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Machine Learning & Deep Learning in Python & R

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Machine Learning & Deep Learning in Python & R

文件列表
  1. 27 ANN in R/008 Saving - Restoring Models and Using Callbacks.mp4216.03MB
  2. 02 Setting up Python and Jupyter Notebook/001 Installing Python and Anaconda.mp416.27MB
  3. 02 Setting up Python and Jupyter Notebook/002 This is a milestone!.mp420.66MB
  4. 02 Setting up Python and Jupyter Notebook/003 Opening Jupyter Notebook.mp465.19MB
  5. 02 Setting up Python and Jupyter Notebook/004 Introduction to Jupyter.mp440.91MB
  6. 02 Setting up Python and Jupyter Notebook/005 Arithmetic operators in Python_ Python Basics.mp412.74MB
  7. 02 Setting up Python and Jupyter Notebook/006 Strings in Python_ Python Basics.mp464.43MB
  8. 02 Setting up Python and Jupyter Notebook/007 Lists, Tuples and Directories_ Python Basics.mp460.32MB
  9. 02 Setting up Python and Jupyter Notebook/008 Working with Numpy Library of Python.mp443.87MB
  10. 02 Setting up Python and Jupyter Notebook/009 Working with Pandas Library of Python.mp446.88MB
  11. 02 Setting up Python and Jupyter Notebook/010 Working with Seaborn Library of Python.mp440.36MB
  12. 03 Setting up R Studio and R crash course/001 Installing R and R studio.mp435.71MB
  13. 03 Setting up R Studio and R crash course/002 Basics of R and R studio.mp438.84MB
  14. 03 Setting up R Studio and R crash course/003 Packages in R.mp482.94MB
  15. 03 Setting up R Studio and R crash course/004 Inputting data part 1_ Inbuilt datasets of R.mp440.74MB
  16. 03 Setting up R Studio and R crash course/005 Inputting data part 2_ Manual data entry.mp425.52MB
  17. 03 Setting up R Studio and R crash course/006 Inputting data part 3_ Importing from CSV or Text files.mp460.1MB
  18. 03 Setting up R Studio and R crash course/007 Creating Barplots in R.mp496.73MB
  19. 03 Setting up R Studio and R crash course/008 Creating Histograms in R.mp442.02MB
  20. 04 Basics of Statistics/001 Types of Data.mp421.76MB
  21. 04 Basics of Statistics/002 Types of Statistics.mp410.93MB
  22. 04 Basics of Statistics/003 Describing data Graphically.mp465.39MB
  23. 04 Basics of Statistics/004 Measures of Centers.mp438.57MB
  24. 04 Basics of Statistics/005 Measures of Dispersion.mp422.85MB
  25. 05 Introduction to Machine Learning/001 Introduction to Machine Learning.mp4109.17MB
  26. 05 Introduction to Machine Learning/002 Building a Machine Learning Model.mp439.48MB
  27. 06 Data Preprocessing/001 Gathering Business Knowledge.mp422.28MB
  28. 06 Data Preprocessing/002 Data Exploration.mp420.5MB
  29. 06 Data Preprocessing/003 The Dataset and the Data Dictionary.mp469.28MB
  30. 06 Data Preprocessing/004 Importing Data in Python.mp427.83MB
  31. 06 Data Preprocessing/005 Importing the dataset into R.mp413.11MB
  32. 06 Data Preprocessing/006 Univariate analysis and EDD.mp424.18MB
  33. 06 Data Preprocessing/007 EDD in Python.mp461.8MB
  34. 06 Data Preprocessing/008 EDD in R.mp496.98MB
  35. 06 Data Preprocessing/009 Outlier Treatment.mp424.49MB
  36. 06 Data Preprocessing/010 Outlier Treatment in Python.mp470.25MB
  37. 06 Data Preprocessing/011 Outlier Treatment in R.mp430.74MB
  38. 06 Data Preprocessing/012 Missing Value Imputation.mp424.99MB
  39. 06 Data Preprocessing/013 Missing Value Imputation in Python.mp423.42MB
  40. 06 Data Preprocessing/014 Missing Value imputation in R.mp426MB
  41. 06 Data Preprocessing/015 Seasonality in Data.mp417.01MB
  42. 06 Data Preprocessing/016 Bi-variate analysis and Variable transformation.mp4100.39MB
  43. 06 Data Preprocessing/017 Variable transformation and deletion in Python.mp444.11MB
  44. 06 Data Preprocessing/018 Variable transformation in R.mp455.42MB
  45. 06 Data Preprocessing/019 Non-usable variables.mp420.24MB
  46. 06 Data Preprocessing/020 Dummy variable creation_ Handling qualitative data.mp436.8MB
  47. 06 Data Preprocessing/021 Dummy variable creation in Python.mp426.53MB
  48. 06 Data Preprocessing/022 Dummy variable creation in R.mp443.98MB
  49. 06 Data Preprocessing/023 Correlation Analysis.mp471.59MB
  50. 06 Data Preprocessing/024 Correlation Analysis in Python.mp455.3MB
  51. 06 Data Preprocessing/025 Correlation Matrix in R.mp483.13MB
  52. 07 Linear Regression/001 The Problem Statement.mp49.37MB
  53. 07 Linear Regression/002 Basic Equations and Ordinary Least Squares (OLS) method.mp443.37MB
  54. 07 Linear Regression/003 Assessing accuracy of predicted coefficients.mp492.11MB
  55. 07 Linear Regression/004 Assessing Model Accuracy_ RSE and R squared.mp443.59MB
  56. 07 Linear Regression/005 Simple Linear Regression in Python.mp463.43MB
  57. 07 Linear Regression/006 Simple Linear Regression in R.mp440.82MB
  58. 07 Linear Regression/007 Multiple Linear Regression.mp434.31MB
  59. 07 Linear Regression/008 The F - statistic.mp455.98MB
  60. 07 Linear Regression/009 Interpreting results of Categorical variables.mp422.5MB
  61. 07 Linear Regression/010 Multiple Linear Regression in Python.mp469.73MB
  62. 07 Linear Regression/011 Multiple Linear Regression in R.mp462.37MB
  63. 07 Linear Regression/012 Test-train split.mp441.88MB
  64. 07 Linear Regression/013 Bias Variance trade-off.mp425.09MB
  65. 07 Linear Regression/014 Test train split in Python.mp444.88MB
  66. 07 Linear Regression/015 Test-Train Split in R.mp475.6MB
  67. 07 Linear Regression/016 Regression models other than OLS.mp416.54MB
  68. 07 Linear Regression/017 Subset selection techniques.mp479.06MB
  69. 07 Linear Regression/018 Subset selection in R.mp463.53MB
  70. 07 Linear Regression/019 Shrinkage methods_ Ridge and Lasso.mp433.34MB
  71. 07 Linear Regression/020 Ridge regression and Lasso in Python.mp4128.84MB
  72. 07 Linear Regression/021 Ridge regression and Lasso in R.mp4103.43MB
  73. 07 Linear Regression/022 Heteroscedasticity.mp414.49MB
  74. 08 Classification Models_ Data Preparation/001 The Data and the Data Dictionary.mp479MB
  75. 08 Classification Models_ Data Preparation/002 Data Import in Python.mp422.06MB
  76. 08 Classification Models_ Data Preparation/003 Importing the dataset into R.mp413.46MB
  77. 08 Classification Models_ Data Preparation/004 EDD in Python.mp477.62MB
  78. 08 Classification Models_ Data Preparation/005 EDD in R.mp466.52MB
  79. 08 Classification Models_ Data Preparation/006 Outlier treatment in Python.mp447.32MB
  80. 08 Classification Models_ Data Preparation/007 Outlier Treatment in R.mp425.37MB
  81. 08 Classification Models_ Data Preparation/008 Missing Value Imputation in Python.mp422.56MB
  82. 08 Classification Models_ Data Preparation/009 Missing Value imputation in R.mp419.05MB
  83. 08 Classification Models_ Data Preparation/010 Variable transformation and Deletion in Python.mp429.25MB
  84. 08 Classification Models_ Data Preparation/011 Variable transformation in R.mp438.02MB
  85. 08 Classification Models_ Data Preparation/012 Dummy variable creation in Python.mp426.37MB
  86. 08 Classification Models_ Data Preparation/013 Dummy variable creation in R.mp444.35MB
  87. 09 The Three classification models/001 Three Classifiers and the problem statement.mp420.33MB
  88. 09 The Three classification models/002 Why can't we use Linear Regression_.mp416.93MB
  89. 10 Logistic Regression/001 Logistic Regression.mp432.92MB
  90. 10 Logistic Regression/002 Training a Simple Logistic Model in Python.mp447.87MB
  91. 10 Logistic Regression/003 Training a Simple Logistic model in R.mp425.56MB
  92. 10 Logistic Regression/004 Result of Simple Logistic Regression.mp426.93MB
  93. 10 Logistic Regression/005 Logistic with multiple predictors.mp48.53MB
  94. 10 Logistic Regression/006 Training multiple predictor Logistic model in Python.mp426.25MB
  95. 10 Logistic Regression/007 Training multiple predictor Logistic model in R.mp415.78MB
  96. 10 Logistic Regression/008 Confusion Matrix.mp421.1MB
  97. 10 Logistic Regression/009 Creating Confusion Matrix in Python.mp451.25MB
  98. 10 Logistic Regression/010 Evaluating performance of model.mp435.16MB
  99. 10 Logistic Regression/011 Evaluating model performance in Python.mp49.01MB
  100. 10 Logistic Regression/012 Predicting probabilities, assigning classes and making Confusion Matrix in R.mp455.69MB
  101. 11 Linear Discriminant Analysis (LDA)/001 Linear Discriminant Analysis.mp440.95MB
  102. 11 Linear Discriminant Analysis (LDA)/002 LDA in Python.mp411.4MB
  103. 11 Linear Discriminant Analysis (LDA)/003 Linear Discriminant Analysis in R.mp474.35MB
  104. 12 K-Nearest Neighbors classifier/001 Test-Train Split.mp439.29MB
  105. 12 K-Nearest Neighbors classifier/002 Test-Train Split in Python.mp433.1MB
  106. 12 K-Nearest Neighbors classifier/003 Test-Train Split in R.mp474.23MB
  107. 12 K-Nearest Neighbors classifier/004 K-Nearest Neighbors classifier.mp475.42MB
  108. 12 K-Nearest Neighbors classifier/005 K-Nearest Neighbors in Python_ Part 1.mp437.23MB
  109. 12 K-Nearest Neighbors classifier/006 K-Nearest Neighbors in Python_ Part 2.mp442.35MB
  110. 12 K-Nearest Neighbors classifier/007 K-Nearest Neighbors in R.mp464.85MB
  111. 13 Comparing results from 3 models/001 Understanding the results of classification models.mp441.64MB
  112. 13 Comparing results from 3 models/002 Summary of the three models.mp422.21MB
  113. 14 Simple Decision Trees/001 Basics of Decision Trees.mp442.64MB
  114. 14 Simple Decision Trees/002 Understanding a Regression Tree.mp443.72MB
  115. 14 Simple Decision Trees/003 The stopping criteria for controlling tree growth.mp413.97MB
  116. 14 Simple Decision Trees/004 The Data set for this part.mp437.26MB
  117. 14 Simple Decision Trees/005 Importing the Data set into Python.mp425.84MB
  118. 14 Simple Decision Trees/006 Importing the Data set into R.mp443.7MB
  119. 14 Simple Decision Trees/007 Missing value treatment in Python.mp417.92MB
  120. 14 Simple Decision Trees/008 Dummy Variable creation in Python.mp424.94MB
  121. 14 Simple Decision Trees/009 Dependent- Independent Data split in Python.mp415.18MB
  122. 14 Simple Decision Trees/010 Test-Train split in Python.mp424.87MB
  123. 14 Simple Decision Trees/011 Splitting Data into Test and Train Set in R.mp443.97MB
  124. 14 Simple Decision Trees/012 Creating Decision tree in Python.mp417.87MB
  125. 14 Simple Decision Trees/013 Building a Regression Tree in R.mp4103.33MB
  126. 14 Simple Decision Trees/014 Evaluating model performance in Python.mp416.44MB
  127. 14 Simple Decision Trees/015 Plotting decision tree in Python.mp421.47MB
  128. 14 Simple Decision Trees/016 Pruning a tree.mp418.46MB
  129. 14 Simple Decision Trees/017 Pruning a tree in Python.mp473.5MB
  130. 14 Simple Decision Trees/018 Pruning a Tree in R.mp482.09MB
  131. 15 Simple Classification Tree/001 Classification tree.mp428.2MB
  132. 15 Simple Classification Tree/002 The Data set for Classification problem.mp418.57MB
  133. 15 Simple Classification Tree/003 Classification tree in Python _ Preprocessing.mp445.38MB
  134. 15 Simple Classification Tree/004 Classification tree in Python _ Training.mp482.71MB
  135. 15 Simple Classification Tree/005 Building a classification Tree in R.mp485.1MB
  136. 15 Simple Classification Tree/006 Advantages and Disadvantages of Decision Trees.mp46.86MB
  137. 16 Ensemble technique 1 - Bagging/001 Ensemble technique 1 - Bagging.mp428.14MB
  138. 16 Ensemble technique 1 - Bagging/002 Ensemble technique 1 - Bagging in Python.mp477.3MB
  139. 16 Ensemble technique 1 - Bagging/003 Bagging in R.mp458.95MB
  140. 17 Ensemble technique 2 - Random Forests/001 Ensemble technique 2 - Random Forests.mp418.19MB
  141. 17 Ensemble technique 2 - Random Forests/002 Ensemble technique 2 - Random Forests in Python.mp446.7MB
  142. 17 Ensemble technique 2 - Random Forests/003 Using Grid Search in Python.mp480.66MB
  143. 17 Ensemble technique 2 - Random Forests/004 Random Forest in R.mp430.72MB
  144. 18 Ensemble technique 3 - Boosting/001 Boosting.mp430.58MB
  145. 18 Ensemble technique 3 - Boosting/002 Ensemble technique 3a - Boosting in Python.mp439.87MB
  146. 18 Ensemble technique 3 - Boosting/003 Gradient Boosting in R.mp469.09MB
  147. 18 Ensemble technique 3 - Boosting/004 Ensemble technique 3b - AdaBoost in Python.mp430.53MB
  148. 18 Ensemble technique 3 - Boosting/005 AdaBoosting in R.mp488.67MB
  149. 18 Ensemble technique 3 - Boosting/006 Ensemble technique 3c - XGBoost in Python.mp475MB
  150. 18 Ensemble technique 3 - Boosting/007 XGBoosting in R.mp4161.3MB
  151. 19 Maximum Margin Classifier/001 Content flow.mp48.64MB
  152. 19 Maximum Margin Classifier/002 The Concept of a Hyperplane.mp429.42MB
  153. 19 Maximum Margin Classifier/003 Maximum Margin Classifier.mp422.48MB
  154. 19 Maximum Margin Classifier/004 Limitations of Maximum Margin Classifier.mp410.6MB
  155. 20 Support Vector Classifier/001 Support Vector classifiers.mp456.16MB
  156. 20 Support Vector Classifier/002 Limitations of Support Vector Classifiers.mp410.8MB
  157. 21 Support Vector Machines/001 Kernel Based Support Vector Machines.mp440.12MB
  158. 22 Creating Support Vector Machine Model in Python/001 Regression and Classification Models.mp44.03MB
  159. 22 Creating Support Vector Machine Model in Python/002 The Data set for the Regression problem.mp437.2MB
  160. 22 Creating Support Vector Machine Model in Python/003 Importing data for regression model.mp425.84MB
  161. 22 Creating Support Vector Machine Model in Python/004 X-y Split.mp415.18MB
  162. 22 Creating Support Vector Machine Model in Python/005 Test-Train Split.mp424.86MB
  163. 22 Creating Support Vector Machine Model in Python/006 Standardizing the data.mp438.41MB
  164. 22 Creating Support Vector Machine Model in Python/007 SVM based Regression Model in Python.mp467.63MB
  165. 22 Creating Support Vector Machine Model in Python/008 The Data set for the Classification problem.mp418.55MB
  166. 22 Creating Support Vector Machine Model in Python/009 Classification model - Preprocessing.mp445.37MB
  167. 22 Creating Support Vector Machine Model in Python/010 Classification model - Standardizing the data.mp49.72MB
  168. 22 Creating Support Vector Machine Model in Python/011 SVM Based classification model.mp464.12MB
  169. 22 Creating Support Vector Machine Model in Python/012 Hyper Parameter Tuning.mp457.74MB
  170. 22 Creating Support Vector Machine Model in Python/013 Polynomial Kernel with Hyperparameter Tuning.mp422.92MB
  171. 22 Creating Support Vector Machine Model in Python/014 Radial Kernel with Hyperparameter Tuning.mp437.21MB
  172. 23 Creating Support Vector Machine Model in R/001 Importing Data into R.mp453.67MB
  173. 23 Creating Support Vector Machine Model in R/002 Test-Train Split.mp450.48MB
  174. 23 Creating Support Vector Machine Model in R/004 Classification SVM model using Linear Kernel.mp4139.16MB
  175. 23 Creating Support Vector Machine Model in R/005 Hyperparameter Tuning for Linear Kernel.mp460.5MB
  176. 23 Creating Support Vector Machine Model in R/006 Polynomial Kernel with Hyperparameter Tuning.mp483.14MB
  177. 23 Creating Support Vector Machine Model in R/007 Radial Kernel with Hyperparameter Tuning.mp456.68MB
  178. 23 Creating Support Vector Machine Model in R/008 SVM based Regression Model in R.mp4106.12MB
  179. 24 Introduction - Deep Learning/001 Introduction to Neural Networks and Course flow.mp429.07MB
  180. 24 Introduction - Deep Learning/002 Perceptron.mp444.75MB
  181. 24 Introduction - Deep Learning/003 Activation Functions.mp434.61MB
  182. 24 Introduction - Deep Learning/004 Python - Creating Perceptron model.mp486.55MB
  183. 25 Neural Networks - Stacking cells to create network/001 Basic Terminologies.mp440.42MB
  184. 25 Neural Networks - Stacking cells to create network/002 Gradient Descent.mp460.34MB
  185. 25 Neural Networks - Stacking cells to create network/003 Back Propagation.mp4122.2MB
  186. 25 Neural Networks - Stacking cells to create network/004 Some Important Concepts.mp462.18MB
  187. 25 Neural Networks - Stacking cells to create network/005 Hyperparameter.mp445.35MB
  188. 26 ANN in Python/001 Keras and Tensorflow.mp414.91MB
  189. 26 ANN in Python/002 Installing Tensorflow and Keras.mp420.06MB
  190. 26 ANN in Python/003 Dataset for classification.mp456.19MB
  191. 26 ANN in Python/004 Normalization and Test-Train split.mp444.2MB
  192. 26 ANN in Python/005 Different ways to create ANN using Keras.mp410.81MB
  193. 26 ANN in Python/006 Building the Neural Network using Keras.mp479.11MB
  194. 26 ANN in Python/007 Compiling and Training the Neural Network model.mp481.63MB
  195. 26 ANN in Python/008 Evaluating performance and Predicting using Keras.mp469.91MB
  196. 26 ANN in Python/009 Building Neural Network for Regression Problem.mp4155.9MB
  197. 26 ANN in Python/010 Using Functional API for complex architectures.mp492.1MB
  198. 26 ANN in Python/011 Saving - Restoring Models and Using Callbacks.mp4151.58MB
  199. 26 ANN in Python/012 Hyperparameter Tuning.mp460.63MB
  200. 27 ANN in R/001 Installing Keras and Tensorflow.mp422.78MB
  201. 27 ANN in R/002 Data Normalization and Test-Train Split.mp4111.78MB
  202. 27 ANN in R/003 Building,Compiling and Training.mp4130.73MB
  203. 27 ANN in R/004 Evaluating and Predicting.mp499.28MB
  204. 27 ANN in R/005 ANN with NeuralNets Package.mp484.42MB
  205. 27 ANN in R/006 Building Regression Model with Functional API.mp4131.12MB
  206. 27 ANN in R/007 Complex Architectures using Functional API.mp479.57MB
  207. 01 Introduction/001 Introduction.mp429.39MB
  208. 28 CNN - Basics/001 CNN Introduction.mp451.15MB
  209. 28 CNN - Basics/002 Stride.mp416.58MB
  210. 28 CNN - Basics/003 Padding.mp431.63MB
  211. 28 CNN - Basics/004 Filters and Feature maps.mp452.71MB
  212. 28 CNN - Basics/005 Channels.mp467.77MB
  213. 28 CNN - Basics/006 PoolingLayer.mp446.87MB
  214. 29 Creating CNN model in Python/001 CNN model in Python - Preprocessing.mp440.63MB
  215. 29 Creating CNN model in Python/002 CNN model in Python - structure and Compile.mp443.25MB
  216. 29 Creating CNN model in Python/003 CNN model in Python - Training and results.mp455.15MB
  217. 29 Creating CNN model in Python/004 Comparison - Pooling vs Without Pooling in Python.mp457.97MB
  218. 30 Creating CNN model in R/001 CNN on MNIST Fashion Dataset - Model Architecture.mp47.35MB
  219. 30 Creating CNN model in R/002 Data Preprocessing.mp467.02MB
  220. 30 Creating CNN model in R/003 Creating Model Architecture.mp471.6MB
  221. 30 Creating CNN model in R/004 Compiling and training.mp432.2MB
  222. 30 Creating CNN model in R/005 Model Performance.mp468.08MB
  223. 30 Creating CNN model in R/006 Comparison - Pooling vs Without Pooling in R.mp444.6MB
  224. 31 Project _ Creating CNN model from scratch in Python/001 Project - Introduction.mp449.39MB
  225. 31 Project _ Creating CNN model from scratch in Python/003 Project - Data Preprocessing in Python.mp471.83MB
  226. 31 Project _ Creating CNN model from scratch in Python/004 Project - Training CNN model in Python.mp465.98MB
  227. 31 Project _ Creating CNN model from scratch in Python/005 Project in Python - model results.mp421.02MB
  228. 32 Project _ Creating CNN model from scratch/001 Project in R - Data Preprocessing.mp487.76MB
  229. 32 Project _ Creating CNN model from scratch/002 CNN Project in R - Structure and Compile.mp446.11MB
  230. 32 Project _ Creating CNN model from scratch/003 Project in R - Training.mp424.58MB
  231. 32 Project _ Creating CNN model from scratch/004 Project in R - Model Performance.mp423.18MB
  232. 32 Project _ Creating CNN model from scratch/005 Project in R - Data Augmentation.mp456.38MB
  233. 32 Project _ Creating CNN model from scratch/006 Project in R - Validation Performance.mp423.69MB
  234. 33 Project _ Data Augmentation for avoiding overfitting/001 Project - Data Augmentation Preprocessing.mp441.41MB
  235. 33 Project _ Data Augmentation for avoiding overfitting/002 Project - Data Augmentation Training and Results.mp453.04MB
  236. 34 Transfer Learning _ Basics/001 ILSVRC.mp420.92MB
  237. 34 Transfer Learning _ Basics/002 LeNET.mp47MB
  238. 34 Transfer Learning _ Basics/003 VGG16NET.mp410.35MB
  239. 34 Transfer Learning _ Basics/004 GoogLeNet.mp421.37MB
  240. 34 Transfer Learning _ Basics/005 Transfer Learning.mp429.99MB
  241. 34 Transfer Learning _ Basics/006 Project - Transfer Learning - VGG16.mp4129.09MB
  242. 35 Transfer Learning in R/001 Project - Transfer Learning - VGG16 (Implementation).mp4101.57MB
  243. 35 Transfer Learning in R/002 Project - Transfer Learning - VGG16 (Performance).mp464.11MB
  244. 36 Time Series Analysis and Forecasting/001 Introduction.mp412.26MB
  245. 36 Time Series Analysis and Forecasting/002 Time Series Forecasting - Use cases.mp425.91MB
  246. 36 Time Series Analysis and Forecasting/003 Forecasting model creation - Steps.mp410.11MB
  247. 36 Time Series Analysis and Forecasting/004 Forecasting model creation - Steps 1 (Goal).mp434.5MB
  248. 36 Time Series Analysis and Forecasting/005 Time Series - Basic Notations.mp462.48MB
  249. 37 Time Series - Preprocessing in Python/001 Data Loading in Python.mp4108.86MB
  250. 37 Time Series - Preprocessing in Python/002 Time Series - Visualization Basics.mp463.72MB
  251. 37 Time Series - Preprocessing in Python/003 Time Series - Visualization in Python.mp4165.19MB
  252. 37 Time Series - Preprocessing in Python/004 Time Series - Feature Engineering Basics.mp459.47MB
  253. 37 Time Series - Preprocessing in Python/005 Time Series - Feature Engineering in Python.mp4112.69MB
  254. 37 Time Series - Preprocessing in Python/006 Time Series - Upsampling and Downsampling.mp416.95MB
  255. 37 Time Series - Preprocessing in Python/007 Time Series - Upsampling and Downsampling in Python.mp4100.67MB
  256. 37 Time Series - Preprocessing in Python/008 Time Series - Power Transformation.mp414.85MB
  257. 37 Time Series - Preprocessing in Python/009 Moving Average.mp438.7MB
  258. 37 Time Series - Preprocessing in Python/010 Exponential Smoothing.mp48.38MB
  259. 38 Time Series - Important Concepts/001 White Noise.mp411.37MB
  260. 38 Time Series - Important Concepts/002 Random Walk.mp421.16MB
  261. 38 Time Series - Important Concepts/003 Decomposing Time Series in Python.mp459.84MB
  262. 38 Time Series - Important Concepts/004 Differencing.mp432.35MB
  263. 38 Time Series - Important Concepts/005 Differencing in Python.mp4113MB
  264. 39 Time Series - Implementation in Python/001 Test Train Split in Python.mp457.41MB
  265. 39 Time Series - Implementation in Python/002 Naive (Persistence) model in Python.mp443.37MB
  266. 39 Time Series - Implementation in Python/003 Auto Regression Model - Basics.mp416.88MB
  267. 39 Time Series - Implementation in Python/004 Auto Regression Model creation in Python.mp453.49MB
  268. 39 Time Series - Implementation in Python/005 Auto Regression with Walk Forward validation in Python.mp449.59MB
  269. 39 Time Series - Implementation in Python/006 Moving Average model -Basics.mp424.09MB
  270. 39 Time Series - Implementation in Python/007 Moving Average model in Python.mp456.65MB
  271. 40 Time Series - ARIMA model/001 ACF and PACF.mp441.22MB
  272. 40 Time Series - ARIMA model/002 ARIMA model - Basics.mp421.36MB
  273. 40 Time Series - ARIMA model/003 ARIMA model in Python.mp474.43MB
  274. 40 Time Series - ARIMA model/004 ARIMA model with Walk Forward Validation in Python.mp432.15MB
  275. 41 Time Series - SARIMA model/001 SARIMA model.mp439.02MB
  276. 41 Time Series - SARIMA model/002 SARIMA model in Python.mp466.23MB
  277. 41 Time Series - SARIMA model/003 Stationary time Series.mp45.58MB
  278. 42 Bonus Section/001 The final milestone!.mp411.84MB
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