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

  • 收录时间:2022-01-22 12:05:01
  • 文件大小:13GB
  • 下载次数:1
  • 最近下载:2022-01-22 12:05:01
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文件列表

  1. 27 ANN in R/008 Saving - Restoring Models and Using Callbacks.mp4 216MB
  2. 37 Time Series - Preprocessing in Python/003 Time Series - Visualization in Python.mp4 165MB
  3. 18 Ensemble technique 3 - Boosting/007 XGBoosting in R.mp4 161MB
  4. 26 ANN in Python/009 Building Neural Network for Regression Problem.mp4 156MB
  5. 26 ANN in Python/011 Saving - Restoring Models and Using Callbacks.mp4 152MB
  6. 23 Creating Support Vector Machine Model in R/004 Classification SVM model using Linear Kernel.mp4 139MB
  7. 27 ANN in R/006 Building Regression Model with Functional API.mp4 131MB
  8. 27 ANN in R/003 Building,Compiling and Training.mp4 131MB
  9. 34 Transfer Learning _ Basics/006 Project - Transfer Learning - VGG16.mp4 129MB
  10. 07 Linear Regression/020 Ridge regression and Lasso in Python.mp4 129MB
  11. 25 Neural Networks - Stacking cells to create network/003 Back Propagation.mp4 122MB
  12. 38 Time Series - Important Concepts/005 Differencing in Python.mp4 113MB
  13. 37 Time Series - Preprocessing in Python/005 Time Series - Feature Engineering in Python.mp4 113MB
  14. 27 ANN in R/002 Data Normalization and Test-Train Split.mp4 112MB
  15. 05 Introduction to Machine Learning/001 Introduction to Machine Learning.mp4 109MB
  16. 37 Time Series - Preprocessing in Python/001 Data Loading in Python.mp4 109MB
  17. 23 Creating Support Vector Machine Model in R/008 SVM based Regression Model in R.mp4 106MB
  18. 07 Linear Regression/021 Ridge regression and Lasso in R.mp4 103MB
  19. 14 Simple Decision Trees/013 Building a Regression Tree in R.mp4 103MB
  20. 35 Transfer Learning in R/001 Project - Transfer Learning - VGG16 (Implementation).mp4 102MB
  21. 37 Time Series - Preprocessing in Python/007 Time Series - Upsampling and Downsampling in Python.mp4 101MB
  22. 06 Data Preprocessing/016 Bi-variate analysis and Variable transformation.mp4 100MB
  23. 27 ANN in R/004 Evaluating and Predicting.mp4 99MB
  24. 06 Data Preprocessing/008 EDD in R.mp4 97MB
  25. 03 Setting up R Studio and R crash course/007 Creating Barplots in R.mp4 97MB
  26. 07 Linear Regression/003 Assessing accuracy of predicted coefficients.mp4 92MB
  27. 26 ANN in Python/010 Using Functional API for complex architectures.mp4 92MB
  28. 18 Ensemble technique 3 - Boosting/005 AdaBoosting in R.mp4 89MB
  29. 32 Project _ Creating CNN model from scratch/001 Project in R - Data Preprocessing.mp4 88MB
  30. 24 Introduction - Deep Learning/004 Python - Creating Perceptron model.mp4 87MB
  31. 15 Simple Classification Tree/005 Building a classification Tree in R.mp4 85MB
  32. 27 ANN in R/005 ANN with NeuralNets Package.mp4 84MB
  33. 23 Creating Support Vector Machine Model in R/006 Polynomial Kernel with Hyperparameter Tuning.mp4 83MB
  34. 06 Data Preprocessing/025 Correlation Matrix in R.mp4 83MB
  35. 03 Setting up R Studio and R crash course/003 Packages in R.mp4 83MB
  36. 15 Simple Classification Tree/004 Classification tree in Python _ Training.mp4 83MB
  37. 14 Simple Decision Trees/018 Pruning a Tree in R.mp4 82MB
  38. 26 ANN in Python/007 Compiling and Training the Neural Network model.mp4 82MB
  39. 17 Ensemble technique 2 - Random Forests/003 Using Grid Search in Python.mp4 81MB
  40. 27 ANN in R/007 Complex Architectures using Functional API.mp4 80MB
  41. 26 ANN in Python/006 Building the Neural Network using Keras.mp4 79MB
  42. 07 Linear Regression/017 Subset selection techniques.mp4 79MB
  43. 08 Classification Models_ Data Preparation/001 The Data and the Data Dictionary.mp4 79MB
  44. 08 Classification Models_ Data Preparation/004 EDD in Python.mp4 78MB
  45. 16 Ensemble technique 1 - Bagging/002 Ensemble technique 1 - Bagging in Python.mp4 77MB
  46. 07 Linear Regression/015 Test-Train Split in R.mp4 76MB
  47. 12 K-Nearest Neighbors classifier/004 K-Nearest Neighbors classifier.mp4 75MB
  48. 18 Ensemble technique 3 - Boosting/006 Ensemble technique 3c - XGBoost in Python.mp4 75MB
  49. 40 Time Series - ARIMA model/003 ARIMA model in Python.mp4 74MB
  50. 11 Linear Discriminant Analysis (LDA)/003 Linear Discriminant Analysis in R.mp4 74MB
  51. 12 K-Nearest Neighbors classifier/003 Test-Train Split in R.mp4 74MB
  52. 14 Simple Decision Trees/017 Pruning a tree in Python.mp4 74MB
  53. 31 Project _ Creating CNN model from scratch in Python/003 Project - Data Preprocessing in Python.mp4 72MB
  54. 30 Creating CNN model in R/003 Creating Model Architecture.mp4 72MB
  55. 06 Data Preprocessing/023 Correlation Analysis.mp4 72MB
  56. 06 Data Preprocessing/010 Outlier Treatment in Python.mp4 70MB
  57. 26 ANN in Python/008 Evaluating performance and Predicting using Keras.mp4 70MB
  58. 07 Linear Regression/010 Multiple Linear Regression in Python.mp4 70MB
  59. 06 Data Preprocessing/003 The Dataset and the Data Dictionary.mp4 69MB
  60. 18 Ensemble technique 3 - Boosting/003 Gradient Boosting in R.mp4 69MB
  61. 30 Creating CNN model in R/005 Model Performance.mp4 68MB
  62. 28 CNN - Basics/005 Channels.mp4 68MB
  63. 22 Creating Support Vector Machine Model in Python/007 SVM based Regression Model in Python.mp4 68MB
  64. 30 Creating CNN model in R/002 Data Preprocessing.mp4 67MB
  65. 08 Classification Models_ Data Preparation/005 EDD in R.mp4 67MB
  66. 41 Time Series - SARIMA model/002 SARIMA model in Python.mp4 66MB
  67. 31 Project _ Creating CNN model from scratch in Python/004 Project - Training CNN model in Python.mp4 66MB
  68. 04 Basics of Statistics/003 Describing data Graphically.mp4 65MB
  69. 02 Setting up Python and Jupyter Notebook/003 Opening Jupyter Notebook.mp4 65MB
  70. 12 K-Nearest Neighbors classifier/007 K-Nearest Neighbors in R.mp4 65MB
  71. 02 Setting up Python and Jupyter Notebook/006 Strings in Python_ Python Basics.mp4 64MB
  72. 22 Creating Support Vector Machine Model in Python/011 SVM Based classification model.mp4 64MB
  73. 35 Transfer Learning in R/002 Project - Transfer Learning - VGG16 (Performance).mp4 64MB
  74. 37 Time Series - Preprocessing in Python/002 Time Series - Visualization Basics.mp4 64MB
  75. 07 Linear Regression/018 Subset selection in R.mp4 64MB
  76. 07 Linear Regression/005 Simple Linear Regression in Python.mp4 63MB
  77. 36 Time Series Analysis and Forecasting/005 Time Series - Basic Notations.mp4 62MB
  78. 07 Linear Regression/011 Multiple Linear Regression in R.mp4 62MB
  79. 25 Neural Networks - Stacking cells to create network/004 Some Important Concepts.mp4 62MB
  80. 06 Data Preprocessing/007 EDD in Python.mp4 62MB
  81. 26 ANN in Python/012 Hyperparameter Tuning.mp4 61MB
  82. 23 Creating Support Vector Machine Model in R/005 Hyperparameter Tuning for Linear Kernel.mp4 60MB
  83. 25 Neural Networks - Stacking cells to create network/002 Gradient Descent.mp4 60MB
  84. 02 Setting up Python and Jupyter Notebook/007 Lists, Tuples and Directories_ Python Basics.mp4 60MB
  85. 03 Setting up R Studio and R crash course/006 Inputting data part 3_ Importing from CSV or Text files.mp4 60MB
  86. 38 Time Series - Important Concepts/003 Decomposing Time Series in Python.mp4 60MB
  87. 37 Time Series - Preprocessing in Python/004 Time Series - Feature Engineering Basics.mp4 59MB
  88. 16 Ensemble technique 1 - Bagging/003 Bagging in R.mp4 59MB
  89. 29 Creating CNN model in Python/004 Comparison - Pooling vs Without Pooling in Python.mp4 58MB
  90. 22 Creating Support Vector Machine Model in Python/012 Hyper Parameter Tuning.mp4 58MB
  91. 39 Time Series - Implementation in Python/001 Test Train Split in Python.mp4 57MB
  92. 23 Creating Support Vector Machine Model in R/007 Radial Kernel with Hyperparameter Tuning.mp4 57MB
  93. 39 Time Series - Implementation in Python/007 Moving Average model in Python.mp4 57MB
  94. 32 Project _ Creating CNN model from scratch/005 Project in R - Data Augmentation.mp4 56MB
  95. 26 ANN in Python/003 Dataset for classification.mp4 56MB
  96. 20 Support Vector Classifier/001 Support Vector classifiers.mp4 56MB
  97. 07 Linear Regression/008 The F - statistic.mp4 56MB
  98. 10 Logistic Regression/012 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 56MB
  99. 06 Data Preprocessing/018 Variable transformation in R.mp4 55MB
  100. 06 Data Preprocessing/024 Correlation Analysis in Python.mp4 55MB
  101. 29 Creating CNN model in Python/003 CNN model in Python - Training and results.mp4 55MB
  102. 23 Creating Support Vector Machine Model in R/001 Importing Data into R.mp4 54MB
  103. 39 Time Series - Implementation in Python/004 Auto Regression Model creation in Python.mp4 53MB
  104. 33 Project _ Data Augmentation for avoiding overfitting/002 Project - Data Augmentation Training and Results.mp4 53MB
  105. 28 CNN - Basics/004 Filters and Feature maps.mp4 53MB
  106. 10 Logistic Regression/009 Creating Confusion Matrix in Python.mp4 51MB
  107. 28 CNN - Basics/001 CNN Introduction.mp4 51MB
  108. 23 Creating Support Vector Machine Model in R/002 Test-Train Split.mp4 50MB
  109. 39 Time Series - Implementation in Python/005 Auto Regression with Walk Forward validation in Python.mp4 50MB
  110. 31 Project _ Creating CNN model from scratch in Python/001 Project - Introduction.mp4 49MB
  111. 10 Logistic Regression/002 Training a Simple Logistic Model in Python.mp4 48MB
  112. 08 Classification Models_ Data Preparation/006 Outlier treatment in Python.mp4 47MB
  113. 02 Setting up Python and Jupyter Notebook/009 Working with Pandas Library of Python.mp4 47MB
  114. 28 CNN - Basics/006 PoolingLayer.mp4 47MB
  115. 17 Ensemble technique 2 - Random Forests/002 Ensemble technique 2 - Random Forests in Python.mp4 47MB
  116. 32 Project _ Creating CNN model from scratch/002 CNN Project in R - Structure and Compile.mp4 46MB
  117. 15 Simple Classification Tree/003 Classification tree in Python _ Preprocessing.mp4 45MB
  118. 22 Creating Support Vector Machine Model in Python/009 Classification model - Preprocessing.mp4 45MB
  119. 25 Neural Networks - Stacking cells to create network/005 Hyperparameter.mp4 45MB
  120. 07 Linear Regression/014 Test train split in Python.mp4 45MB
  121. 24 Introduction - Deep Learning/002 Perceptron.mp4 45MB
  122. 30 Creating CNN model in R/006 Comparison - Pooling vs Without Pooling in R.mp4 45MB
  123. 08 Classification Models_ Data Preparation/013 Dummy variable creation in R.mp4 44MB
  124. 26 ANN in Python/004 Normalization and Test-Train split.mp4 44MB
  125. 06 Data Preprocessing/017 Variable transformation and deletion in Python.mp4 44MB
  126. 06 Data Preprocessing/022 Dummy variable creation in R.mp4 44MB
  127. 14 Simple Decision Trees/011 Splitting Data into Test and Train Set in R.mp4 44MB
  128. 02 Setting up Python and Jupyter Notebook/008 Working with Numpy Library of Python.mp4 44MB
  129. 14 Simple Decision Trees/002 Understanding a Regression Tree.mp4 44MB
  130. 14 Simple Decision Trees/006 Importing the Data set into R.mp4 44MB
  131. 07 Linear Regression/004 Assessing Model Accuracy_ RSE and R squared.mp4 44MB
  132. 07 Linear Regression/002 Basic Equations and Ordinary Least Squares (OLS) method.mp4 43MB
  133. 39 Time Series - Implementation in Python/002 Naive (Persistence) model in Python.mp4 43MB
  134. 29 Creating CNN model in Python/002 CNN model in Python - structure and Compile.mp4 43MB
  135. 14 Simple Decision Trees/001 Basics of Decision Trees.mp4 43MB
  136. 12 K-Nearest Neighbors classifier/006 K-Nearest Neighbors in Python_ Part 2.mp4 42MB
  137. 03 Setting up R Studio and R crash course/008 Creating Histograms in R.mp4 42MB
  138. 07 Linear Regression/012 Test-train split.mp4 42MB
  139. 13 Comparing results from 3 models/001 Understanding the results of classification models.mp4 42MB
  140. 33 Project _ Data Augmentation for avoiding overfitting/001 Project - Data Augmentation Preprocessing.mp4 41MB
  141. 40 Time Series - ARIMA model/001 ACF and PACF.mp4 41MB
  142. 11 Linear Discriminant Analysis (LDA)/001 Linear Discriminant Analysis.mp4 41MB
  143. 02 Setting up Python and Jupyter Notebook/004 Introduction to Jupyter.mp4 41MB
  144. 07 Linear Regression/006 Simple Linear Regression in R.mp4 41MB
  145. 03 Setting up R Studio and R crash course/004 Inputting data part 1_ Inbuilt datasets of R.mp4 41MB
  146. 29 Creating CNN model in Python/001 CNN model in Python - Preprocessing.mp4 41MB
  147. 25 Neural Networks - Stacking cells to create network/001 Basic Terminologies.mp4 40MB
  148. 02 Setting up Python and Jupyter Notebook/010 Working with Seaborn Library of Python.mp4 40MB
  149. 21 Support Vector Machines/001 Kernel Based Support Vector Machines.mp4 40MB
  150. 18 Ensemble technique 3 - Boosting/002 Ensemble technique 3a - Boosting in Python.mp4 40MB
  151. 05 Introduction to Machine Learning/002 Building a Machine Learning Model.mp4 39MB
  152. 12 K-Nearest Neighbors classifier/001 Test-Train Split.mp4 39MB
  153. 41 Time Series - SARIMA model/001 SARIMA model.mp4 39MB
  154. 03 Setting up R Studio and R crash course/002 Basics of R and R studio.mp4 39MB
  155. 37 Time Series - Preprocessing in Python/009 Moving Average.mp4 39MB
  156. 04 Basics of Statistics/004 Measures of Centers.mp4 39MB
  157. 22 Creating Support Vector Machine Model in Python/006 Standardizing the data.mp4 38MB
  158. 08 Classification Models_ Data Preparation/011 Variable transformation in R.mp4 38MB
  159. 14 Simple Decision Trees/004 The Data set for this part.mp4 37MB
  160. 12 K-Nearest Neighbors classifier/005 K-Nearest Neighbors in Python_ Part 1.mp4 37MB
  161. 22 Creating Support Vector Machine Model in Python/014 Radial Kernel with Hyperparameter Tuning.mp4 37MB
  162. 22 Creating Support Vector Machine Model in Python/002 The Data set for the Regression problem.mp4 37MB
  163. 06 Data Preprocessing/020 Dummy variable creation_ Handling qualitative data.mp4 37MB
  164. 03 Setting up R Studio and R crash course/001 Installing R and R studio.mp4 36MB
  165. 10 Logistic Regression/010 Evaluating performance of model.mp4 35MB
  166. 24 Introduction - Deep Learning/003 Activation Functions.mp4 35MB
  167. 36 Time Series Analysis and Forecasting/004 Forecasting model creation - Steps 1 (Goal).mp4 34MB
  168. 07 Linear Regression/007 Multiple Linear Regression.mp4 34MB
  169. 07 Linear Regression/019 Shrinkage methods_ Ridge and Lasso.mp4 33MB
  170. 12 K-Nearest Neighbors classifier/002 Test-Train Split in Python.mp4 33MB
  171. 10 Logistic Regression/001 Logistic Regression.mp4 33MB
  172. 38 Time Series - Important Concepts/004 Differencing.mp4 32MB
  173. 30 Creating CNN model in R/004 Compiling and training.mp4 32MB
  174. 40 Time Series - ARIMA model/004 ARIMA model with Walk Forward Validation in Python.mp4 32MB
  175. 28 CNN - Basics/003 Padding.mp4 32MB
  176. 06 Data Preprocessing/011 Outlier Treatment in R.mp4 31MB
  177. 17 Ensemble technique 2 - Random Forests/004 Random Forest in R.mp4 31MB
  178. 18 Ensemble technique 3 - Boosting/001 Boosting.mp4 31MB
  179. 18 Ensemble technique 3 - Boosting/004 Ensemble technique 3b - AdaBoost in Python.mp4 31MB
  180. 34 Transfer Learning _ Basics/005 Transfer Learning.mp4 30MB
  181. 19 Maximum Margin Classifier/002 The Concept of a Hyperplane.mp4 29MB
  182. 01 Introduction/001 Introduction.mp4 29MB
  183. 08 Classification Models_ Data Preparation/010 Variable transformation and Deletion in Python.mp4 29MB
  184. 24 Introduction - Deep Learning/001 Introduction to Neural Networks and Course flow.mp4 29MB
  185. 15 Simple Classification Tree/001 Classification tree.mp4 28MB
  186. 16 Ensemble technique 1 - Bagging/001 Ensemble technique 1 - Bagging.mp4 28MB
  187. 06 Data Preprocessing/004 Importing Data in Python.mp4 28MB
  188. 10 Logistic Regression/004 Result of Simple Logistic Regression.mp4 27MB
  189. 06 Data Preprocessing/021 Dummy variable creation in Python.mp4 27MB
  190. 08 Classification Models_ Data Preparation/012 Dummy variable creation in Python.mp4 26MB
  191. 10 Logistic Regression/006 Training multiple predictor Logistic model in Python.mp4 26MB
  192. 06 Data Preprocessing/014 Missing Value imputation in R.mp4 26MB
  193. 36 Time Series Analysis and Forecasting/002 Time Series Forecasting - Use cases.mp4 26MB
  194. 14 Simple Decision Trees/005 Importing the Data set into Python.mp4 26MB
  195. 22 Creating Support Vector Machine Model in Python/003 Importing data for regression model.mp4 26MB
  196. 10 Logistic Regression/003 Training a Simple Logistic model in R.mp4 26MB
  197. 03 Setting up R Studio and R crash course/005 Inputting data part 2_ Manual data entry.mp4 26MB
  198. 08 Classification Models_ Data Preparation/007 Outlier Treatment in R.mp4 25MB
  199. 07 Linear Regression/013 Bias Variance trade-off.mp4 25MB
  200. 06 Data Preprocessing/012 Missing Value Imputation.mp4 25MB
  201. 14 Simple Decision Trees/008 Dummy Variable creation in Python.mp4 25MB
  202. 14 Simple Decision Trees/010 Test-Train split in Python.mp4 25MB
  203. 22 Creating Support Vector Machine Model in Python/005 Test-Train Split.mp4 25MB
  204. 32 Project _ Creating CNN model from scratch/003 Project in R - Training.mp4 25MB
  205. 06 Data Preprocessing/009 Outlier Treatment.mp4 24MB
  206. 06 Data Preprocessing/006 Univariate analysis and EDD.mp4 24MB
  207. 39 Time Series - Implementation in Python/006 Moving Average model -Basics.mp4 24MB
  208. 32 Project _ Creating CNN model from scratch/006 Project in R - Validation Performance.mp4 24MB
  209. 06 Data Preprocessing/013 Missing Value Imputation in Python.mp4 23MB
  210. 32 Project _ Creating CNN model from scratch/004 Project in R - Model Performance.mp4 23MB
  211. 22 Creating Support Vector Machine Model in Python/013 Polynomial Kernel with Hyperparameter Tuning.mp4 23MB
  212. 04 Basics of Statistics/005 Measures of Dispersion.mp4 23MB
  213. 27 ANN in R/001 Installing Keras and Tensorflow.mp4 23MB
  214. 08 Classification Models_ Data Preparation/008 Missing Value Imputation in Python.mp4 23MB
  215. 07 Linear Regression/009 Interpreting results of Categorical variables.mp4 23MB
  216. 19 Maximum Margin Classifier/003 Maximum Margin Classifier.mp4 22MB
  217. 06 Data Preprocessing/001 Gathering Business Knowledge.mp4 22MB
  218. 13 Comparing results from 3 models/002 Summary of the three models.mp4 22MB
  219. 08 Classification Models_ Data Preparation/002 Data Import in Python.mp4 22MB
  220. 04 Basics of Statistics/001 Types of Data.mp4 22MB
  221. 14 Simple Decision Trees/015 Plotting decision tree in Python.mp4 21MB
  222. 34 Transfer Learning _ Basics/004 GoogLeNet.mp4 21MB
  223. 40 Time Series - ARIMA model/002 ARIMA model - Basics.mp4 21MB
  224. 38 Time Series - Important Concepts/002 Random Walk.mp4 21MB
  225. 10 Logistic Regression/008 Confusion Matrix.mp4 21MB
  226. 31 Project _ Creating CNN model from scratch in Python/005 Project in Python - model results.mp4 21MB
  227. 34 Transfer Learning _ Basics/001 ILSVRC.mp4 21MB
  228. 02 Setting up Python and Jupyter Notebook/002 This is a milestone!.mp4 21MB
  229. 06 Data Preprocessing/002 Data Exploration.mp4 20MB
  230. 09 The Three classification models/001 Three Classifiers and the problem statement.mp4 20MB
  231. 06 Data Preprocessing/019 Non-usable variables.mp4 20MB
  232. 26 ANN in Python/002 Installing Tensorflow and Keras.mp4 20MB
  233. 08 Classification Models_ Data Preparation/009 Missing Value imputation in R.mp4 19MB
  234. 15 Simple Classification Tree/002 The Data set for Classification problem.mp4 19MB
  235. 22 Creating Support Vector Machine Model in Python/008 The Data set for the Classification problem.mp4 19MB
  236. 14 Simple Decision Trees/016 Pruning a tree.mp4 18MB
  237. 17 Ensemble technique 2 - Random Forests/001 Ensemble technique 2 - Random Forests.mp4 18MB
  238. 14 Simple Decision Trees/007 Missing value treatment in Python.mp4 18MB
  239. 14 Simple Decision Trees/012 Creating Decision tree in Python.mp4 18MB
  240. 06 Data Preprocessing/015 Seasonality in Data.mp4 17MB
  241. 37 Time Series - Preprocessing in Python/006 Time Series - Upsampling and Downsampling.mp4 17MB
  242. 09 The Three classification models/002 Why can't we use Linear Regression_.mp4 17MB
  243. 39 Time Series - Implementation in Python/003 Auto Regression Model - Basics.mp4 17MB
  244. 28 CNN - Basics/002 Stride.mp4 17MB
  245. 07 Linear Regression/016 Regression models other than OLS.mp4 17MB
  246. 14 Simple Decision Trees/014 Evaluating model performance in Python.mp4 16MB
  247. 02 Setting up Python and Jupyter Notebook/001 Installing Python and Anaconda.mp4 16MB
  248. 10 Logistic Regression/007 Training multiple predictor Logistic model in R.mp4 16MB
  249. 22 Creating Support Vector Machine Model in Python/004 X-y Split.mp4 15MB
  250. 14 Simple Decision Trees/009 Dependent- Independent Data split in Python.mp4 15MB
  251. 26 ANN in Python/001 Keras and Tensorflow.mp4 15MB
  252. 37 Time Series - Preprocessing in Python/008 Time Series - Power Transformation.mp4 15MB
  253. 07 Linear Regression/022 Heteroscedasticity.mp4 14MB
  254. 14 Simple Decision Trees/003 The stopping criteria for controlling tree growth.mp4 14MB
  255. 08 Classification Models_ Data Preparation/003 Importing the dataset into R.mp4 13MB
  256. 06 Data Preprocessing/005 Importing the dataset into R.mp4 13MB
  257. 02 Setting up Python and Jupyter Notebook/005 Arithmetic operators in Python_ Python Basics.mp4 13MB
  258. 36 Time Series Analysis and Forecasting/001 Introduction.mp4 12MB
  259. 42 Bonus Section/001 The final milestone!.mp4 12MB
  260. 11 Linear Discriminant Analysis (LDA)/002 LDA in Python.mp4 11MB
  261. 38 Time Series - Important Concepts/001 White Noise.mp4 11MB
  262. 04 Basics of Statistics/002 Types of Statistics.mp4 11MB
  263. 26 ANN in Python/005 Different ways to create ANN using Keras.mp4 11MB
  264. 20 Support Vector Classifier/002 Limitations of Support Vector Classifiers.mp4 11MB
  265. 19 Maximum Margin Classifier/004 Limitations of Maximum Margin Classifier.mp4 11MB
  266. 34 Transfer Learning _ Basics/003 VGG16NET.mp4 10MB
  267. 36 Time Series Analysis and Forecasting/003 Forecasting model creation - Steps.mp4 10MB
  268. 22 Creating Support Vector Machine Model in Python/010 Classification model - Standardizing the data.mp4 10MB
  269. 07 Linear Regression/001 The Problem Statement.mp4 9MB
  270. 10 Logistic Regression/011 Evaluating model performance in Python.mp4 9MB
  271. 19 Maximum Margin Classifier/001 Content flow.mp4 9MB
  272. 10 Logistic Regression/005 Logistic with multiple predictors.mp4 9MB
  273. 37 Time Series - Preprocessing in Python/010 Exponential Smoothing.mp4 8MB
  274. 30 Creating CNN model in R/001 CNN on MNIST Fashion Dataset - Model Architecture.mp4 7MB
  275. 34 Transfer Learning _ Basics/002 LeNET.mp4 7MB
  276. 15 Simple Classification Tree/006 Advantages and Disadvantages of Decision Trees.mp4 7MB
  277. 41 Time Series - SARIMA model/003 Stationary time Series.mp4 6MB
  278. 22 Creating Support Vector Machine Model in Python/001 Regression and Classification Models.mp4 4MB
  279. 42 Bonus Section/002 Congratulations & About your certificate.html 2KB
  280. 23 Creating Support Vector Machine Model in R/003 More about test-train split.html 1KB
  281. 01 Introduction/002 Course Resources.html 1KB
  282. 31 Project _ Creating CNN model from scratch in Python/002 Data for the project.html 1KB
  283. [FreeCourseLab.com].url 126B