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[GigaCourse.Com] Udemy - Machine Learning & Deep Learning in Python & R

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GigaCourse Com Udemy - Machine Learning & Deep Learning in Python & R

文件列表
  1. 1. Introduction/1. Introduction.mp429.4MB
  2. 10. Logistic Regression/1. Logistic Regression.mp432.93MB
  3. 10. Logistic Regression/10. Evaluating performance of model.mp435.17MB
  4. 10. Logistic Regression/11. Evaluating model performance in Python.mp49.02MB
  5. 10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp455.7MB
  6. 10. Logistic Regression/2. Training a Simple Logistic Model in Python.mp447.87MB
  7. 10. Logistic Regression/3. Training a Simple Logistic model in R.mp425.57MB
  8. 10. Logistic Regression/4. Result of Simple Logistic Regression.mp426.94MB
  9. 10. Logistic Regression/5. Logistic with multiple predictors.mp48.53MB
  10. 10. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp426.25MB
  11. 10. Logistic Regression/7. Training multiple predictor Logistic model in R.mp415.78MB
  12. 10. Logistic Regression/8. Confusion Matrix.mp421.1MB
  13. 10. Logistic Regression/9. Creating Confusion Matrix in Python.mp451.25MB
  14. 11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp440.96MB
  15. 11. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp411.4MB
  16. 11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp474.36MB
  17. 12. K-Nearest Neighbors classifier/1. Test-Train Split.mp439.3MB
  18. 12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp433.1MB
  19. 12. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp474.23MB
  20. 12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp475.42MB
  21. 12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp437.23MB
  22. 12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp442.36MB
  23. 12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp464.85MB
  24. 13. Comparing results from 3 models/1. Understanding the results of classification models.mp441.64MB
  25. 13. Comparing results from 3 models/2. Summary of the three models.mp422.22MB
  26. 14. Simple Decision Trees/1. Basics of Decision Trees.mp442.64MB
  27. 14. Simple Decision Trees/10. Test-Train split in Python.mp424.87MB
  28. 14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp443.98MB
  29. 14. Simple Decision Trees/12. Creating Decision tree in Python.mp417.87MB
  30. 14. Simple Decision Trees/13. Building a Regression Tree in R.mp4103.34MB
  31. 14. Simple Decision Trees/14. Evaluating model performance in Python.mp416.44MB
  32. 14. Simple Decision Trees/15. Plotting decision tree in Python.mp421.48MB
  33. 14. Simple Decision Trees/16. Pruning a tree.mp418.46MB
  34. 14. Simple Decision Trees/17. Pruning a tree in Python.mp473.5MB
  35. 14. Simple Decision Trees/18. Pruning a Tree in R.mp482.1MB
  36. 14. Simple Decision Trees/2. Understanding a Regression Tree.mp443.72MB
  37. 14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.mp413.98MB
  38. 14. Simple Decision Trees/4. The Data set for this part.mp437.26MB
  39. 14. Simple Decision Trees/5. Importing the Data set into Python.mp425.85MB
  40. 14. Simple Decision Trees/6. Importing the Data set into R.mp443.7MB
  41. 14. Simple Decision Trees/7. Missing value treatment in Python.mp417.93MB
  42. 14. Simple Decision Trees/8. Dummy Variable creation in Python.mp424.94MB
  43. 14. Simple Decision Trees/9. Dependent- Independent Data split in Python.mp415.18MB
  44. 15. Simple Classification Tree/1. Classification tree.mp428.2MB
  45. 15. Simple Classification Tree/2. The Data set for Classification problem.mp418.57MB
  46. 15. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp445.38MB
  47. 15. Simple Classification Tree/4. Classification tree in Python Training.mp482.72MB
  48. 15. Simple Classification Tree/5. Building a classification Tree in R.mp485.1MB
  49. 15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.mp46.86MB
  50. 16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp428.14MB
  51. 16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp477.3MB
  52. 16. Ensemble technique 1 - Bagging/3. Bagging in R.mp458.96MB
  53. 17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp418.2MB
  54. 17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp446.7MB
  55. 17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp480.67MB
  56. 17. Ensemble technique 2 - Random Forests/4. Random Forest in R.mp430.72MB
  57. 18. Ensemble technique 3 - Boosting/1. Boosting.mp430.58MB
  58. 18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp439.88MB
  59. 18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp469.09MB
  60. 18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.mp430.54MB
  61. 18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp488.67MB
  62. 18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp475.01MB
  63. 18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4161.3MB
  64. 19. Maximum Margin Classifier/1. Content flow.mp48.64MB
  65. 19. Maximum Margin Classifier/2. The Concept of a Hyperplane.mp429.42MB
  66. 19. Maximum Margin Classifier/3. Maximum Margin Classifier.mp422.48MB
  67. 19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.mp410.61MB
  68. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp416.27MB
  69. 2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.mp440.37MB
  70. 2. Setting up Python and Jupyter Notebook/2. This is a milestone!.mp420.66MB
  71. 2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.mp465.19MB
  72. 2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.mp440.92MB
  73. 2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.mp412.74MB
  74. 2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.mp464.44MB
  75. 2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.mp460.33MB
  76. 2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.mp443.88MB
  77. 2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.mp446.88MB
  78. 20. Support Vector Classifier/1. Support Vector classifiers.mp456.17MB
  79. 20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp410.8MB
  80. 21. Support Vector Machines/1. Kernel Based Support Vector Machines.mp440.12MB
  81. 22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp44.04MB
  82. 22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.mp49.72MB
  83. 22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.mp464.13MB
  84. 22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.mp457.74MB
  85. 22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.mp422.92MB
  86. 22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.mp437.21MB
  87. 22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.mp437.2MB
  88. 22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.mp425.84MB
  89. 22. Creating Support Vector Machine Model in Python/4. X-y Split.mp415.18MB
  90. 22. Creating Support Vector Machine Model in Python/5. Test-Train Split.mp424.87MB
  91. 22. Creating Support Vector Machine Model in Python/6. Standardizing the data.mp438.41MB
  92. 22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.mp467.64MB
  93. 22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.mp418.56MB
  94. 22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.mp445.38MB
  95. 23. Creating Support Vector Machine Model in R/1. Importing Data into R.mp453.67MB
  96. 23. Creating Support Vector Machine Model in R/2. Test-Train Split.mp450.48MB
  97. 23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.mp4139.16MB
  98. 23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.mp460.5MB
  99. 23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.mp483.14MB
  100. 23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.mp456.68MB
  101. 23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.mp4106.12MB
  102. 24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp429.07MB
  103. 24. Introduction - Deep Learning/2. Perceptron.mp444.75MB
  104. 24. Introduction - Deep Learning/3. Activation Functions.mp434.62MB
  105. 24. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp486.56MB
  106. 25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.42MB
  107. 25. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.34MB
  108. 25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.2MB
  109. 25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp462.18MB
  110. 25. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp445.36MB
  111. 26. ANN in Python/1. Keras and Tensorflow.mp414.92MB
  112. 26. ANN in Python/10. Using Functional API for complex architectures.mp492.11MB
  113. 26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4151.59MB
  114. 26. ANN in Python/12. Hyperparameter Tuning.mp460.63MB
  115. 26. ANN in Python/2. Installing Tensorflow and Keras.mp420.06MB
  116. 26. ANN in Python/3. Dataset for classification.mp456.19MB
  117. 26. ANN in Python/4. Normalization and Test-Train split.mp444.2MB
  118. 26. ANN in Python/5. Different ways to create ANN using Keras.mp410.82MB
  119. 26. ANN in Python/6. Building the Neural Network using Keras.mp479.11MB
  120. 26. ANN in Python/7. Compiling and Training the Neural Network model.mp481.63MB
  121. 26. ANN in Python/8. Evaluating performance and Predicting using Keras.mp469.91MB
  122. 26. ANN in Python/9. Building Neural Network for Regression Problem.mp4155.9MB
  123. 27. ANN in R/1. Installing Keras and Tensorflow.mp422.79MB
  124. 27. ANN in R/2. Data Normalization and Test-Train Split.mp4111.78MB
  125. 27. ANN in R/3. Building,Compiling and Training.mp4130.74MB
  126. 27. ANN in R/4. Evaluating and Predicting.mp499.28MB
  127. 27. ANN in R/5. ANN with NeuralNets Package.mp484.42MB
  128. 27. ANN in R/6. Building Regression Model with Functional API.mp4131.13MB
  129. 27. ANN in R/7. Complex Architectures using Functional API.mp479.57MB
  130. 27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4216.03MB
  131. 28. CNN - Basics/1. CNN Introduction.mp451.16MB
  132. 28. CNN - Basics/2. Stride.mp416.58MB
  133. 28. CNN - Basics/3. Padding.mp431.63MB
  134. 28. CNN - Basics/4. Filters and Feature maps.mp452.71MB
  135. 28. CNN - Basics/5. Channels.mp467.77MB
  136. 28. CNN - Basics/6. PoolingLayer.mp446.88MB
  137. 29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp440.63MB
  138. 29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp443.26MB
  139. 29. Creating CNN model in Python/3. CNN model in Python - Training and results.mp455.15MB
  140. 29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp457.97MB
  141. 3. Setting up R Studio and R crash course/1. Installing R and R studio.mp435.71MB
  142. 3. Setting up R Studio and R crash course/2. Basics of R and R studio.mp438.85MB
  143. 3. Setting up R Studio and R crash course/3. Packages in R.mp482.95MB
  144. 3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp440.74MB
  145. 3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp425.52MB
  146. 3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp460.11MB
  147. 3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp496.74MB
  148. 3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp442.02MB
  149. 30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp47.35MB
  150. 30. Creating CNN model in R/2. Data Preprocessing.mp467.03MB
  151. 30. Creating CNN model in R/3. Creating Model Architecture.mp471.6MB
  152. 30. Creating CNN model in R/4. Compiling and training.mp432.2MB
  153. 30. Creating CNN model in R/5. Model Performance.mp468.08MB
  154. 30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.mp444.6MB
  155. 31. Project Creating CNN model from scratch in Python/1. Project - Introduction.mp449.39MB
  156. 31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.mp471.83MB
  157. 31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp465.98MB
  158. 31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.mp421.02MB
  159. 32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp487.76MB
  160. 32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.mp446.12MB
  161. 32. Project Creating CNN model from scratch/3. Project in R - Training.mp424.58MB
  162. 32. Project Creating CNN model from scratch/4. Project in R - Model Performance.mp423.18MB
  163. 32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp456.38MB
  164. 32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.mp423.69MB
  165. 33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.mp441.42MB
  166. 33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.mp453.04MB
  167. 34. Transfer Learning Basics/1. ILSVRC.mp420.93MB
  168. 34. Transfer Learning Basics/2. LeNET.mp47MB
  169. 34. Transfer Learning Basics/3. VGG16NET.mp410.35MB
  170. 34. Transfer Learning Basics/4. GoogLeNet.mp421.37MB
  171. 34. Transfer Learning Basics/5. Transfer Learning.mp429.99MB
  172. 34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4129.1MB
  173. 35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4101.57MB
  174. 35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp464.11MB
  175. 36. Time Series Analysis and Forecasting/1. Introduction.mp412.27MB
  176. 36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp425.92MB
  177. 36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp410.11MB
  178. 36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp434.5MB
  179. 36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp462.48MB
  180. 37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4108.87MB
  181. 37. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp48.39MB
  182. 37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp463.72MB
  183. 37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4165.2MB
  184. 37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp459.48MB
  185. 37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4112.69MB
  186. 37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.mp416.96MB
  187. 37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4100.67MB
  188. 37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp414.86MB
  189. 37. Time Series - Preprocessing in Python/9. Moving Average.mp438.71MB
  190. 38. Time Series - Important Concepts/1. White Noise.mp411.37MB
  191. 38. Time Series - Important Concepts/2. Random Walk.mp421.17MB
  192. 38. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp459.84MB
  193. 38. Time Series - Important Concepts/4. Differencing.mp432.35MB
  194. 38. Time Series - Important Concepts/5. Differencing in Python.mp4113.01MB
  195. 39. Time Series - Implementation in Python/1. Test Train Split in Python.mp457.42MB
  196. 39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp443.38MB
  197. 39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp416.89MB
  198. 39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp453.49MB
  199. 39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp449.6MB
  200. 39. Time Series - Implementation in Python/6. Moving Average model -Basics.mp424.1MB
  201. 39. Time Series - Implementation in Python/7. Moving Average model in Python.mp456.65MB
  202. 4. Basics of Statistics/1. Types of Data.mp421.76MB
  203. 4. Basics of Statistics/2. Types of Statistics.mp410.94MB
  204. 4. Basics of Statistics/3. Describing data Graphically.mp465.4MB
  205. 4. Basics of Statistics/4. Measures of Centers.mp438.58MB
  206. 4. Basics of Statistics/5. Measures of Dispersion.mp422.85MB
  207. 40. Time Series - ARIMA model/1. ACF and PACF.mp441.23MB
  208. 40. Time Series - ARIMA model/2. ARIMA model - Basics.mp421.37MB
  209. 40. Time Series - ARIMA model/3. ARIMA model in Python.mp474.44MB
  210. 40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp432.15MB
  211. 41. Time Series - SARIMA model/1. SARIMA model.mp439.03MB
  212. 41. Time Series - SARIMA model/2. SARIMA model in Python.mp466.23MB
  213. 41. Time Series - SARIMA model/3. Stationary time Series.mp45.58MB
  214. 42. Bonus Section/1. The final milestone!.mp411.85MB
  215. 5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4109.18MB
  216. 5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp439.48MB
  217. 6. Data Preprocessing/1. Gathering Business Knowledge.mp422.29MB
  218. 6. Data Preprocessing/10. Outlier Treatment in Python.mp470.26MB
  219. 6. Data Preprocessing/11. Outlier Treatment in R.mp430.74MB
  220. 6. Data Preprocessing/12. Missing Value Imputation.mp425MB
  221. 6. Data Preprocessing/13. Missing Value Imputation in Python.mp423.42MB
  222. 6. Data Preprocessing/14. Missing Value imputation in R.mp426.01MB
  223. 6. Data Preprocessing/15. Seasonality in Data.mp417.02MB
  224. 6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4100.4MB
  225. 6. Data Preprocessing/17. Variable transformation and deletion in Python.mp444.12MB
  226. 6. Data Preprocessing/18. Variable transformation in R.mp455.43MB
  227. 6. Data Preprocessing/19. Non-usable variables.mp420.25MB
  228. 6. Data Preprocessing/2. Data Exploration.mp420.51MB
  229. 6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp436.81MB
  230. 6. Data Preprocessing/21. Dummy variable creation in Python.mp426.53MB
  231. 6. Data Preprocessing/22. Dummy variable creation in R.mp443.99MB
  232. 6. Data Preprocessing/23. Correlation Analysis.mp471.6MB
  233. 6. Data Preprocessing/24. Correlation Analysis in Python.mp455.3MB
  234. 6. Data Preprocessing/25. Correlation Matrix in R.mp483.13MB
  235. 6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp469.29MB
  236. 6. Data Preprocessing/4. Importing Data in Python.mp427.84MB
  237. 6. Data Preprocessing/5. Importing the dataset into R.mp413.12MB
  238. 6. Data Preprocessing/6. Univariate analysis and EDD.mp424.19MB
  239. 6. Data Preprocessing/7. EDD in Python.mp461.81MB
  240. 6. Data Preprocessing/8. EDD in R.mp496.98MB
  241. 6. Data Preprocessing/9. Outlier Treatment.mp424.5MB
  242. 7. Linear Regression/1. The Problem Statement.mp49.37MB
  243. 7. Linear Regression/10. Multiple Linear Regression in Python.mp469.74MB
  244. 7. Linear Regression/11. Multiple Linear Regression in R.mp462.38MB
  245. 7. Linear Regression/12. Test-train split.mp441.88MB
  246. 7. Linear Regression/13. Bias Variance trade-off.mp425.09MB
  247. 7. Linear Regression/14. Test train split in Python.mp444.88MB
  248. 7. Linear Regression/15. Test-Train Split in R.mp475.6MB
  249. 7. Linear Regression/16. Regression models other than OLS.mp416.55MB
  250. 7. Linear Regression/17. Subset selection techniques.mp479.07MB
  251. 7. Linear Regression/18. Subset selection in R.mp463.53MB
  252. 7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp433.34MB
  253. 7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp443.37MB
  254. 7. Linear Regression/20. Ridge regression and Lasso in Python.mp4128.85MB
  255. 7. Linear Regression/21. Ridge regression and Lasso in R.mp4103.43MB
  256. 7. Linear Regression/22. Heteroscedasticity.mp414.49MB
  257. 7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp492.11MB
  258. 7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp443.6MB
  259. 7. Linear Regression/5. Simple Linear Regression in Python.mp463.43MB
  260. 7. Linear Regression/6. Simple Linear Regression in R.mp440.83MB
  261. 7. Linear Regression/7. Multiple Linear Regression.mp434.32MB
  262. 7. Linear Regression/8. The F - statistic.mp455.99MB
  263. 7. Linear Regression/9. Interpreting results of Categorical variables.mp422.5MB
  264. 8. Classification Models Data Preparation/1. The Data and the Data Dictionary.mp479.01MB
  265. 8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.mp429.26MB
  266. 8. Classification Models Data Preparation/11. Variable transformation in R.mp438.03MB
  267. 8. Classification Models Data Preparation/12. Dummy variable creation in Python.mp426.37MB
  268. 8. Classification Models Data Preparation/13. Dummy variable creation in R.mp444.36MB
  269. 8. Classification Models Data Preparation/2. Data Import in Python.mp422.06MB
  270. 8. Classification Models Data Preparation/3. Importing the dataset into R.mp413.47MB
  271. 8. Classification Models Data Preparation/4. EDD in Python.mp477.63MB
  272. 8. Classification Models Data Preparation/5. EDD in R.mp466.52MB
  273. 8. Classification Models Data Preparation/6. Outlier treatment in Python.mp447.32MB
  274. 8. Classification Models Data Preparation/7. Outlier Treatment in R.mp425.37MB
  275. 8. Classification Models Data Preparation/8. Missing Value Imputation in Python.mp422.56MB
  276. 8. Classification Models Data Preparation/9. Missing Value imputation in R.mp419.05MB
  277. 9. The Three classification models/1. Three Classifiers and the problem statement.mp420.34MB
  278. 9. The Three classification models/2. Why can't we use Linear Regression.mp416.94MB
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不会用的朋友看这里 把磁力链接复制到离线下载，或者bt下载软件里即可下载文件，或者直接复制迅雷链接到迅雷里下载！ 亲，你造吗？将网页分享给您的基友，下载的人越多速度越快哦！

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