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[FreeCourseSite.com] Udemy - Machine Learning using Python

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视频 2022-11-14 02:01 2024-11-19 21:26 115 7.06 GB 154
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FreeCourseSite com Udemy - Machine Learning using Python

文件列表
  1. 1. Setting up Python and Jupyter notebook/1. Installing Python and Anaconda.mp418.06MB
  2. 1. Setting up Python and Jupyter notebook/10. Working with Seaborn Library of Python.mp439.57MB
  3. 1. Setting up Python and Jupyter notebook/2. This is a Milestone!.mp420.66MB
  4. 1. Setting up Python and Jupyter notebook/3. Opening Jupyter Notebook.mp468.44MB
  5. 1. Setting up Python and Jupyter notebook/4. Introduction to Jupyter.mp444.06MB
  6. 1. Setting up Python and Jupyter notebook/5. Arithmetic operators in Python Python Basics.mp413.53MB
  7. 1. Setting up Python and Jupyter notebook/6. Strings in Python Python Basics.mp468.18MB
  8. 1. Setting up Python and Jupyter notebook/7. Lists, Tuples and Directories Python Basics.mp463.21MB
  9. 1. Setting up Python and Jupyter notebook/8. Working with Numpy Library of Python.mp446.45MB
  10. 1. Setting up Python and Jupyter notebook/9. Working with Pandas Library of Python.mp450.69MB
  11. 10. Comparing results from 3 models/1. Understanding the results of classification models.mp441.65MB
  12. 10. Comparing results from 3 models/2. Summary of the three models.mp422.23MB
  13. 11. Simple Decision Trees/1. Introduction to Decision trees.mp444.91MB
  14. 11. Simple Decision Trees/10. Creating Decision tree in Python.mp421.33MB
  15. 11. Simple Decision Trees/11. Evaluating model performance in Python.mp418.28MB
  16. 11. Simple Decision Trees/12. Plotting decision tree in Python.mp427.05MB
  17. 11. Simple Decision Trees/13. Pruning a tree.mp425.05MB
  18. 11. Simple Decision Trees/14. Pruning a tree in Python.mp425.06MB
  19. 11. Simple Decision Trees/2. Basics of Decision Trees.mp458.65MB
  20. 11. Simple Decision Trees/3. Understanding a Regression Tree.mp461.1MB
  21. 11. Simple Decision Trees/4. The stopping criteria for controlling tree growth.mp419.39MB
  22. 11. Simple Decision Trees/5. Importing the Data set into Python.mp415.86MB
  23. 11. Simple Decision Trees/6. Missing value treatment in Python.mp412.94MB
  24. 11. Simple Decision Trees/7. Dummy Variable Creation in Python.mp424.58MB
  25. 11. Simple Decision Trees/8. Dependent- Independent Data split in Python.mp416.87MB
  26. 11. Simple Decision Trees/9. Test-Train split in Python.mp425.63MB
  27. 12. Simple Classification Tree/1. Classification tree.mp440.23MB
  28. 12. Simple Classification Tree/2. The Data set for Classification problem.mp420.89MB
  29. 12. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp453.82MB
  30. 12. Simple Classification Tree/4. Classification tree in Python Training.mp499.55MB
  31. 12. Simple Classification Tree/5. Advantages and Disadvantages of Decision Trees.mp410.05MB
  32. 13. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp439.32MB
  33. 13. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp497.09MB
  34. 14. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp426.03MB
  35. 14. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp454.86MB
  36. 14. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp491.74MB
  37. 15. Ensemble technique 3 - Boosting/1. Boosting.mp440.9MB
  38. 15. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp439.86MB
  39. 15. Ensemble technique 3 - Boosting/3. Ensemble technique 3b - AdaBoost in Python.mp430.54MB
  40. 15. Ensemble technique 3 - Boosting/4. Ensemble technique 3c - XGBoost in Python.mp474.98MB
  41. 16. Support Vector Machines/1. Introduction to SVM's.mp421.64MB
  42. 16. Support Vector Machines/2. The Concept of a Hyperplane.mp440.55MB
  43. 16. Support Vector Machines/3. Maximum Margin Classifier.mp430.64MB
  44. 16. Support Vector Machines/4. Limitations of Maximum Margin Classifier.mp414.52MB
  45. 17. Support Vector classifiers/1. Support Vector classifiers.mp473.69MB
  46. 17. Support Vector classifiers/2. Limitations of Support Vector Classifiers.mp415.63MB
  47. 18. Support Vector Machines/1. Kernel Based Support Vector Machines.mp453.27MB
  48. 19. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp44.69MB
  49. 19. Creating Support Vector Machine Model in Python/10. Radial Kernel with Hyperparameter Tuning.mp444.5MB
  50. 19. Creating Support Vector Machine Model in Python/2. Importing and preprocessing data in Python.mp426.45MB
  51. 19. Creating Support Vector Machine Model in Python/3. Standardizing the data.mp442.09MB
  52. 19. Creating Support Vector Machine Model in Python/4. SVM based Regression Model in Python.mp473.72MB
  53. 19. Creating Support Vector Machine Model in Python/5. Classification model - Preprocessing.mp453.92MB
  54. 19. Creating Support Vector Machine Model in Python/6. Classification model - Standardizing the data.mp410.61MB
  55. 19. Creating Support Vector Machine Model in Python/7. SVM Based classification model.mp472.9MB
  56. 19. Creating Support Vector Machine Model in Python/8. Hyper Parameter Tuning.mp467.53MB
  57. 19. Creating Support Vector Machine Model in Python/9. Polynomial Kernel with Hyperparameter Tuning.mp426.45MB
  58. 2. Basics of statistics/1. Types of Data.mp423.31MB
  59. 2. Basics of statistics/2. Types of Statistics.mp411.99MB
  60. 2. Basics of statistics/3. Describing data Graphically.mp476.04MB
  61. 2. Basics of statistics/4. Measures of Centers.mp443.32MB
  62. 2. Basics of statistics/5. Measures of Dispersion.mp426.31MB
  63. 20. Time Series Analysis and Forecasting/1. Introduction.mp418.68MB
  64. 20. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp431.35MB
  65. 20. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp412.13MB
  66. 20. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp445.94MB
  67. 20. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp478.9MB
  68. 21. Time Series - Preprocessing in Pyhton/1. Data Loading in Python.mp4134.61MB
  69. 21. Time Series - Preprocessing in Pyhton/10. Exponential Smoothing.mp410.86MB
  70. 21. Time Series - Preprocessing in Pyhton/2. Time Series - Visualization Basics.mp480.35MB
  71. 21. Time Series - Preprocessing in Pyhton/3. Time Series - Visualization in Python.mp4208.24MB
  72. 21. Time Series - Preprocessing in Pyhton/4. Time Series - Feature Engineering Basics.mp476.92MB
  73. 21. Time Series - Preprocessing in Pyhton/5. Time Series - Feature Engineering in Python.mp4142.5MB
  74. 21. Time Series - Preprocessing in Pyhton/6. Time Series - Upsampling and Downsampling.mp423.34MB
  75. 21. Time Series - Preprocessing in Pyhton/7. Time Series - Upsampling and Downsampling in Python.mp4124.28MB
  76. 21. Time Series - Preprocessing in Pyhton/8. Time Series - Power Transformation.mp418.7MB
  77. 21. Time Series - Preprocessing in Pyhton/9. Moving Average.mp450.04MB
  78. 22. Time Series - Important Concepts/1. White Noise.mp414.71MB
  79. 22. Time Series - Important Concepts/2. Random Walk.mp428.05MB
  80. 22. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp478.61MB
  81. 22. Time Series - Important Concepts/4. Differencing.mp444.01MB
  82. 22. Time Series - Important Concepts/5. Differencing in Python.mp4141.15MB
  83. 23. Time Series - Implementation in Python/1. Test Train Split in Python.mp477.12MB
  84. 23. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp456.85MB
  85. 23. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp420.93MB
  86. 23. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp467.57MB
  87. 23. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp461.79MB
  88. 23. Time Series - Implementation in Python/6. Moving Average model -Basics.mp431.74MB
  89. 23. Time Series - Implementation in Python/7. Moving Average model in Python.mp464.3MB
  90. 24. Time Series - ARIMA model/1. ACF and PACF.mp452.76MB
  91. 24. Time Series - ARIMA model/2. ARIMA model - Basics.mp426.47MB
  92. 24. Time Series - ARIMA model/3. ARIMA model in Python.mp484.87MB
  93. 24. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp436.14MB
  94. 25. Time Series - SARIMA model/1. SARIMA model.mp440.23MB
  95. 25. Time Series - SARIMA model/2. SARIMA model in Python.mp475.11MB
  96. 25. Time Series - SARIMA model/3. Stationary time Series.mp45.66MB
  97. 25. Time Series - SARIMA model/4. The final milestone!.mp411.85MB
  98. 3. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4113.34MB
  99. 3. Introduction to Machine Learning/2. Building a Machine Learning Model.mp440.97MB
  100. 4. Data Preprocessing/1. Gathering Business Knowledge.mp417.27MB
  101. 4. Data Preprocessing/10. Missing Value Imputation in Python.mp433.32MB
  102. 4. Data Preprocessing/11. Seasonality in Data.mp417.02MB
  103. 4. Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4100.45MB
  104. 4. Data Preprocessing/13. Variable transformation and deletion in Python.mp467.33MB
  105. 4. Data Preprocessing/14. Non-usable variables.mp420.24MB
  106. 4. Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp440.47MB
  107. 4. Data Preprocessing/16. Dummy variable creation in Python.mp440.8MB
  108. 4. Data Preprocessing/17. Correlation Analysis.mp474.68MB
  109. 4. Data Preprocessing/18. Correlation Analysis in Python.mp465.61MB
  110. 4. Data Preprocessing/2. Data Exploration.mp428.38MB
  111. 4. Data Preprocessing/3. The Dataset and the Data Dictionary.mp476.34MB
  112. 4. Data Preprocessing/4. Importing Data in Python.mp433.93MB
  113. 4. Data Preprocessing/5. Univariate analysis and EDD.mp429.25MB
  114. 4. Data Preprocessing/6. EDD in Python.mp478.5MB
  115. 4. Data Preprocessing/7. Outlier Treatment.mp426.61MB
  116. 4. Data Preprocessing/8. Outlier Treatment in Python.mp498.28MB
  117. 4. Data Preprocessing/9. Missing Value Imputation.mp424.46MB
  118. 5. Linear Regression/1. The Problem Statement.mp410.14MB
  119. 5. Linear Regression/10. Test-train split.mp441.83MB
  120. 5. Linear Regression/11. Bias Variance trade-off.mp425.1MB
  121. 5. Linear Regression/12. Test train split in Python.mp464.03MB
  122. 5. Linear Regression/13. Regression models other than OLS.mp416.53MB
  123. 5. Linear Regression/14. Subset selection techniques.mp479.05MB
  124. 5. Linear Regression/15. Shrinkage methods Ridge and Lasso.mp433.29MB
  125. 5. Linear Regression/16. Ridge regression and Lasso in Python.mp4174.92MB
  126. 5. Linear Regression/17. Heteroscedasticity.mp414.49MB
  127. 5. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp442.52MB
  128. 5. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4103.21MB
  129. 5. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp444.97MB
  130. 5. Linear Regression/5. Simple Linear Regression in Python.mp484.86MB
  131. 5. Linear Regression/6. Multiple Linear Regression.mp438.17MB
  132. 5. Linear Regression/7. The F - statistic.mp453.78MB
  133. 5. Linear Regression/8. Interpreting results of Categorical variables.mp421.42MB
  134. 5. Linear Regression/9. Multiple Linear Regression in Python.mp485.12MB
  135. 6. Introduction to the classification Models/1. Three classification models and Data set.mp452.25MB
  136. 6. Introduction to the classification Models/2. Importing the data into Python.mp46.88MB
  137. 6. Introduction to the classification Models/3. The problem statements.mp417.05MB
  138. 6. Introduction to the classification Models/4. Why can't we use Linear Regression.mp416.91MB
  139. 7. Logistic Regression/1. Logistic Regression.mp432.91MB
  140. 7. Logistic Regression/2. Training a Simple Logistic Model in Python.mp469.52MB
  141. 7. Logistic Regression/3. Result of Simple Logistic Regression.mp426.9MB
  142. 7. Logistic Regression/4. Logistic with multiple predictors.mp48.51MB
  143. 7. Logistic Regression/5. Training multiple predictor Logistic model in Python.mp434.25MB
  144. 7. Logistic Regression/6. Confusion Matrix.mp421.1MB
  145. 7. Logistic Regression/7. Creating Confusion Matrix in Python.mp460.79MB
  146. 7. Logistic Regression/8. Evaluating performance of model.mp435.17MB
  147. 7. Logistic Regression/9. Evaluating model performance in Python.mp413.39MB
  148. 8. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp440.92MB
  149. 8. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp417.65MB
  150. 9. K Nearest neighbors classifier/1. Test-Train Split.mp439.26MB
  151. 9. K Nearest neighbors classifier/2. Test-Train Split in Python.mp459MB
  152. 9. K Nearest neighbors classifier/3. K-Nearest Neighbors classifier.mp475.36MB
  153. 9. K Nearest neighbors classifier/4. K-Nearest Neighbors in Python Part 1.mp446.15MB
  154. 9. K Nearest neighbors classifier/5. K-Nearest Neighbors in Python Part 2.mp453.16MB
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