp4 101.4 MB
  • 7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 96.6 MB
  • 26. ANN in Python/10. Using Functional API for complex architectures.mp4 96.6 MB
  • 18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp4 93.0 MB
  • 32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp4 92.0 MB
  • 24. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp4 90.8 MB
  • 15. Simple Classification Tree/5. Building a classification Tree in R.mp4 89.2 MB
  • 27. ANN in R/5. ANN with NeuralNets Package.mp4 88.5 MB
  • 6. Data Preprocessing/25. Correlation Matrix in R.mp4 87.2 MB
  • 23. Creating Support Vector Machine Model in R/5. Polynomial Kernel with Hyperparameter Tuning.mp4 87.2 MB
  • 3. Setting up R Studio and R crash course/3. Packages in R.mp4 87.0 MB
  • 15. Simple Classification Tree/4. Classification tree in Python Training.mp4 86.7 MB
  • 14. Simple Decision Trees/18. Pruning a Tree in R.mp4 86.1 MB
  • 26. ANN in Python/7. Compiling and Training the Neural Network model.mp4 85.6 MB
  • 17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4 84.6 MB
  • 27. ANN in R/7. Complex Architectures using Functional API.mp4 83.4 MB
  • 26. ANN in Python/6. Building the Neural Network using Keras.mp4 83.0 MB
  • 7. Linear Regression/17. Subset selection techniques.mp4 82.9 MB
  • 8. Classification Models Data Preparation/1. The Data and the Data Dictionary.mp4 82.8 MB
  • 8. Classification Models Data Preparation/4. EDD in Python.mp4 81.4 MB
  • 16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4 81.1 MB
  • 7. Linear Regression/15. Test-Train Split in R.mp4 79.3 MB
  • 12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp4 79.1 MB
  • 18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp4 78.6 MB
  • 40. Time Series - ARIMA model/3. ARIMA model in Python.mp4 78.1 MB
  • 11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp4 78.0 MB
  • 12. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp4 77.8 MB
  • 14. Simple Decision Trees/17. Pruning a tree in Python.mp4 77.1 MB
  • 31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.mp4 75.3 MB
  • 30. Creating CNN model in R/3. Creating Model Architecture.mp4 75.1 MB
  • 6. Data Preprocessing/23. Correlation Analysis.mp4 75.1 MB
  • 6. Data Preprocessing/10. Outlier Treatment in Python.mp4 73.7 MB
  • 26. ANN in Python/8. Evaluating performance and Predicting using Keras.mp4 73.3 MB
  • 7. Linear Regression/10. Multiple Linear Regression in Python.mp4 73.1 MB
  • 6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 72.6 MB
  • 4. Basics of Statistics/3. Describing data Graphically.mp4 72.6 MB
  • 18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp4 72.5 MB
  • 30. Creating CNN model in R/5. Model Performance.mp4 71.4 MB
  • 28. CNN - Basics/5. Channels.mp4 71.1 MB
  • 22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.mp4 70.9 MB
  • 30. Creating CNN model in R/2. Data Preprocessing.mp4 70.3 MB
  • 8. Classification Models Data Preparation/5. EDD in R.mp4 69.7 MB
  • 41. Time Series - SARIMA model/2. SARIMA model in Python.mp4 69.5 MB
  • 31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp4 69.2 MB
  • 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 68.4 MB
  • 12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp4 68.0 MB
  • 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 67.6 MB
  • 22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.mp4 67.2 MB
  • 35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp4 67.2 MB
  • 37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp4 66.8 MB
  • 7. Linear Regression/18. Subset selection in R.mp4 66.6 MB
  • 7. Linear Regression/5. Simple Linear Regression in Python.mp4 66.5 MB
  • 36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp4 65.5 MB
  • 7. Linear Regression/11. Multiple Linear Regression in R.mp4 65.4 MB
  • 25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp4 65.2 MB
  • 6. Data Preprocessing/7. EDD in Python.mp4 64.8 MB
  • 26. ANN in Python/12. Hyperparameter Tuning.mp4 63.6 MB
  • 23. Creating Support Vector Machine Model in R/4. Hyperparameter Tuning for Linear Kernel.mp4 63.4 MB
  • 25. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 63.3 MB
  • 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 63.3 MB
  • 3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp4 63.0 MB
  • 38. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp4 62.7 MB
  • 37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp4 62.4 MB
  • 16. Ensemble technique 1 - Bagging/3. Bagging in R.mp4 61.8 MB
  • 29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp4 60.8 MB
  • 22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.mp4 60.5 MB
  • 39. Time Series - Implementation in Python/1. Test Train Split in Python.mp4 60.2 MB
  • 23. Creating Support Vector Machine Model in R/6. Radial Kernel with Hyperparameter Tuning.mp4 59.4 MB
  • 39. Time Series - Implementation in Python/7. Moving Average model in Python.mp4 59.4 MB
  • 32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp4 59.1 MB
  • 26. ANN in Python/3. Dataset for classification.mp4 58.9 MB
  • 20. Support Vector Classifier/1. Support Vector classifiers.mp4 58.9 MB
  • 7. Linear Regression/8. The F - statistic.mp4 58.7 MB
  • 10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 58.4 MB
  • 6. Data Preprocessing/18. Variable transformation in R.mp4 58.1 MB
  • 6. Data Preprocessing/24. Correlation Analysis in Python.mp4 58.0 MB
  • 29. Creating CNN model in Python/3. CNN model in Python - Training and results.mp4 57.8 MB
  • 23. Creating Support Vector Machine Model in R/1. Importing Data into R.mp4 56.3 MB
  • 39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp4 56.1 MB
  • 33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.mp4 55.6 MB
  • 28. CNN - Basics/4. Filters and Feature maps.mp4 55.3 MB
  • 10. Logistic Regression/9. Creating Confusion Matrix in Python.mp4 53.7 MB
  • 28. CNN - Basics/1. CNN Introduction.mp4 53.6 MB
  • 23. Creating Support Vector Machine Model in R/2. Test-Train Split.mp4 52.9 MB
  • 39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4 52.0 MB
  • 31. Project Creating CNN model from scratch in Python/1. Project - Introduction.mp4 51.8 MB
  • 10. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4 50.2 MB
  • 8. Classification Models Data Preparation/6. Outlier treatment in Python.mp4 49.6 MB
  • 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp4 49.2 MB
  • 28. CNN - Basics/6. PoolingLayer.mp4 49.2 MB
  • 17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp4 49.0 MB
  • 32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.mp4 48.4 MB
  • 15. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp4 47.6 MB
  • 22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.mp4 47.6 MB
  • 25. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp4 47.6 MB
  • 7. Linear Regression/14. Test train split in Python.mp4 47.1 MB
  • 24. Introduction - Deep Learning/2. Perceptron.mp4 46.9 MB
  • 30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.mp4 46.8 MB
  • 8. Classification Models Data Preparation/13. Dummy variable creation in R.mp4 46.5 MB
  • 26. ANN in Python/4. Normalization and Test-Train split.mp4 46.3 MB
  • 6. Data Preprocessing/17. Variable transformation and deletion in Python.mp4 46.3 MB
  • 6. Data Preprocessing/22. Dummy variable creation in R.mp4 46.1 MB
  • 14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp4 46.1 MB
  • 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp4 46.0 MB
  • 14. Simple Decision Trees/2. Understanding a Regression Tree.mp4 45.8 MB
  • 14. Simple Decision Trees/6. Importing the Data set into R.mp4 45.8 MB
  • 7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 45.7 MB
  • 7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 45.5 MB
  • 39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp4 45.5 MB
  • 29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp4 45.4 MB
  • 14. Simple Decision Trees/1. Basics of Decision Trees.mp4 44.7 MB
  • 12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp4 44.4 MB
  • 3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp4 44.1 MB
  • 7. Linear Regression/12. Test-train split.mp4 43.9 MB
  • 13. Comparing results from 3 models/1. Understanding the results of classification models.mp4 43.7 MB
  • 33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.mp4 43.4 MB
  • 40. Time Series - ARIMA model/1. ACF and PACF.mp4 43.2 MB
  • 11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4 42.9 MB
  • 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp4 42.9 MB
  • 7. Linear Regression/6. Simple Linear Regression in R.mp4 42.8 MB
  • 4. Basics of Statistics/4. Measures of Centers.mp4 42.8 MB
  • 3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp4 42.7 MB
  • 29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp4 42.6 MB
  • 25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 42.4 MB
  • 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp4 42.3 MB
  • 21. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4 42.1 MB
  • 18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp4 41.8 MB
  • 5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4 41.4 MB
  • 12. K-Nearest Neighbors classifier/1. Test-Train Split.mp4 41.2 MB
  • 41. Time Series - SARIMA model/1. SARIMA model.mp4 40.9 MB
  • 3. Setting up R Studio and R crash course/2. Basics of R and R studio.mp4 40.7 MB
  • 37. Time Series - Preprocessing in Python/9. Moving Average.mp4 40.6 MB
  • 22. Creating Support Vector Machine Model in Python/6. Standardizing the data.mp4 40.3 MB
  • 8. Classification Models Data Preparation/11. Variable transformation in R.mp4 39.9 MB
  • 14. Simple Decision Trees/4. The Data set for this part.mp4 39.1 MB
  • 12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp4 39.0 MB
  • 22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.mp4 39.0 MB
  • 22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.mp4 39.0 MB
  • 6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4 38.6 MB
  • 3. Setting up R Studio and R crash course/1. Installing R and R studio.mp4 37.5 MB
  • 10. Logistic Regression/10. Evaluating performance of model.mp4 36.9 MB
  • 24. Introduction - Deep Learning/3. Activation Functions.mp4 36.3 MB
  • 36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp4 36.2 MB
  • 7. Linear Regression/7. Multiple Linear Regression.mp4 36.0 MB
  • 7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp4 35.0 MB
  • 12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp4 34.7 MB
  • 10. Logistic Regression/1. Logistic Regression.mp4 34.5 MB
  • 38. Time Series - Important Concepts/4. Differencing.mp4 33.9 MB
  • 30. Creating CNN model in R/4. Compiling and training.mp4 33.8 MB
  • 40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4 33.7 MB
  • 28. CNN - Basics/3. Padding.mp4 33.2 MB
  • 6. Data Preprocessing/11. Outlier Treatment in R.mp4 32.2 MB
  • 17. Ensemble technique 2 - Random Forests/4. Random Forest in R.mp4 32.2 MB
  • 18. Ensemble technique 3 - Boosting/1. Boosting.mp4 32.1 MB
  • 18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.mp4 32.0 MB
  • 34. Transfer Learning Basics/5. Transfer Learning.mp4 31.5 MB
  • 19. Maximum Margin Classifier/2. The Concept of a Hyperplane.mp4 30.8 MB
  • 1. Introduction/1. Introduction.mp4 30.8 MB
  • 8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.mp4 30.7 MB
  • 24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp4 30.5 MB
  • 15. Simple Classification Tree/1. Classification tree.mp4 29.6 MB
  • 16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp4 29.5 MB
  • 6. Data Preprocessing/4. Importing Data in Python.mp4 29.2 MB
  • 10. Logistic Regression/4. Result of Simple Logistic Regression.mp4 28.2 MB
  • 6. Data Preprocessing/21. Dummy variable creation in Python.mp4 27.8 MB
  • 8. Classification Models Data Preparation/12. Dummy variable creation in Python.mp4 27.7 MB
  • 10. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp4 27.5 MB
  • 6. Data Preprocessing/14. Missing Value imputation in R.mp4 27.3 MB
  • 36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp4 27.2 MB
  • 14. Simple Decision Trees/5. Importing the Data set into Python.mp4 27.1 MB
  • 22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.mp4 27.1 MB
  • 10. Logistic Regression/3. Training a Simple Logistic model in R.mp4 26.8 MB
  • 3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp4 26.8 MB
  • 8. Classification Models Data Preparation/7. Outlier Treatment in R.mp4 26.6 MB
  • 7. Linear Regression/13. Bias Variance trade-off.mp4 26.3 MB
  • 6. Data Preprocessing/12. Missing Value Imputation.mp4 26.2 MB
  • 14. Simple Decision Trees/8. Dummy Variable creation in Python.mp4 26.2 MB
  • 14. Simple Decision Trees/10. Test-Train split in Python.mp4 26.1 MB
  • 22. Creating Support Vector Machine Model in Python/5. Test-Train Split.mp4 26.1 MB
  • 4. Basics of Statistics/5. Measures of Dispersion.mp4 25.9 MB
  • 32. Project Creating CNN model from scratch/3. Project in R - Training.mp4 25.8 MB
  • 6. Data Preprocessing/9. Outlier Treatment.mp4 25.7 MB
  • 6. Data Preprocessing/6. Univariate analysis and EDD.mp4 25.4 MB
  • 39. Time Series - Implementation in Python/6. Moving Average model -Basics.mp4 25.3 MB
  • 32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.mp4 24.8 MB
  • 6. Data Preprocessing/13. Missing Value Imputation in Python.mp4 24.6 MB
  • 32. Project Creating CNN model from scratch/4. Project in R - Model Performance.mp4 24.3 MB
  • 22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.mp4 24.0 MB
  • 27. ANN in R/1. Installing Keras and Tensorflow.mp4 23.9 MB
  • 4. Basics of Statistics/1. Types of Data.mp4 23.8 MB
  • 8. Classification Models Data Preparation/8. Missing Value Imputation in Python.mp4 23.7 MB
  • 7. Linear Regression/9. Interpreting results of Categorical variables.mp4 23.6 MB
  • 19. Maximum Margin Classifier/3. Maximum Margin Classifier.mp4 23.6 MB
  • 6. Data Preprocessing/1. Gathering Business Knowledge.mp4 23.4 MB
  • 13. Comparing results from 3 models/2. Summary of the three models.mp4 23.3 MB
  • 8. Classification Models Data Preparation/2. Data Import in Python.mp4 23.1 MB
  • 14. Simple Decision Trees/15. Plotting decision tree in Python.mp4 22.5 MB
  • 34. Transfer Learning Basics/4. GoogLeNet.mp4 22.4 MB
  • 40. Time Series - ARIMA model/2. ARIMA model - Basics.mp4 22.4 MB
  • 38. Time Series - Important Concepts/2. Random Walk.mp4 22.2 MB
  • 10. Logistic Regression/8. Confusion Matrix.mp4 22.1 MB
  • 31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.mp4 22.1 MB
  • 34. Transfer Learning Basics/1. ILSVRC.mp4 21.9 MB
  • 6. Data Preprocessing/2. Data Exploration.mp4 21.5 MB
  • 9. The Three classification models/1. Three Classifiers and the problem statement.mp4 21.3 MB
  • 6. Data Preprocessing/19. Non-usable variables.mp4 21.2 MB
  • 26. ANN in Python/2. Installing Tensorflow and Keras.mp4 21.0 MB
  • 8. Classification Models Data Preparation/9. Missing Value imputation in R.mp4 20.0 MB
  • 15. Simple Classification Tree/2. The Data set for Classification problem.mp4 19.5 MB
  • 22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.mp4 19.4 MB
  • 14. Simple Decision Trees/16. Pruning a tree.mp4 19.4 MB
  • 17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp4 19.1 MB
  • 14. Simple Decision Trees/7. Missing value treatment in Python.mp4 18.8 MB
  • 14. Simple Decision Trees/12. Creating Decision tree in Python.mp4 18.7 MB
  • 6. Data Preprocessing/15. Seasonality in Data.mp4 17.9 MB
  • 37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.mp4 17.8 MB
  • 9. The Three classification models/2. Why can't we use Linear Regression.mp4 17.8 MB
  • 39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp4 17.7 MB
  • 28. CNN - Basics/2. Stride.mp4 17.4 MB
  • 7. Linear Regression/16. Regression models other than OLS.mp4 17.4 MB
  • 14. Simple Decision Trees/14. Evaluating model performance in Python.mp4 17.2 MB
  • 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 17.1 MB
  • 10. Logistic Regression/7. Training multiple predictor Logistic model in R.mp4 16.6 MB
  • 22. Creating Support Vector Machine Model in Python/4. X-y Split.mp4 15.9 MB
  • 14. Simple Decision Trees/9. Dependent- Independent Data split in Python.mp4 15.9 MB
  • 26. ANN in Python/1. Keras and Tensorflow.mp4 15.6 MB
  • 37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp4 15.6 MB
  • 7. Linear Regression/22. Heteroscedasticity.mp4 15.2 MB
  • 14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.mp4 14.6 MB
  • 8. Classification Models Data Preparation/3. Importing the dataset into R.mp4 14.1 MB
  • 6. Data Preprocessing/5. Importing the dataset into R.mp4 13.8 MB
  • 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp4 13.4 MB
  • 36. Time Series Analysis and Forecasting/1. Introduction.mp4 12.9 MB
  • 11. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4 12.0 MB
  • 38. Time Series - Important Concepts/1. White Noise.mp4 11.9 MB
  • 4. Basics of Statistics/2. Types of Statistics.mp4 11.9 MB
  • 26. ANN in Python/5. Different ways to create ANN using Keras.mp4 11.3 MB
  • 20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp4 11.3 MB
  • 19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.mp4 11.1 MB
  • 34. Transfer Learning Basics/3. VGG16NET.mp4 10.9 MB
  • 36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp4 10.6 MB
  • 22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.mp4 10.2 MB
  • 7. Linear Regression/1. The Problem Statement.mp4 9.8 MB
  • 10. Logistic Regression/11. Evaluating model performance in Python.mp4 9.4 MB
  • 19. Maximum Margin Classifier/1. Content flow.mp4 9.1 MB
  • 10. Logistic Regression/5. Logistic with multiple predictors.mp4 9.0 MB
  • 37. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp4 8.8 MB
  • 30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp4 7.7 MB
  • 34. Transfer Learning Basics/2. LeNET.mp4 7.3 MB
  • 15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.mp4 7.2 MB
  • 41. Time Series - SARIMA model/3. Stationary time Series.mp4 5.9 MB
  • 22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp4 4.2 MB
  • 37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.srt 29.6 kB
  • 25. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 25.4 kB
  • 26. ANN in Python/9. Building Neural Network for Regression Problem.srt 24.3 kB
  • 27. ANN in R/8. Saving - Restoring Models and Using Callbacks.srt 21.9 kB
  • 7. Linear Regression/20. Ridge regression and Lasso in Python.srt 21.4 kB
  • 26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.srt 21.3 kB
  • 34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.srt 20.9 kB
  • 2. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.srt 20.6 kB
  • 5. Introduction to Machine Learning/1. Introduction to Machine Learning.srt 20.2 kB
  • 6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.srt 19.8 kB
  • 37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.srt 19.7 kB
  • 18. Ensemble technique 3 - Boosting/7. XGBoosting in R.srt 18.9 kB
  • 2. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.srt 18.4 kB
  • 8. Classification Models Data Preparation/4. EDD in Python.srt 18.2 kB
  • 23. Creating Support Vector Machine Model in R/3. Classification SVM model using Linear Kernel.srt 18.2 kB
  • 37. Time Series - Preprocessing in Python/1. Data Loading in Python.srt 18.1 kB
  • 37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.srt 18.0 kB
  • 7. Linear Regression/3. Assessing accuracy of predicted coefficients.srt 17.8 kB
  • 27. ANN in R/3. Building,Compiling and Training.srt 16.7 kB
  • 38. Time Series - Important Concepts/5. Differencing in Python.srt 16.1 kB
  • 24. Introduction - Deep Learning/4. Python - Creating Perceptron model.srt 16.1 kB
  • 14. Simple Decision Trees/13. Building a Regression Tree in R.srt 15.9 kB
  • 3. Setting up R Studio and R crash course/7. Creating Barplots in R.srt 15.4 kB
  • 15. Simple Classification Tree/4. Classification tree in Python Training.srt 14.9 kB
  • 40. Time Series - ARIMA model/3. ARIMA model in Python.srt 14.6 kB
  • 7. Linear Regression/10. Multiple Linear Regression in Python.srt 14.6 kB
  • 35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).srt 14.5 kB
  • 6. Data Preprocessing/10. Outlier Treatment in Python.srt 14.5 kB
  • 4. Basics of Statistics/3. Describing data Graphically.srt 14.2 kB
  • 17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.srt 14.0 kB
  • 7. Linear Regression/17. Subset selection techniques.srt 14.0 kB
  • 25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.srt 14.0 kB
  • 27. ANN in R/6. Building Regression Model with Functional API.srt 13.9 kB
  • 2. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.srt 13.5 kB
  • 6. Data Preprocessing/8. EDD in R.srt 13.5 kB
  • 7. Linear Regression/5. Simple Linear Regression in Python.srt 13.4 kB
  • 26. ANN in Python/10. Using Functional API for complex architectures.srt 13.3 kB
  • 26. ANN in Python/6. Building the Neural Network using Keras.srt 13.2 kB
  • 27. ANN in R/2. Data Normalization and Test-Train Split.srt 13.2 kB
  • 25. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 13.0 kB
  • 22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.srt 12.7 kB
  • 7. Linear Regression/21. Ridge regression and Lasso in R.srt 12.7 kB
  • 16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.srt 12.6 kB
  • 3. Setting up R Studio and R crash course/3. Packages in R.srt 12.5 kB
  • 23. Creating Support Vector Machine Model in R/7. SVM based Regression Model in R.srt 12.3 kB
  • 39. Time Series - Implementation in Python/1. Test Train Split in Python.srt 12.3 kB
  • 3. Setting up R Studio and R crash course/2. Basics of R and R studio.srt 12.3 kB
  • 14. Simple Decision Trees/2. Understanding a Regression Tree.srt 12.2 kB
  • 6. Data Preprocessing/23. Correlation Analysis.srt 12.2 kB
  • 11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.srt 12.2 kB
  • 32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.srt 12.2 kB
  • 2. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.srt 12.1 kB
  • 37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.srt 12.0 kB
  • 6. Data Preprocessing/7. EDD in Python.srt 11.9 kB
  • 41. Time Series - SARIMA model/2. SARIMA model in Python.srt 11.9 kB
  • 23. Creating Support Vector Machine Model in R/5. Polynomial Kernel with Hyperparameter Tuning.srt 11.8 kB
  • 18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.srt 11.7 kB
  • 8. Classification Models Data Preparation/5. EDD in R.srt 11.6 kB
  • 14. Simple Decision Trees/1. Basics of Decision Trees.srt 11.5 kB
  • 7. Linear Regression/12. Test-train split.srt 11.1 kB
  • 20. Support Vector Classifier/1. Support Vector classifiers.srt 11.1 kB
  • 10. Logistic Regression/9. Creating Confusion Matrix in Python.srt 11.1 kB
  • 25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 11.1 kB
  • 22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.srt 11.0 kB
  • 14. Simple Decision Trees/17. Pruning a tree in Python.srt 11.0 kB
  • 10. Logistic Regression/2. Training a Simple Logistic Model in Python.srt 10.9 kB
  • 12. K-Nearest Neighbors classifier/1. Test-Train Split.srt 10.8 kB
  • 18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.srt 10.8 kB
  • 22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.srt 10.7 kB
  • 7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 10.7 kB
  • 38. Time Series - Important Concepts/3. Decomposing Time Series in Python.srt 10.7 kB
  • 37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.srt 10.5 kB
  • 5. Introduction to Machine Learning/2. Building a Machine Learning Model.srt 10.5 kB
  • 11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.srt 10.5 kB
  • 24. Introduction - Deep Learning/2. Perceptron.srt 10.5 kB
  • 39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.srt 10.4 kB
  • 15. Simple Classification Tree/5. Building a classification Tree in R.srt 10.4 kB
  • 2. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.srt 10.4 kB
  • 27. ANN in R/4. Evaluating and Predicting.srt 10.4 kB
  • 26. ANN in Python/7. Compiling and Training the Neural Network model.srt 10.3 kB
  • 12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.srt 10.2 kB
  • 6. Data Preprocessing/18. Variable transformation in R.srt 10.2 kB
  • 2. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.srt 10.1 kB
  • 26. ANN in Python/12. Hyperparameter Tuning.srt 10.0 kB
  • 12. K-Nearest Neighbors classifier/3. Test-Train Split in R.srt 10.0 kB
  • 26. ANN in Python/8. Evaluating performance and Predicting using Keras.srt 10.0 kB
  • 7. Linear Regression/8. The F - statistic.srt 9.9 kB
  • 14. Simple Decision Trees/18. Pruning a Tree in R.srt 9.9 kB
  • 36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.srt 9.9 kB
  • 39. Time Series - Implementation in Python/7. Moving Average model in Python.srt 9.8 kB
  • 6. Data Preprocessing/25. Correlation Matrix in R.srt 9.8 kB
  • 8. Classification Models Data Preparation/6. Outlier treatment in Python.srt 9.8 kB
  • 10. Logistic Regression/10. Evaluating performance of model.srt 9.6 kB
  • 7. Linear Regression/15. Test-Train Split in R.srt 9.6 kB
  • 8. Classification Models Data Preparation/1. The Data and the Data Dictionary.srt 9.5 kB
  • 25. Neural Networks - Stacking cells to create network/5. Hyperparameter.srt 9.5 kB
  • 7. Linear Regression/6. Simple Linear Regression in R.srt 9.5 kB
  • 7. Linear Regression/11. Multiple Linear Regression in R.srt 9.4 kB
  • 31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.srt 9.4 kB
  • 31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.srt 9.4 kB
  • 6. Data Preprocessing/17. Variable transformation and deletion in Python.srt 9.2 kB
  • 7. Linear Regression/19. Shrinkage methods Ridge and Lasso.srt 9.2 kB
  • 12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.srt 9.2 kB
  • 15. Simple Classification Tree/3. Classification tree in Python Preprocessing.srt 9.1 kB
  • 22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.srt 9.1 kB
  • 23. Creating Support Vector Machine Model in R/1. Importing Data into R.srt 9.1 kB
  • 27. ANN in R/7. Complex Architectures using Functional API.srt 9.1 kB
  • 35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).srt 9.0 kB
  • 39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.srt 9.0 kB
  • 6. Data Preprocessing/3. The Dataset and the Data Dictionary.srt 9.0 kB
  • 7. Linear Regression/14. Test train split in Python.srt 8.9 kB
  • 40. Time Series - ARIMA model/1. ACF and PACF.srt 8.9 kB
  • 10. Logistic Regression/1. Logistic Regression.srt 8.8 kB
  • 18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.srt 8.8 kB
  • 27. ANN in R/5. ANN with NeuralNets Package.srt 8.6 kB
  • 7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 8.6 kB
  • 2. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.srt 8.4 kB
  • 7. Linear Regression/18. Subset selection in R.srt 8.4 kB
  • 4. Basics of Statistics/4. Measures of Centers.srt 8.4 kB
  • 39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.srt 8.4 kB
  • 24. Introduction - Deep Learning/3. Activation Functions.srt 8.4 kB
  • 28. CNN - Basics/1. CNN Introduction.srt 8.3 kB
  • 26. ANN in Python/3. Dataset for classification.srt 8.1 kB
  • 41. Time Series - SARIMA model/1. SARIMA model.srt 8.1 kB
  • 32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.srt 8.0 kB
  • 18. Ensemble technique 3 - Boosting/1. Boosting.srt 8.0 kB
  • 37. Time Series - Preprocessing in Python/9. Moving Average.srt 8.0 kB
  • 28. CNN - Basics/4. Filters and Feature maps.srt 7.8 kB
  • 13. Comparing results from 3 models/1. Understanding the results of classification models.srt 7.7 kB
  • 31. Project Creating CNN model from scratch in Python/1. Project - Introduction.srt 7.7 kB
  • 30. Creating CNN model in R/2. Data Preprocessing.srt 7.6 kB
  • 10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt 7.6 kB
  • 12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.srt 7.6 kB
  • 16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.srt 7.4 kB
  • 29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.srt 7.4 kB
  • 22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.srt 7.4 kB
  • 33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.srt 7.4 kB
  • 14. Simple Decision Trees/6. Importing the Data set into R.srt 7.4 kB
  • 23. Creating Support Vector Machine Model in R/6. Radial Kernel with Hyperparameter Tuning.srt 7.4 kB
  • 16. Ensemble technique 1 - Bagging/3. Bagging in R.srt 7.3 kB
  • 3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.srt 7.2 kB
  • 6. Data Preprocessing/24. Correlation Analysis in Python.srt 7.1 kB
  • 7. Linear Regression/13. Bias Variance trade-off.srt 7.1 kB
  • 23. Creating Support Vector Machine Model in R/4. Hyperparameter Tuning for Linear Kernel.srt 7.1 kB
  • 12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.srt 7.1 kB
  • 33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.srt 7.0 kB
  • 3. Setting up R Studio and R crash course/1. Installing R and R studio.srt 7.0 kB
  • 8. Classification Models Data Preparation/11. Variable transformation in R.srt 6.9 kB
  • 15. Simple Classification Tree/1. Classification tree.srt 6.9 kB
  • 21. Support Vector Machines/1. Kernel Based Support Vector Machines.srt 6.9 kB
  • 17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.srt 6.9 kB
  • 38. Time Series - Important Concepts/4. Differencing.srt 6.9 kB
  • 30. Creating CNN model in R/5. Model Performance.srt 6.7 kB
  • 22. Creating Support Vector Machine Model in Python/6. Standardizing the data.srt 6.7 kB
  • 8. Classification Models Data Preparation/13. Dummy variable creation in R.srt 6.6 kB
  • 6. Data Preprocessing/4. Importing Data in Python.srt 6.6 kB
  • 36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).srt 6.6 kB
  • 29. Creating CNN model in Python/3. CNN model in Python - Training and results.srt 6.6 kB
  • 7. Linear Regression/7. Multiple Linear Regression.srt 6.5 kB
  • 30. Creating CNN model in R/3. Creating Model Architecture.srt 6.4 kB
  • 28. CNN - Basics/5. Channels.srt 6.4 kB
  • 6. Data Preprocessing/21. Dummy variable creation in Python.srt 6.4 kB
  • 40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.srt 6.4 kB
  • 14. Simple Decision Trees/10. Test-Train split in Python.srt 6.3 kB
  • 22. Creating Support Vector Machine Model in Python/5. Test-Train Split.srt 6.3 kB
  • 8. Classification Models Data Preparation/12. Dummy variable creation in Python.srt 6.3 kB
  • 3. Setting up R Studio and R crash course/8. Creating Histograms in R.srt 6.3 kB
  • 26. ANN in Python/4. Normalization and Test-Train split.srt 6.3 kB
  • 6. Data Preprocessing/22. Dummy variable creation in R.srt 6.2 kB
  • 4. Basics of Statistics/5. Measures of Dispersion.srt 6.2 kB
  • 23. Creating Support Vector Machine Model in R/2. Test-Train Split.srt 6.2 kB
  • 6. Data Preprocessing/19. Non-usable variables.srt 6.2 kB
  • 10. Logistic Regression/6. Training multiple predictor Logistic model in Python.srt 6.2 kB
  • 13. Comparing results from 3 models/2. Summary of the three models.srt 6.1 kB
  • 7. Linear Regression/9. Interpreting results of Categorical variables.srt 6.1 kB
  • 10. Logistic Regression/4. Result of Simple Logistic Regression.srt 6.0 kB
  • 14. Simple Decision Trees/5. Importing the Data set into Python.srt 6.0 kB
  • 22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.srt 6.0 kB
  • 28. CNN - Basics/6. PoolingLayer.srt 6.0 kB
  • 14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.srt 6.0 kB
  • 12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.srt 6.0 kB
  • 6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.srt 5.9 kB
  • 29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.srt 5.9 kB
  • 29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.srt 5.7 kB
  • 32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.srt 5.7 kB
  • 9. The Three classification models/2. Why can't we use Linear Regression.srt 5.6 kB
  • 34. Transfer Learning Basics/5. Transfer Learning.srt 5.6 kB
  • 18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.srt 5.6 kB
  • 14. Simple Decision Trees/8. Dummy Variable creation in Python.srt 5.5 kB
  • 19. Maximum Margin Classifier/2. The Concept of a Hyperplane.srt 5.4 kB
  • 14. Simple Decision Trees/15. Plotting decision tree in Python.srt 5.4 kB
  • 8. Classification Models Data Preparation/2. Data Import in Python.srt 5.4 kB
  • 4. Basics of Statistics/1. Types of Data.srt 5.4 kB
  • 40. Time Series - ARIMA model/2. ARIMA model - Basics.srt 5.2 kB
  • 6. Data Preprocessing/9. Outlier Treatment.srt 5.2 kB
  • 39. Time Series - Implementation in Python/6. Moving Average model -Basics.srt 5.1 kB
  • 28. CNN - Basics/3. Padding.srt 5.1 kB
  • 10. Logistic Regression/8. Confusion Matrix.srt 5.0 kB
  • 6. Data Preprocessing/11. Outlier Treatment in R.srt 5.0 kB
  • 8. Classification Models Data Preparation/8. Missing Value Imputation in Python.srt 4.9 kB
  • 8. Classification Models Data Preparation/7. Outlier Treatment in R.srt 4.9 kB
  • 6. Data Preprocessing/13. Missing Value Imputation in Python.srt 4.9 kB
  • 24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.srt 4.9 kB
  • 17. Ensemble technique 2 - Random Forests/4. Random Forest in R.srt 4.9 kB
  • 7. Linear Regression/16. Regression models other than OLS.srt 4.9 kB
  • 14. Simple Decision Trees/14. Evaluating model performance in Python.srt 4.8 kB
  • 3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.srt 4.8 kB
  • 34. Transfer Learning Basics/1. ILSVRC.srt 4.7 kB
  • 17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.srt 4.7 kB
  • 38. Time Series - Important Concepts/2. Random Walk.srt 4.7 kB
  • 14. Simple Decision Trees/16. Pruning a tree.srt 4.6 kB
  • 1. Introduction/1. Introduction.srt 4.6 kB
  • 22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.srt 4.6 kB
  • 2. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.srt 4.5 kB
  • 18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.srt 4.5 kB
  • 8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.srt 4.4 kB
  • 14. Simple Decision Trees/12. Creating Decision tree in Python.srt 4.4 kB
  • 37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.srt 4.4 kB
  • 14. Simple Decision Trees/9. Dependent- Independent Data split in Python.srt 4.3 kB
  • 22. Creating Support Vector Machine Model in Python/4. X-y Split.srt 4.3 kB
  • 6. Data Preprocessing/12. Missing Value Imputation.srt 4.3 kB
  • 10. Logistic Regression/3. Training a Simple Logistic model in R.srt 4.3 kB
  • 30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.srt 4.3 kB
  • 6. Data Preprocessing/1. Gathering Business Knowledge.srt 4.2 kB
  • 26. ANN in Python/2. Installing Tensorflow and Keras.srt 4.2 kB
  • 8. Classification Models Data Preparation/9. Missing Value imputation in R.srt 4.2 kB
  • 6. Data Preprocessing/14. Missing Value imputation in R.srt 4.2 kB
  • 6. Data Preprocessing/6. Univariate analysis and EDD.srt 4.1 kB
  • 6. Data Preprocessing/15. Seasonality in Data.srt 4.1 kB
  • 9. The Three classification models/1. Three Classifiers and the problem statement.srt 4.0 kB
  • 6. Data Preprocessing/2. Data Exploration.srt 4.0 kB
  • 26. ANN in Python/1. Keras and Tensorflow.srt 3.9 kB
  • 14. Simple Decision Trees/7. Missing value treatment in Python.srt 3.8 kB
  • 39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.srt 3.7 kB
  • 14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.srt 3.6 kB
  • 19. Maximum Margin Classifier/3. Maximum Margin Classifier.srt 3.5 kB
  • 3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.srt 3.4 kB
  • 4. Basics of Statistics/2. Types of Statistics.srt 3.4 kB
  • 14. Simple Decision Trees/4. The Data set for this part.srt 3.4 kB
  • 22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.srt 3.4 kB
  • 34. Transfer Learning Basics/4. GoogLeNet.srt 3.3 kB
  • 32. Project Creating CNN model from scratch/3. Project in R - Training.srt 3.2 kB
  • 30. Creating CNN model in R/4. Compiling and training.srt 3.2 kB
  • 28. CNN - Basics/2. Stride.srt 3.1 kB
  • 27. ANN in R/1. Installing Keras and Tensorflow.srt 3.1 kB
  • 10. Logistic Regression/5. Logistic with multiple predictors.srt 3.0 kB
  • 36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.srt 3.0 kB
  • 31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.srt 3.0 kB
  • 7. Linear Regression/22. Heteroscedasticity.srt 2.9 kB
  • 8. Classification Models Data Preparation/3. Importing the dataset into R.srt 2.9 kB
  • 6. Data Preprocessing/5. Importing the dataset into R.srt 2.9 kB
  • 37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.srt 2.7 kB
  • 10. Logistic Regression/11. Evaluating model performance in Python.srt 2.7 kB
  • 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt 2.7 kB
  • 19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.srt 2.7 kB
  • 32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.srt 2.6 kB
  • 11. Linear Discriminant Analysis (LDA)/2. LDA in Python.srt 2.6 kB
  • 38. Time Series - Important Concepts/1. White Noise.srt 2.6 kB
  • 32. Project Creating CNN model from scratch/4. Project in R - Model Performance.srt 2.6 kB
  • 36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.srt 2.6 kB
  • 30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.srt 2.4 kB
  • 36. Time Series Analysis and Forecasting/1. Introduction.srt 2.2 kB
  • 37. Time Series - Preprocessing in Python/10. Exponential Smoothing.srt 2.1 kB
  • 10. Logistic Regression/7. Training multiple predictor Logistic model in R.srt 2.1 kB
  • 26. ANN in Python/5. Different ways to create ANN using Keras.srt 2.0 kB
  • 34. Transfer Learning Basics/3. VGG16NET.srt 2.0 kB
  • 15. Simple Classification Tree/2. The Data set for Classification problem.srt 2.0 kB
  • 22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.srt 2.0 kB
  • 22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.srt 1.9 kB
  • 34. Transfer Learning Basics/2. LeNET.srt 1.9 kB
  • 19. Maximum Margin Classifier/1. Content flow.srt 1.8 kB
  • 41. Time Series - SARIMA model/3. Stationary time Series.srt 1.7 kB
  • 15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.srt 1.7 kB
  • 7. Linear Regression/1. The Problem Statement.srt 1.7 kB
  • 20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.srt 1.7 kB
  • 42. Bonus Section/1. Congratulations & About your certificate.html 1.6 kB
  • 22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.srt 810 Bytes
  • 1. Introduction/2. Course Resources.html 370 Bytes
  • 31. Project Creating CNN model from scratch in Python/2. Data for the project.html 232 Bytes
  • 0. Websites you may like/[FCS Forum].url 133 Bytes
  • 6. Data Preprocessing/26. Quiz.html 130 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.ME].url 122 Bytes

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