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[] Udemy - Machine Learning & Deep Learning in Python & R

  • 收录时间:2021-12-22 06:15:47
  • 文件大小:13GB
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文件列表

  1. 27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4 216MB
  2. 37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4 165MB
  3. 18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4 161MB
  4. 26. ANN in Python/9. Building Neural Network for Regression Problem.mp4 156MB
  5. 26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4 152MB
  6. 23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.mp4 139MB
  7. 27. ANN in R/6. Building Regression Model with Functional API.mp4 131MB
  8. 27. ANN in R/3. Building,Compiling and Training.mp4 131MB
  9. 34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4 129MB
  10. 7. Linear Regression/20. Ridge regression and Lasso in Python.mp4 129MB
  11. 25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 122MB
  12. 38. Time Series - Important Concepts/5. Differencing in Python.mp4 113MB
  13. 37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4 113MB
  14. 27. ANN in R/2. Data Normalization and Test-Train Split.mp4 112MB
  15. 5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 109MB
  16. 37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4 109MB
  17. 23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.mp4 106MB
  18. 7. Linear Regression/21. Ridge regression and Lasso in R.mp4 103MB
  19. 14. Simple Decision Trees/13. Building a Regression Tree in R.mp4 103MB
  20. 35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4 102MB
  21. 37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4 101MB
  22. 6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4 100MB
  23. 27. ANN in R/4. Evaluating and Predicting.mp4 99MB
  24. 6. Data Preprocessing/8. EDD in R.mp4 97MB
  25. 3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp4 97MB
  26. 7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4 92MB
  27. 26. ANN in Python/10. Using Functional API for complex architectures.mp4 92MB
  28. 18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp4 89MB
  29. 32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp4 88MB
  30. 24. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp4 87MB
  31. 15. Simple Classification Tree/5. Building a classification Tree in R.mp4 85MB
  32. 27. ANN in R/5. ANN with NeuralNets Package.mp4 84MB
  33. 23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.mp4 83MB
  34. 6. Data Preprocessing/25. Correlation Matrix in R.mp4 83MB
  35. 3. Setting up R Studio and R crash course/3. Packages in R.mp4 83MB
  36. 15. Simple Classification Tree/4. Classification tree in Python Training.mp4 83MB
  37. 14. Simple Decision Trees/18. Pruning a Tree in R.mp4 82MB
  38. 26. ANN in Python/7. Compiling and Training the Neural Network model.mp4 82MB
  39. 17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp4 81MB
  40. 27. ANN in R/7. Complex Architectures using Functional API.mp4 80MB
  41. 26. ANN in Python/6. Building the Neural Network using Keras.mp4 79MB
  42. 7. Linear Regression/17. Subset selection techniques.mp4 79MB
  43. 8. Classification Models Data Preparation/1. The Data and the Data Dictionary.mp4 79MB
  44. 8. Classification Models Data Preparation/4. EDD in Python.mp4 78MB
  45. 16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp4 77MB
  46. 7. Linear Regression/15. Test-Train Split in R.mp4 76MB
  47. 12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp4 75MB
  48. 18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp4 75MB
  49. 40. Time Series - ARIMA model/3. ARIMA model in Python.mp4 74MB
  50. 11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp4 74MB
  51. 12. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp4 74MB
  52. 14. Simple Decision Trees/17. Pruning a tree in Python.mp4 74MB
  53. 31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.mp4 72MB
  54. 30. Creating CNN model in R/3. Creating Model Architecture.mp4 72MB
  55. 6. Data Preprocessing/23. Correlation Analysis.mp4 72MB
  56. 6. Data Preprocessing/10. Outlier Treatment in Python.mp4 70MB
  57. 26. ANN in Python/8. Evaluating performance and Predicting using Keras.mp4 70MB
  58. 7. Linear Regression/10. Multiple Linear Regression in Python.mp4 70MB
  59. 6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 69MB
  60. 18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp4 69MB
  61. 30. Creating CNN model in R/5. Model Performance.mp4 68MB
  62. 28. CNN - Basics/5. Channels.mp4 68MB
  63. 22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.mp4 68MB
  64. 30. Creating CNN model in R/2. Data Preprocessing.mp4 67MB
  65. 8. Classification Models Data Preparation/5. EDD in R.mp4 67MB
  66. 41. Time Series - SARIMA model/2. SARIMA model in Python.mp4 66MB
  67. 31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp4 66MB
  68. 4. Basics of Statistics/3. Describing data Graphically.mp4 65MB
  69. 2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.mp4 65MB
  70. 12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp4 65MB
  71. 2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.mp4 64MB
  72. 22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.mp4 64MB
  73. 35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp4 64MB
  74. 37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp4 64MB
  75. 7. Linear Regression/18. Subset selection in R.mp4 64MB
  76. 7. Linear Regression/5. Simple Linear Regression in Python.mp4 63MB
  77. 36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp4 62MB
  78. 7. Linear Regression/11. Multiple Linear Regression in R.srt 62MB
  79. 7. Linear Regression/11. Multiple Linear Regression in R.mp4 62MB
  80. 25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp4 62MB
  81. 6. Data Preprocessing/7. EDD in Python.mp4 62MB
  82. 26. ANN in Python/12. Hyperparameter Tuning.mp4 61MB
  83. 23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.mp4 60MB
  84. 25. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 60MB
  85. 2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.mp4 60MB
  86. 3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp4 60MB
  87. 38. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp4 60MB
  88. 37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp4 59MB
  89. 16. Ensemble technique 1 - Bagging/3. Bagging in R.mp4 59MB
  90. 29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp4 58MB
  91. 22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.mp4 58MB
  92. 39. Time Series - Implementation in Python/1. Test Train Split in Python.mp4 57MB
  93. 23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.mp4 57MB
  94. 39. Time Series - Implementation in Python/7. Moving Average model in Python.mp4 57MB
  95. 32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp4 56MB
  96. 26. ANN in Python/3. Dataset for classification.mp4 56MB
  97. 20. Support Vector Classifier/1. Support Vector classifiers.mp4 56MB
  98. 7. Linear Regression/8. The F - statistic.mp4 56MB
  99. 10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 56MB
  100. 6. Data Preprocessing/18. Variable transformation in R.mp4 55MB
  101. 6. Data Preprocessing/24. Correlation Analysis in Python.mp4 55MB
  102. 29. Creating CNN model in Python/3. CNN model in Python - Training and results.mp4 55MB
  103. 23. Creating Support Vector Machine Model in R/1. Importing Data into R.mp4 54MB
  104. 39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp4 53MB
  105. 33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.mp4 53MB
  106. 28. CNN - Basics/4. Filters and Feature maps.mp4 53MB
  107. 10. Logistic Regression/9. Creating Confusion Matrix in Python.mp4 51MB
  108. 28. CNN - Basics/1. CNN Introduction.mp4 51MB
  109. 23. Creating Support Vector Machine Model in R/2. Test-Train Split.mp4 50MB
  110. 39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp4 50MB
  111. 31. Project Creating CNN model from scratch in Python/1. Project - Introduction.mp4 49MB
  112. 10. Logistic Regression/2. Training a Simple Logistic Model in Python.mp4 48MB
  113. 8. Classification Models Data Preparation/6. Outlier treatment in Python.mp4 47MB
  114. 2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.mp4 47MB
  115. 28. CNN - Basics/6. PoolingLayer.mp4 47MB
  116. 17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp4 47MB
  117. 32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.mp4 46MB
  118. 22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.mp4 45MB
  119. 15. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp4 45MB
  120. 25. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp4 45MB
  121. 7. Linear Regression/14. Test train split in Python.mp4 45MB
  122. 24. Introduction - Deep Learning/2. Perceptron.mp4 45MB
  123. 30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.mp4 45MB
  124. 8. Classification Models Data Preparation/13. Dummy variable creation in R.mp4 44MB
  125. 26. ANN in Python/4. Normalization and Test-Train split.mp4 44MB
  126. 6. Data Preprocessing/17. Variable transformation and deletion in Python.mp4 44MB
  127. 6. Data Preprocessing/22. Dummy variable creation in R.mp4 44MB
  128. 14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp4 44MB
  129. 2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.mp4 44MB
  130. 14. Simple Decision Trees/2. Understanding a Regression Tree.mp4 44MB
  131. 14. Simple Decision Trees/6. Importing the Data set into R.mp4 44MB
  132. 7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp4 44MB
  133. 7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp4 43MB
  134. 39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp4 43MB
  135. 29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp4 43MB
  136. 14. Simple Decision Trees/1. Basics of Decision Trees.mp4 43MB
  137. 12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp4 42MB
  138. 3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp4 42MB
  139. 7. Linear Regression/12. Test-train split.mp4 42MB
  140. 13. Comparing results from 3 models/1. Understanding the results of classification models.mp4 42MB
  141. 33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.mp4 41MB
  142. 40. Time Series - ARIMA model/1. ACF and PACF.mp4 41MB
  143. 11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp4 41MB
  144. 2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.mp4 41MB
  145. 7. Linear Regression/6. Simple Linear Regression in R.mp4 41MB
  146. 3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp4 41MB
  147. 29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp4 41MB
  148. 25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 40MB
  149. 2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.mp4 40MB
  150. 21. Support Vector Machines/1. Kernel Based Support Vector Machines.mp4 40MB
  151. 18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp4 40MB
  152. 5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp4 39MB
  153. 12. K-Nearest Neighbors classifier/1. Test-Train Split.mp4 39MB
  154. 41. Time Series - SARIMA model/1. SARIMA model.mp4 39MB
  155. 3. Setting up R Studio and R crash course/2. Basics of R and R studio.mp4 39MB
  156. 37. Time Series - Preprocessing in Python/9. Moving Average.mp4 39MB
  157. 4. Basics of Statistics/4. Measures of Centers.mp4 39MB
  158. 22. Creating Support Vector Machine Model in Python/6. Standardizing the data.mp4 38MB
  159. 8. Classification Models Data Preparation/11. Variable transformation in R.mp4 38MB
  160. 14. Simple Decision Trees/4. The Data set for this part.mp4 37MB
  161. 12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp4 37MB
  162. 22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.mp4 37MB
  163. 22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.mp4 37MB
  164. 6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp4 37MB
  165. 3. Setting up R Studio and R crash course/1. Installing R and R studio.mp4 36MB
  166. 10. Logistic Regression/10. Evaluating performance of model.mp4 35MB
  167. 24. Introduction - Deep Learning/3. Activation Functions.mp4 35MB
  168. 36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp4 34MB
  169. 7. Linear Regression/7. Multiple Linear Regression.mp4 34MB
  170. 7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp4 33MB
  171. 12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp4 33MB
  172. 10. Logistic Regression/1. Logistic Regression.mp4 33MB
  173. 38. Time Series - Important Concepts/4. Differencing.mp4 32MB
  174. 30. Creating CNN model in R/4. Compiling and training.mp4 32MB
  175. 40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp4 32MB
  176. 28. CNN - Basics/3. Padding.mp4 32MB
  177. 6. Data Preprocessing/11. Outlier Treatment in R.mp4 31MB
  178. 17. Ensemble technique 2 - Random Forests/4. Random Forest in R.mp4 31MB
  179. 18. Ensemble technique 3 - Boosting/1. Boosting.mp4 31MB
  180. 18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.mp4 31MB
  181. 34. Transfer Learning Basics/5. Transfer Learning.mp4 30MB
  182. 19. Maximum Margin Classifier/2. The Concept of a Hyperplane.mp4 29MB
  183. 1. Introduction/1. Introduction.mp4 29MB
  184. 8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.mp4 29MB
  185. 24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp4 29MB
  186. 15. Simple Classification Tree/1. Classification tree.mp4 28MB
  187. 16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp4 28MB
  188. 6. Data Preprocessing/4. Importing Data in Python.mp4 28MB
  189. 10. Logistic Regression/4. Result of Simple Logistic Regression.mp4 27MB
  190. 6. Data Preprocessing/21. Dummy variable creation in Python.mp4 27MB
  191. 8. Classification Models Data Preparation/12. Dummy variable creation in Python.mp4 26MB
  192. 10. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp4 26MB
  193. 6. Data Preprocessing/14. Missing Value imputation in R.mp4 26MB
  194. 36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp4 26MB
  195. 14. Simple Decision Trees/5. Importing the Data set into Python.mp4 26MB
  196. 22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.mp4 26MB
  197. 10. Logistic Regression/3. Training a Simple Logistic model in R.mp4 26MB
  198. 3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp4 26MB
  199. 8. Classification Models Data Preparation/7. Outlier Treatment in R.mp4 25MB
  200. 7. Linear Regression/13. Bias Variance trade-off.mp4 25MB
  201. 6. Data Preprocessing/12. Missing Value Imputation.mp4 25MB
  202. 14. Simple Decision Trees/8. Dummy Variable creation in Python.mp4 25MB
  203. 14. Simple Decision Trees/10. Test-Train split in Python.mp4 25MB
  204. 22. Creating Support Vector Machine Model in Python/5. Test-Train Split.mp4 25MB
  205. 32. Project Creating CNN model from scratch/3. Project in R - Training.mp4 25MB
  206. 6. Data Preprocessing/9. Outlier Treatment.mp4 24MB
  207. 6. Data Preprocessing/6. Univariate analysis and EDD.mp4 24MB
  208. 39. Time Series - Implementation in Python/6. Moving Average model -Basics.mp4 24MB
  209. 32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.mp4 24MB
  210. 6. Data Preprocessing/13. Missing Value Imputation in Python.mp4 23MB
  211. 32. Project Creating CNN model from scratch/4. Project in R - Model Performance.mp4 23MB
  212. 22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.mp4 23MB
  213. 4. Basics of Statistics/5. Measures of Dispersion.mp4 23MB
  214. 27. ANN in R/1. Installing Keras and Tensorflow.mp4 23MB
  215. 8. Classification Models Data Preparation/8. Missing Value Imputation in Python.mp4 23MB
  216. 7. Linear Regression/9. Interpreting results of Categorical variables.mp4 23MB
  217. 19. Maximum Margin Classifier/3. Maximum Margin Classifier.mp4 22MB
  218. 6. Data Preprocessing/1. Gathering Business Knowledge.mp4 22MB
  219. 13. Comparing results from 3 models/2. Summary of the three models.mp4 22MB
  220. 8. Classification Models Data Preparation/2. Data Import in Python.mp4 22MB
  221. 4. Basics of Statistics/1. Types of Data.mp4 22MB
  222. 14. Simple Decision Trees/15. Plotting decision tree in Python.mp4 21MB
  223. 40. Time Series - ARIMA model/2. ARIMA model - Basics.mp4 21MB
  224. 34. Transfer Learning Basics/4. GoogLeNet.mp4 21MB
  225. 38. Time Series - Important Concepts/2. Random Walk.mp4 21MB
  226. 10. Logistic Regression/8. Confusion Matrix.mp4 21MB
  227. 31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.mp4 21MB
  228. 34. Transfer Learning Basics/1. ILSVRC.mp4 21MB
  229. 2. Setting up Python and Jupyter Notebook/2. This is a milestone!.mp4 21MB
  230. 6. Data Preprocessing/2. Data Exploration.mp4 20MB
  231. 9. The Three classification models/1. Three Classifiers and the problem statement.mp4 20MB
  232. 6. Data Preprocessing/19. Non-usable variables.mp4 20MB
  233. 26. ANN in Python/2. Installing Tensorflow and Keras.mp4 20MB
  234. 8. Classification Models Data Preparation/9. Missing Value imputation in R.mp4 19MB
  235. 15. Simple Classification Tree/2. The Data set for Classification problem.mp4 19MB
  236. 22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.mp4 19MB
  237. 14. Simple Decision Trees/16. Pruning a tree.mp4 18MB
  238. 17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp4 18MB
  239. 14. Simple Decision Trees/7. Missing value treatment in Python.mp4 18MB
  240. 14. Simple Decision Trees/12. Creating Decision tree in Python.mp4 18MB
  241. 6. Data Preprocessing/15. Seasonality in Data.mp4 17MB
  242. 37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.mp4 17MB
  243. 9. The Three classification models/2. Why can't we use Linear Regression.mp4 17MB
  244. 39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp4 17MB
  245. 28. CNN - Basics/2. Stride.mp4 17MB
  246. 7. Linear Regression/16. Regression models other than OLS.mp4 17MB
  247. 14. Simple Decision Trees/14. Evaluating model performance in Python.mp4 16MB
  248. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp4 16MB
  249. 10. Logistic Regression/7. Training multiple predictor Logistic model in R.mp4 16MB
  250. 10. Logistic Regression/7. Training multiple predictor Logistic model in R.srt 15MB
  251. 14. Simple Decision Trees/9. Dependent- Independent Data split in Python.mp4 15MB
  252. 22. Creating Support Vector Machine Model in Python/4. X-y Split.mp4 15MB
  253. 26. ANN in Python/1. Keras and Tensorflow.mp4 15MB
  254. 37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp4 15MB
  255. 7. Linear Regression/22. Heteroscedasticity.mp4 14MB
  256. 14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.mp4 14MB
  257. 8. Classification Models Data Preparation/3. Importing the dataset into R.mp4 13MB
  258. 6. Data Preprocessing/5. Importing the dataset into R.mp4 13MB
  259. 2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.mp4 13MB
  260. 36. Time Series Analysis and Forecasting/1. Introduction.mp4 12MB
  261. 42. Bonus Section/1. The final milestone!.mp4 12MB
  262. 11. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp4 11MB
  263. 38. Time Series - Important Concepts/1. White Noise.mp4 11MB
  264. 4. Basics of Statistics/2. Types of Statistics.mp4 11MB
  265. 26. ANN in Python/5. Different ways to create ANN using Keras.mp4 11MB
  266. 20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp4 11MB
  267. 19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.mp4 11MB
  268. 34. Transfer Learning Basics/3. VGG16NET.mp4 10MB
  269. 36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp4 10MB
  270. 22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.mp4 10MB
  271. 7. Linear Regression/1. The Problem Statement.mp4 9MB
  272. 10. Logistic Regression/11. Evaluating model performance in Python.mp4 9MB
  273. 19. Maximum Margin Classifier/1. Content flow.mp4 9MB
  274. 10. Logistic Regression/5. Logistic with multiple predictors.mp4 9MB
  275. 37. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp4 8MB
  276. 30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp4 7MB
  277. 34. Transfer Learning Basics/2. LeNET.mp4 7MB
  278. 15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.mp4 7MB
  279. 41. Time Series - SARIMA model/3. Stationary time Series.mp4 6MB
  280. 22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp4 4MB
  281. 37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.srt 29KB
  282. 25. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 25KB
  283. 26. ANN in Python/9. Building Neural Network for Regression Problem.srt 24KB
  284. 27. ANN in R/8. Saving - Restoring Models and Using Callbacks.srt 21KB
  285. 7. Linear Regression/20. Ridge regression and Lasso in Python.srt 21KB
  286. 26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.srt 21KB
  287. 34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.srt 20KB
  288. 2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.srt 20KB
  289. 5. Introduction to Machine Learning/1. Introduction to Machine Learning.srt 20KB
  290. 6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.srt 19KB
  291. 37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.srt 19KB
  292. 18. Ensemble technique 3 - Boosting/7. XGBoosting in R.srt 18KB
  293. 2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.srt 18KB
  294. 8. Classification Models Data Preparation/4. EDD in Python.srt 18KB
  295. 23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.srt 18KB
  296. 37. Time Series - Preprocessing in Python/1. Data Loading in Python.srt 18KB
  297. 37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.srt 18KB
  298. 7. Linear Regression/3. Assessing accuracy of predicted coefficients.srt 17KB
  299. 27. ANN in R/3. Building,Compiling and Training.srt 16KB
  300. 38. Time Series - Important Concepts/5. Differencing in Python.srt 16KB
  301. 24. Introduction - Deep Learning/4. Python - Creating Perceptron model.srt 16KB
  302. 14. Simple Decision Trees/13. Building a Regression Tree in R.srt 16KB
  303. 3. Setting up R Studio and R crash course/7. Creating Barplots in R.srt 15KB
  304. 15. Simple Classification Tree/4. Classification tree in Python Training.srt 15KB
  305. 40. Time Series - ARIMA model/3. ARIMA model in Python.srt 14KB
  306. 7. Linear Regression/10. Multiple Linear Regression in Python.srt 14KB
  307. 35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).srt 14KB
  308. 6. Data Preprocessing/10. Outlier Treatment in Python.srt 14KB
  309. 17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.srt 14KB
  310. 7. Linear Regression/17. Subset selection techniques.srt 14KB
  311. 25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.srt 14KB
  312. 27. ANN in R/6. Building Regression Model with Functional API.srt 14KB
  313. 2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.srt 13KB
  314. 6. Data Preprocessing/8. EDD in R.srt 13KB
  315. 7. Linear Regression/5. Simple Linear Regression in Python.srt 13KB
  316. 26. ANN in Python/10. Using Functional API for complex architectures.srt 13KB
  317. 26. ANN in Python/6. Building the Neural Network using Keras.srt 13KB
  318. 27. ANN in R/2. Data Normalization and Test-Train Split.srt 13KB
  319. 4. Basics of Statistics/3. Describing data Graphically.srt 13KB
  320. 25. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 13KB
  321. 22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.srt 12KB
  322. 7. Linear Regression/21. Ridge regression and Lasso in R.srt 12KB
  323. 16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.srt 12KB
  324. 3. Setting up R Studio and R crash course/3. Packages in R.srt 12KB
  325. 23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.srt 12KB
  326. 39. Time Series - Implementation in Python/1. Test Train Split in Python.srt 12KB
  327. 3. Setting up R Studio and R crash course/2. Basics of R and R studio.srt 12KB
  328. 14. Simple Decision Trees/2. Understanding a Regression Tree.srt 12KB
  329. 6. Data Preprocessing/23. Correlation Analysis.srt 12KB
  330. 11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.srt 12KB
  331. 32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.srt 12KB
  332. 2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.srt 12KB
  333. 37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.srt 12KB
  334. 6. Data Preprocessing/7. EDD in Python.srt 12KB
  335. 41. Time Series - SARIMA model/2. SARIMA model in Python.srt 12KB
  336. 23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.srt 11KB
  337. 18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.srt 11KB
  338. 8. Classification Models Data Preparation/5. EDD in R.srt 11KB
  339. 14. Simple Decision Trees/1. Basics of Decision Trees.srt 11KB
  340. 7. Linear Regression/12. Test-train split.srt 11KB
  341. 20. Support Vector Classifier/1. Support Vector classifiers.srt 11KB
  342. 10. Logistic Regression/9. Creating Confusion Matrix in Python.srt 11KB
  343. 25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 11KB
  344. 22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.srt 11KB
  345. 14. Simple Decision Trees/17. Pruning a tree in Python.srt 11KB
  346. 10. Logistic Regression/2. Training a Simple Logistic Model in Python.srt 11KB
  347. 12. K-Nearest Neighbors classifier/1. Test-Train Split.srt 11KB
  348. 18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.srt 11KB
  349. 22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.srt 10KB
  350. 7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.srt 10KB
  351. 38. Time Series - Important Concepts/3. Decomposing Time Series in Python.srt 10KB
  352. 37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.srt 10KB
  353. 5. Introduction to Machine Learning/2. Building a Machine Learning Model.srt 10KB
  354. 11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.srt 10KB
  355. 24. Introduction - Deep Learning/2. Perceptron.srt 10KB
  356. 39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.srt 10KB
  357. 15. Simple Classification Tree/5. Building a classification Tree in R.srt 10KB
  358. 2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.srt 10KB
  359. 27. ANN in R/4. Evaluating and Predicting.srt 10KB
  360. 26. ANN in Python/7. Compiling and Training the Neural Network model.srt 10KB
  361. 12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.srt 10KB
  362. 6. Data Preprocessing/18. Variable transformation in R.srt 10KB
  363. 2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.srt 10KB
  364. 26. ANN in Python/12. Hyperparameter Tuning.srt 10KB
  365. 12. K-Nearest Neighbors classifier/3. Test-Train Split in R.srt 10KB
  366. 26. ANN in Python/8. Evaluating performance and Predicting using Keras.srt 10KB
  367. 7. Linear Regression/8. The F - statistic.srt 10KB
  368. 14. Simple Decision Trees/18. Pruning a Tree in R.srt 10KB
  369. 36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.srt 10KB
  370. 39. Time Series - Implementation in Python/7. Moving Average model in Python.srt 10KB
  371. 6. Data Preprocessing/25. Correlation Matrix in R.srt 10KB
  372. 8. Classification Models Data Preparation/6. Outlier treatment in Python.srt 10KB
  373. 10. Logistic Regression/10. Evaluating performance of model.srt 9KB
  374. 7. Linear Regression/15. Test-Train Split in R.srt 9KB
  375. 8. Classification Models Data Preparation/1. The Data and the Data Dictionary.srt 9KB
  376. 25. Neural Networks - Stacking cells to create network/5. Hyperparameter.srt 9KB
  377. 7. Linear Regression/6. Simple Linear Regression in R.srt 9KB
  378. 31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.srt 9KB
  379. 31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.srt 9KB
  380. 6. Data Preprocessing/17. Variable transformation and deletion in Python.srt 9KB
  381. 7. Linear Regression/19. Shrinkage methods Ridge and Lasso.srt 9KB
  382. 12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.srt 9KB
  383. 15. Simple Classification Tree/3. Classification tree in Python Preprocessing.srt 9KB
  384. 22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.srt 9KB
  385. 23. Creating Support Vector Machine Model in R/1. Importing Data into R.srt 9KB
  386. 27. ANN in R/7. Complex Architectures using Functional API.srt 9KB
  387. 35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).srt 9KB
  388. 39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.srt 9KB
  389. 6. Data Preprocessing/3. The Dataset and the Data Dictionary.srt 9KB
  390. 7. Linear Regression/14. Test train split in Python.srt 9KB
  391. 40. Time Series - ARIMA model/1. ACF and PACF.srt 9KB
  392. 10. Logistic Regression/1. Logistic Regression.srt 9KB
  393. 18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.srt 9KB
  394. 27. ANN in R/5. ANN with NeuralNets Package.srt 8KB
  395. 7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.srt 8KB
  396. 2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.srt 8KB
  397. 7. Linear Regression/18. Subset selection in R.srt 8KB
  398. 39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.srt 8KB
  399. 24. Introduction - Deep Learning/3. Activation Functions.srt 8KB
  400. 28. CNN - Basics/1. CNN Introduction.srt 8KB
  401. 26. ANN in Python/3. Dataset for classification.srt 8KB
  402. 41. Time Series - SARIMA model/1. SARIMA model.srt 8KB
  403. 4. Basics of Statistics/4. Measures of Centers.srt 8KB
  404. 32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.srt 8KB
  405. 18. Ensemble technique 3 - Boosting/1. Boosting.srt 8KB
  406. 37. Time Series - Preprocessing in Python/9. Moving Average.srt 8KB
  407. 28. CNN - Basics/4. Filters and Feature maps.srt 8KB
  408. 13. Comparing results from 3 models/1. Understanding the results of classification models.srt 8KB
  409. 31. Project Creating CNN model from scratch in Python/1. Project - Introduction.srt 7KB
  410. 30. Creating CNN model in R/2. Data Preprocessing.srt 7KB
  411. 10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.srt 7KB
  412. 12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.srt 7KB
  413. 16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.srt 7KB
  414. 29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.srt 7KB
  415. 22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.srt 7KB
  416. 33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.srt 7KB
  417. 14. Simple Decision Trees/6. Importing the Data set into R.srt 7KB
  418. 23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.srt 7KB
  419. 16. Ensemble technique 1 - Bagging/3. Bagging in R.srt 7KB
  420. 3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.srt 7KB
  421. 6. Data Preprocessing/24. Correlation Analysis in Python.srt 7KB
  422. 7. Linear Regression/13. Bias Variance trade-off.srt 7KB
  423. 23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.srt 7KB
  424. 12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.srt 7KB
  425. 33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.srt 7KB
  426. 3. Setting up R Studio and R crash course/1. Installing R and R studio.srt 7KB
  427. 8. Classification Models Data Preparation/11. Variable transformation in R.srt 7KB
  428. 15. Simple Classification Tree/1. Classification tree.srt 7KB
  429. 21. Support Vector Machines/1. Kernel Based Support Vector Machines.srt 7KB
  430. 17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.srt 7KB
  431. 38. Time Series - Important Concepts/4. Differencing.srt 7KB
  432. 30. Creating CNN model in R/5. Model Performance.srt 7KB
  433. 22. Creating Support Vector Machine Model in Python/6. Standardizing the data.srt 7KB
  434. 8. Classification Models Data Preparation/13. Dummy variable creation in R.srt 6KB
  435. 6. Data Preprocessing/4. Importing Data in Python.srt 6KB
  436. 36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).srt 6KB
  437. 29. Creating CNN model in Python/3. CNN model in Python - Training and results.srt 6KB
  438. 7. Linear Regression/7. Multiple Linear Regression.srt 6KB
  439. 30. Creating CNN model in R/3. Creating Model Architecture.srt 6KB
  440. 28. CNN - Basics/5. Channels.srt 6KB
  441. 6. Data Preprocessing/21. Dummy variable creation in Python.srt 6KB
  442. 40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.srt 6KB
  443. 14. Simple Decision Trees/10. Test-Train split in Python.srt 6KB
  444. 22. Creating Support Vector Machine Model in Python/5. Test-Train Split.srt 6KB
  445. 8. Classification Models Data Preparation/12. Dummy variable creation in Python.srt 6KB
  446. 3. Setting up R Studio and R crash course/8. Creating Histograms in R.srt 6KB
  447. 26. ANN in Python/4. Normalization and Test-Train split.srt 6KB
  448. 6. Data Preprocessing/22. Dummy variable creation in R.srt 6KB
  449. 23. Creating Support Vector Machine Model in R/2. Test-Train Split.srt 6KB
  450. 6. Data Preprocessing/19. Non-usable variables.srt 6KB
  451. 10. Logistic Regression/6. Training multiple predictor Logistic model in Python.srt 6KB
  452. 13. Comparing results from 3 models/2. Summary of the three models.srt 6KB
  453. 7. Linear Regression/9. Interpreting results of Categorical variables.srt 6KB
  454. 10. Logistic Regression/4. Result of Simple Logistic Regression.srt 6KB
  455. 14. Simple Decision Trees/5. Importing the Data set into Python.srt 6KB
  456. 22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.srt 6KB
  457. 28. CNN - Basics/6. PoolingLayer.srt 6KB
  458. 14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.srt 6KB
  459. 12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.srt 6KB
  460. 6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.srt 6KB
  461. 29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.srt 6KB
  462. 29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.srt 6KB
  463. 32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.srt 6KB
  464. 9. The Three classification models/2. Why can't we use Linear Regression.srt 5KB
  465. 34. Transfer Learning Basics/5. Transfer Learning.srt 5KB
  466. 18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.srt 5KB
  467. 14. Simple Decision Trees/8. Dummy Variable creation in Python.srt 5KB
  468. 19. Maximum Margin Classifier/2. The Concept of a Hyperplane.srt 5KB
  469. 14. Simple Decision Trees/15. Plotting decision tree in Python.srt 5KB
  470. 8. Classification Models Data Preparation/2. Data Import in Python.srt 5KB
  471. 4. Basics of Statistics/5. Measures of Dispersion.srt 5KB
  472. 40. Time Series - ARIMA model/2. ARIMA model - Basics.srt 5KB
  473. 6. Data Preprocessing/9. Outlier Treatment.srt 5KB
  474. 4. Basics of Statistics/1. Types of Data.srt 5KB
  475. 39. Time Series - Implementation in Python/6. Moving Average model -Basics.srt 5KB
  476. 28. CNN - Basics/3. Padding.srt 5KB
  477. 10. Logistic Regression/8. Confusion Matrix.srt 5KB
  478. 6. Data Preprocessing/11. Outlier Treatment in R.srt 5KB
  479. 8. Classification Models Data Preparation/8. Missing Value Imputation in Python.srt 5KB
  480. 8. Classification Models Data Preparation/7. Outlier Treatment in R.srt 5KB
  481. 6. Data Preprocessing/13. Missing Value Imputation in Python.srt 5KB
  482. 24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.srt 5KB
  483. 17. Ensemble technique 2 - Random Forests/4. Random Forest in R.srt 5KB
  484. 7. Linear Regression/16. Regression models other than OLS.srt 5KB
  485. 14. Simple Decision Trees/14. Evaluating model performance in Python.srt 5KB
  486. 3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.srt 5KB
  487. 34. Transfer Learning Basics/1. ILSVRC.srt 5KB
  488. 17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.srt 5KB
  489. 38. Time Series - Important Concepts/2. Random Walk.srt 5KB
  490. 14. Simple Decision Trees/16. Pruning a tree.srt 5KB
  491. 1. Introduction/1. Introduction.srt 4KB
  492. 22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.srt 4KB
  493. 2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.srt 4KB
  494. 18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.srt 4KB
  495. 8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.srt 4KB
  496. 14. Simple Decision Trees/12. Creating Decision tree in Python.srt 4KB
  497. 37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.srt 4KB
  498. 14. Simple Decision Trees/9. Dependent- Independent Data split in Python.srt 4KB
  499. 22. Creating Support Vector Machine Model in Python/4. X-y Split.srt 4KB
  500. 6. Data Preprocessing/12. Missing Value Imputation.srt 4KB
  501. 10. Logistic Regression/3. Training a Simple Logistic model in R.srt 4KB
  502. 30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.srt 4KB
  503. 6. Data Preprocessing/1. Gathering Business Knowledge.srt 4KB
  504. 26. ANN in Python/2. Installing Tensorflow and Keras.srt 4KB
  505. 8. Classification Models Data Preparation/9. Missing Value imputation in R.srt 4KB
  506. 6. Data Preprocessing/14. Missing Value imputation in R.srt 4KB
  507. 6. Data Preprocessing/6. Univariate analysis and EDD.srt 4KB
  508. 6. Data Preprocessing/15. Seasonality in Data.srt 4KB
  509. 9. The Three classification models/1. Three Classifiers and the problem statement.srt 4KB
  510. 6. Data Preprocessing/2. Data Exploration.srt 4KB
  511. 26. ANN in Python/1. Keras and Tensorflow.srt 4KB
  512. 2. Setting up Python and Jupyter Notebook/2. This is a milestone!.srt 4KB
  513. 14. Simple Decision Trees/7. Missing value treatment in Python.srt 4KB
  514. 39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.srt 4KB
  515. 14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.srt 4KB
  516. 19. Maximum Margin Classifier/3. Maximum Margin Classifier.srt 3KB
  517. 3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.srt 3KB
  518. 14. Simple Decision Trees/4. The Data set for this part.srt 3KB
  519. 22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.srt 3KB
  520. 34. Transfer Learning Basics/4. GoogLeNet.srt 3KB
  521. 4. Basics of Statistics/2. Types of Statistics.srt 3KB
  522. 32. Project Creating CNN model from scratch/3. Project in R - Training.srt 3KB
  523. 30. Creating CNN model in R/4. Compiling and training.srt 3KB
  524. 28. CNN - Basics/2. Stride.srt 3KB
  525. 27. ANN in R/1. Installing Keras and Tensorflow.srt 3KB
  526. 10. Logistic Regression/5. Logistic with multiple predictors.srt 3KB
  527. 36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.srt 3KB
  528. 31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.srt 3KB
  529. 7. Linear Regression/22. Heteroscedasticity.srt 3KB
  530. 8. Classification Models Data Preparation/3. Importing the dataset into R.srt 3KB
  531. 6. Data Preprocessing/5. Importing the dataset into R.srt 3KB
  532. 37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.srt 3KB
  533. 10. Logistic Regression/11. Evaluating model performance in Python.srt 3KB
  534. 2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.srt 3KB
  535. 19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.srt 3KB
  536. 32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.srt 3KB
  537. 11. Linear Discriminant Analysis (LDA)/2. LDA in Python.srt 3KB
  538. 38. Time Series - Important Concepts/1. White Noise.srt 3KB
  539. 32. Project Creating CNN model from scratch/4. Project in R - Model Performance.srt 3KB
  540. 36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.srt 3KB
  541. 30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.srt 2KB
  542. 36. Time Series Analysis and Forecasting/1. Introduction.srt 2KB
  543. 37. Time Series - Preprocessing in Python/10. Exponential Smoothing.srt 2KB
  544. 26. ANN in Python/5. Different ways to create ANN using Keras.srt 2KB
  545. 34. Transfer Learning Basics/3. VGG16NET.srt 2KB
  546. 15. Simple Classification Tree/2. The Data set for Classification problem.srt 2KB
  547. 22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.srt 2KB
  548. 22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.srt 2KB
  549. 34. Transfer Learning Basics/2. LeNET.srt 2KB
  550. 19. Maximum Margin Classifier/1. Content flow.srt 2KB
  551. 42. Bonus Section/1. The final milestone!.srt 2KB
  552. 41. Time Series - SARIMA model/3. Stationary time Series.srt 2KB
  553. 15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.srt 2KB
  554. 7. Linear Regression/1. The Problem Statement.srt 2KB
  555. 20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.srt 2KB
  556. 42. Bonus Section/2. Congratulations & About your certificate.html 2KB
  557. 22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.srt 810B
  558. 23. Creating Support Vector Machine Model in R/3. More about test-train split.html 559B
  559. 1. Introduction/2. Course Resources.html 370B
  560. 31. Project Creating CNN model from scratch in Python/2. Data for the project.html 232B
  561. 0. Websites you may like/[FCS Forum].url 133B
  562. 6. Data Preprocessing/26. Quiz.html 130B
  563. 0. Websites you may like/[FreeCourseSite.com].url 127B
  564. 0. Websites you may like/[CourseClub.ME].url 122B