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[GigaCourse.Com] Udemy - Machine Learning & Deep Learning in Python & R
magnet:?xt=urn:btih:4d33b004bdddefc1de86cb8519c18e9d8815374e&dn=[GigaCourse.Com] Udemy - Machine Learning & Deep Learning in Python & R
磁力链接详情
Hash值:
4d33b004bdddefc1de86cb8519c18e9d8815374e
点击数:
129
文件大小:
13.15 GB
文件数量:
278
创建日期:
2021-12-26 04:08
最后访问:
2024-7-21 03:18
访问标签:
GigaCourse
Com
Udemy
-
Machine
Learning
&
Deep
Learning
in
Python
&
R
文件列表详情
1. Introduction/1. Introduction.mp4 29.4 MB
10. Logistic Regression/1. Logistic Regression.mp4 32.93 MB
10. Logistic Regression/10. Evaluating performance of model.mp4 35.17 MB
10. Logistic Regression/11. Evaluating model performance in Python.mp4 9.02 MB
10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 55.7 MB
10. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4 47.87 MB
10. Logistic Regression/3. Training a Simple Logistic model in R.mp4 25.57 MB
10. Logistic Regression/4. Result of Simple Logistic Regression.mp4 26.94 MB
10. Logistic Regression/5. Logistic with multiple predictors.mp4 8.53 MB
10. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp4 26.25 MB
10. Logistic Regression/7. Training multiple predictor Logistic model in R.mp4 15.78 MB
10. Logistic Regression/8. Confusion Matrix.mp4 21.1 MB
10. Logistic Regression/9. Creating Confusion Matrix in Python.mp4 51.25 MB
11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4 40.96 MB
11. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4 11.4 MB
11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp4 74.36 MB
12. K-Nearest Neighbors classifier/1. Test-Train Split.mp4 39.3 MB
12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp4 33.1 MB
12. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp4 74.23 MB
12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp4 75.42 MB
12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp4 37.23 MB
12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp4 42.36 MB
12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp4 64.85 MB
13. Comparing results from 3 models/1. Understanding the results of classification models.mp4 41.64 MB
13. Comparing results from 3 models/2. Summary of the three models.mp4 22.22 MB
14. Simple Decision Trees/1. Basics of Decision Trees.mp4 42.64 MB
14. Simple Decision Trees/10. Test-Train split in Python.mp4 24.87 MB
14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp4 43.98 MB
14. Simple Decision Trees/12. Creating Decision tree in Python.mp4 17.87 MB
14. Simple Decision Trees/13. Building a Regression Tree in R.mp4 103.34 MB
14. Simple Decision Trees/14. Evaluating model performance in Python.mp4 16.44 MB
14. Simple Decision Trees/15. Plotting decision tree in Python.mp4 21.48 MB
14. Simple Decision Trees/16. Pruning a tree.mp4 18.46 MB
14. Simple Decision Trees/17. Pruning a tree in Python.mp4 73.5 MB
14. Simple Decision Trees/18. Pruning a Tree in R.mp4 82.1 MB
14. Simple Decision Trees/2. Understanding a Regression Tree.mp4 43.72 MB
14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.mp4 13.98 MB
14. Simple Decision Trees/4. The Data set for this part.mp4 37.26 MB
14. Simple Decision Trees/5. Importing the Data set into Python.mp4 25.85 MB
14. Simple Decision Trees/6. Importing the Data set into R.mp4 43.7 MB
14. Simple Decision Trees/7. Missing value treatment in Python.mp4 17.93 MB
14. Simple Decision Trees/8. Dummy Variable creation in Python.mp4 24.94 MB
14. Simple Decision Trees/9. Dependent- Independent Data split in Python.mp4 15.18 MB
15. Simple Classification Tree/1. Classification tree.mp4 28.2 MB
15. Simple Classification Tree/2. The Data set for Classification problem.mp4 18.57 MB
15. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp4 45.38 MB
15. Simple Classification Tree/4. Classification tree in Python Training.mp4 82.72 MB
15. Simple Classification Tree/5. Building a classification Tree in R.mp4 85.1 MB
15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.mp4 6.86 MB
16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp4 28.14 MB
16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4 77.3 MB
16. Ensemble technique 1 - Bagging/3. Bagging in R.mp4 58.96 MB
17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp4 18.2 MB
17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp4 46.7 MB
17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4 80.67 MB
17. Ensemble technique 2 - Random Forests/4. Random Forest in R.mp4 30.72 MB
18. Ensemble technique 3 - Boosting/1. Boosting.mp4 30.58 MB
18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp4 39.88 MB
18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp4 69.09 MB
18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.mp4 30.54 MB
18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp4 88.67 MB
18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp4 75.01 MB
18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4 161.3 MB
19. Maximum Margin Classifier/1. Content flow.mp4 8.64 MB
19. Maximum Margin Classifier/2. The Concept of a Hyperplane.mp4 29.42 MB
19. Maximum Margin Classifier/3. Maximum Margin Classifier.mp4 22.48 MB
19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.mp4 10.61 MB
2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 16.27 MB
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/2. This is a milestone!.mp4 20.66 MB
2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.mp4 65.19 MB
2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.mp4 40.92 MB
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/6. Strings in Python Python Basics.mp4 64.44 MB
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/8. Working with Numpy Library of Python.mp4 43.88 MB
2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.mp4 46.88 MB
20. Support Vector Classifier/1. Support Vector classifiers.mp4 56.17 MB
20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp4 10.8 MB
21. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4 40.12 MB
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/10. Classification model - Standardizing the data.mp4 9.72 MB
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/12. Hyper Parameter Tuning.mp4 57.74 MB
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/14. Radial Kernel with Hyperparameter Tuning.mp4 37.21 MB
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/3. Importing data for regression model.mp4 25.84 MB
22. Creating Support Vector Machine Model in Python/4. X-y Split.mp4 15.18 MB
22. Creating Support Vector Machine Model in Python/5. Test-Train Split.mp4 24.87 MB
22. Creating Support Vector Machine Model in Python/6. Standardizing the data.mp4 38.41 MB
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/8. The Data set for the Classification problem.mp4 18.56 MB
22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.mp4 45.38 MB
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/2. Test-Train Split.mp4 50.48 MB
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/5. Hyperparameter Tuning for Linear Kernel.mp4 60.5 MB
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/7. Radial Kernel with Hyperparameter Tuning.mp4 56.68 MB
23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.mp4 106.12 MB
24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp4 29.07 MB
24. Introduction - Deep Learning/2. Perceptron.mp4 44.75 MB
24. Introduction - Deep Learning/3. Activation Functions.mp4 34.62 MB
24. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp4 86.56 MB
25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 40.42 MB
25. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 60.34 MB
25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 122.2 MB
25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp4 62.18 MB
25. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp4 45.36 MB
26. ANN in Python/1. Keras and Tensorflow.mp4 14.92 MB
26. ANN in Python/10. Using Functional API for complex architectures.mp4 92.11 MB
26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4 151.59 MB
26. ANN in Python/12. Hyperparameter Tuning.mp4 60.63 MB
26. ANN in Python/2. Installing Tensorflow and Keras.mp4 20.06 MB
26. ANN in Python/3. Dataset for classification.mp4 56.19 MB
26. ANN in Python/4. Normalization and Test-Train split.mp4 44.2 MB
26. ANN in Python/5. Different ways to create ANN using Keras.mp4 10.82 MB
26. ANN in Python/6. Building the Neural Network using Keras.mp4 79.11 MB
26. ANN in Python/7. Compiling and Training the Neural Network model.mp4 81.63 MB
26. ANN in Python/8. Evaluating performance and Predicting using Keras.mp4 69.91 MB
26. ANN in Python/9. Building Neural Network for Regression Problem.mp4 155.9 MB
27. ANN in R/1. Installing Keras and Tensorflow.mp4 22.79 MB
27. ANN in R/2. Data Normalization and Test-Train Split.mp4 111.78 MB
27. ANN in R/3. Building,Compiling and Training.mp4 130.74 MB
27. ANN in R/4. Evaluating and Predicting.mp4 99.28 MB
27. ANN in R/5. ANN with NeuralNets Package.mp4 84.42 MB
27. ANN in R/6. Building Regression Model with Functional API.mp4 131.13 MB
27. ANN in R/7. Complex Architectures using Functional API.mp4 79.57 MB
27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4 216.03 MB
28. CNN - Basics/1. CNN Introduction.mp4 51.16 MB
28. CNN - Basics/2. Stride.mp4 16.58 MB
28. CNN - Basics/3. Padding.mp4 31.63 MB
28. CNN - Basics/4. Filters and Feature maps.mp4 52.71 MB
28. CNN - Basics/5. Channels.mp4 67.77 MB
28. CNN - Basics/6. PoolingLayer.mp4 46.88 MB
29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp4 40.63 MB
29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp4 43.26 MB
29. Creating CNN model in Python/3. CNN model in Python - Training and results.mp4 55.15 MB
29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp4 57.97 MB
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/2. Basics of R and R studio.mp4 38.85 MB
3. Setting up R Studio and R crash course/3. Packages in R.mp4 82.95 MB
3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp4 40.74 MB
3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp4 25.52 MB
3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp4 60.11 MB
3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp4 96.74 MB
3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp4 42.02 MB
30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp4 7.35 MB
30. Creating CNN model in R/2. Data Preprocessing.mp4 67.03 MB
30. Creating CNN model in R/3. Creating Model Architecture.mp4 71.6 MB
30. Creating CNN model in R/4. Compiling and training.mp4 32.2 MB
30. Creating CNN model in R/5. Model Performance.mp4 68.08 MB
30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.mp4 44.6 MB
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/3. Project - Data Preprocessing in Python.mp4 71.83 MB
31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp4 65.98 MB
31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.mp4 21.02 MB
32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp4 87.76 MB
32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.mp4 46.12 MB
32. Project Creating CNN model from scratch/3. Project in R - Training.mp4 24.58 MB
32. Project Creating CNN model from scratch/4. Project in R - Model Performance.mp4 23.18 MB
32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp4 56.38 MB
32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.mp4 23.69 MB
33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.mp4 41.42 MB
33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.mp4 53.04 MB
34. Transfer Learning Basics/1. ILSVRC.mp4 20.93 MB
34. Transfer Learning Basics/2. LeNET.mp4 7 MB
34. Transfer Learning Basics/3. VGG16NET.mp4 10.35 MB
34. Transfer Learning Basics/4. GoogLeNet.mp4 21.37 MB
34. Transfer Learning Basics/5. Transfer Learning.mp4 29.99 MB
34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4 129.1 MB
35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4 101.57 MB
35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp4 64.11 MB
36. Time Series Analysis and Forecasting/1. Introduction.mp4 12.27 MB
36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp4 25.92 MB
36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp4 10.11 MB
36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp4 34.5 MB
36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp4 62.48 MB
37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4 108.87 MB
37. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp4 8.39 MB
37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp4 63.72 MB
37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4 165.2 MB
37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp4 59.48 MB
37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4 112.69 MB
37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.mp4 16.96 MB
37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4 100.67 MB
37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp4 14.86 MB
37. Time Series - Preprocessing in Python/9. Moving Average.mp4 38.71 MB
38. Time Series - Important Concepts/1. White Noise.mp4 11.37 MB
38. Time Series - Important Concepts/2. Random Walk.mp4 21.17 MB
38. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp4 59.84 MB
38. Time Series - Important Concepts/4. Differencing.mp4 32.35 MB
38. Time Series - Important Concepts/5. Differencing in Python.mp4 113.01 MB
39. Time Series - Implementation in Python/1. Test Train Split in Python.mp4 57.42 MB
39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp4 43.38 MB
39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp4 16.89 MB
39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp4 53.49 MB
39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4 49.6 MB
39. Time Series - Implementation in Python/6. Moving Average model -Basics.mp4 24.1 MB
39. Time Series - Implementation in Python/7. Moving Average model in Python.mp4 56.65 MB
4. Basics of Statistics/1. Types of Data.mp4 21.76 MB
4. Basics of Statistics/2. Types of Statistics.mp4 10.94 MB
4. Basics of Statistics/3. Describing data Graphically.mp4 65.4 MB
4. Basics of Statistics/4. Measures of Centers.mp4 38.58 MB
4. Basics of Statistics/5. Measures of Dispersion.mp4 22.85 MB
40. Time Series - ARIMA model/1. ACF and PACF.mp4 41.23 MB
40. Time Series - ARIMA model/2. ARIMA model - Basics.mp4 21.37 MB
40. Time Series - ARIMA model/3. ARIMA model in Python.mp4 74.44 MB
40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4 32.15 MB
41. Time Series - SARIMA model/1. SARIMA model.mp4 39.03 MB
41. Time Series - SARIMA model/2. SARIMA model in Python.mp4 66.23 MB
41. Time Series - SARIMA model/3. Stationary time Series.mp4 5.58 MB
42. Bonus Section/1. The final milestone!.mp4 11.85 MB
5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 109.18 MB
5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4 39.48 MB
6. Data Preprocessing/1. Gathering Business Knowledge.mp4 22.29 MB
6. Data Preprocessing/10. Outlier Treatment in Python.mp4 70.26 MB
6. Data Preprocessing/11. Outlier Treatment in R.mp4 30.74 MB
6. Data Preprocessing/12. Missing Value Imputation.mp4 25 MB
6. Data Preprocessing/13. Missing Value Imputation in Python.mp4 23.42 MB
6. Data Preprocessing/14. Missing Value imputation in R.mp4 26.01 MB
6. Data Preprocessing/15. Seasonality in Data.mp4 17.02 MB
6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4 100.4 MB
6. Data Preprocessing/17. Variable transformation and deletion in Python.mp4 44.12 MB
6. Data Preprocessing/18. Variable transformation in R.mp4 55.43 MB
6. Data Preprocessing/19. Non-usable variables.mp4 20.25 MB
6. Data Preprocessing/2. Data Exploration.mp4 20.51 MB
6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4 36.81 MB
6. Data Preprocessing/21. Dummy variable creation in Python.mp4 26.53 MB
6. Data Preprocessing/22. Dummy variable creation in R.mp4 43.99 MB
6. Data Preprocessing/23. Correlation Analysis.mp4 71.6 MB
6. Data Preprocessing/24. Correlation Analysis in Python.mp4 55.3 MB
6. Data Preprocessing/25. Correlation Matrix in R.mp4 83.13 MB
6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 69.29 MB
6. Data Preprocessing/4. Importing Data in Python.mp4 27.84 MB
6. Data Preprocessing/5. Importing the dataset into R.mp4 13.12 MB
6. Data Preprocessing/6. Univariate analysis and EDD.mp4 24.19 MB
6. Data Preprocessing/7. EDD in Python.mp4 61.81 MB
6. Data Preprocessing/8. EDD in R.mp4 96.98 MB
6. Data Preprocessing/9. Outlier Treatment.mp4 24.5 MB
7. Linear Regression/1. The Problem Statement.mp4 9.37 MB
7. Linear Regression/10. Multiple Linear Regression in Python.mp4 69.74 MB
7. Linear Regression/11. Multiple Linear Regression in R.mp4 62.38 MB
7. Linear Regression/12. Test-train split.mp4 41.88 MB
7. Linear Regression/13. Bias Variance trade-off.mp4 25.09 MB
7. Linear Regression/14. Test train split in Python.mp4 44.88 MB
7. Linear Regression/15. Test-Train Split in R.mp4 75.6 MB
7. Linear Regression/16. Regression models other than OLS.mp4 16.55 MB
7. Linear Regression/17. Subset selection techniques.mp4 79.07 MB
7. Linear Regression/18. Subset selection in R.mp4 63.53 MB
7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp4 33.34 MB
7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 43.37 MB
7. Linear Regression/20. Ridge regression and Lasso in Python.mp4 128.85 MB
7. Linear Regression/21. Ridge regression and Lasso in R.mp4 103.43 MB
7. Linear Regression/22. Heteroscedasticity.mp4 14.49 MB
7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 92.11 MB
7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 43.6 MB
7. Linear Regression/5. Simple Linear Regression in Python.mp4 63.43 MB
7. Linear Regression/6. Simple Linear Regression in R.mp4 40.83 MB
7. Linear Regression/7. Multiple Linear Regression.mp4 34.32 MB
7. Linear Regression/8. The F - statistic.mp4 55.99 MB
7. Linear Regression/9. Interpreting results of Categorical variables.mp4 22.5 MB
8. Classification Models Data Preparation/1. The Data and the Data Dictionary.mp4 79.01 MB
8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.mp4 29.26 MB
8. Classification Models Data Preparation/11. Variable transformation in R.mp4 38.03 MB
8. Classification Models Data Preparation/12. Dummy variable creation in Python.mp4 26.37 MB
8. Classification Models Data Preparation/13. Dummy variable creation in R.mp4 44.36 MB
8. Classification Models Data Preparation/2. Data Import in Python.mp4 22.06 MB
8. Classification Models Data Preparation/3. Importing the dataset into R.mp4 13.47 MB
8. Classification Models Data Preparation/4. EDD in Python.mp4 77.63 MB
8. Classification Models Data Preparation/5. EDD in R.mp4 66.52 MB
8. Classification Models Data Preparation/6. Outlier treatment in Python.mp4 47.32 MB
8. Classification Models Data Preparation/7. Outlier Treatment in R.mp4 25.37 MB
8. Classification Models Data Preparation/8. Missing Value Imputation in Python.mp4 22.56 MB
8. Classification Models Data Preparation/9. Missing Value imputation in R.mp4 19.05 MB
9. The Three classification models/1. Three Classifiers and the problem statement.mp4 20.34 MB
9. The Three classification models/2. Why can't we use Linear Regression.mp4 16.94 MB
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