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

GigaCourseUdemyMachineLearningDeepLearningPython

种子大小：12.55 Gb

收录时间：2026-01-25

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文件列表：

  1. 26. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4216.04 Mb
  2. 36. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4165.19 Mb
  3. 17. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4161.3 Mb
  4. 25. ANN in Python/9. Building Neural Network for Regression Problem.mp4155.91 Mb
  5. 25. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4151.58 Mb
  6. 22. Creating Support Vector Machine Model in R/3. Classification SVM model using Linear Kernel.mp4139.16 Mb
  7. 26. ANN in R/6. Building Regression Model with Functional API.mp4131.13 Mb
  8. 26. ANN in R/3. Building,Compiling and Training.mp4130.73 Mb
  9. 33. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4129.09 Mb
  10. 7. Linear Regression/20. Ridge regression and Lasso in Python.mp4128.84 Mb
  11. 24. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.2 Mb
  12. 37. Time Series - Important Concepts/5. Differencing in Python.mp4113 Mb
  13. 36. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4112.69 Mb
  14. 26. ANN in R/2. Data Normalization and Test-Train Split.mp4111.78 Mb
  15. 5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4109.17 Mb
  16. 36. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4108.86 Mb
  17. 22. Creating Support Vector Machine Model in R/7. SVM based Regression Model in R.mp4106.13 Mb
  18. 7. Linear Regression/21. Ridge regression and Lasso in R.mp4103.43 Mb
  19. 13. Simple Decision Trees/13. Building a Regression Tree in R.mp4103.33 Mb
  20. 34. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4101.58 Mb
  21. 36. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4100.67 Mb
  22. 6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4100.39 Mb
  23. 26. ANN in R/4. Evaluating and Predicting.mp499.28 Mb
  24. 6. Data Preprocessing/8. EDD in R.mp496.98 Mb
  25. 3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp496.73 Mb
  26. 7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp492.11 Mb
  27. 25. ANN in Python/10. Using Functional API for complex architectures.mp492.1 Mb
  28. 17. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp488.68 Mb
  29. 31. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp487.76 Mb
  30. 23. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp486.56 Mb
  31. 14. Simple Classification Tree/5. Building a classification Tree in R.mp485.11 Mb
  32. 26. ANN in R/5. ANN with NeuralNets Package.mp484.42 Mb
  33. 6. Data Preprocessing/25. Correlation Matrix in R.mp483.14 Mb
  34. 22. Creating Support Vector Machine Model in R/5. Polynomial Kernel with Hyperparameter Tuning.mp483.14 Mb
  35. 3. Setting up R Studio and R crash course/3. Packages in R.mp482.94 Mb
  36. 14. Simple Classification Tree/4. Classification tree in Python Training.mp482.71 Mb
  37. 13. Simple Decision Trees/18. Pruning a Tree in R.mp482.09 Mb
  38. 25. ANN in Python/7. Compiling and Training the Neural Network model.mp481.63 Mb
  39. 16. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp480.66 Mb
  40. 26. ANN in R/7. Complex Architectures using Functional API.mp479.57 Mb
  41. 25. ANN in Python/6. Building the Neural Network using Keras.mp479.11 Mb
  42. 7. Linear Regression/17. Subset selection techniques.mp479.06 Mb
  43. 15. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp477.31 Mb
  44. 7. Linear Regression/15. Test-Train Split in R.mp475.6 Mb
  45. 11. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp475.42 Mb
  46. 17. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp475 Mb
  47. 39. Time Series - ARIMA model/3. ARIMA model in Python.mp474.43 Mb
  48. 10. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp474.35 Mb
  49. 11. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp474.23 Mb
  50. 13. Simple Decision Trees/17. Pruning a tree in Python.mp473.5 Mb
  51. 30. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.mp471.83 Mb
  52. 29. Creating CNN model in R/3. Creating Model Architecture.mp471.6 Mb
  53. 6. Data Preprocessing/23. Correlation Analysis.mp471.6 Mb
  54. 6. Data Preprocessing/10. Outlier Treatment in Python.mp470.25 Mb
  55. 25. ANN in Python/8. Evaluating performance and Predicting using Keras.mp469.91 Mb
  56. 7. Linear Regression/10. Multiple Linear Regression in Python.mp469.73 Mb
  57. 6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp469.28 Mb
  58. 17. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp469.09 Mb
  59. 29. Creating CNN model in R/5. Model Performance.mp468.08 Mb
  60. 27. CNN - Basics/5. Channels.mp467.77 Mb
  61. 21. Creating Support Vector Machine Model in Python/4. SVM based Regression Model in Python.mp467.63 Mb
  62. 29. Creating CNN model in R/2. Data Preprocessing.mp467.02 Mb
  63. 40. Time Series - SARIMA model/2. SARIMA model in Python.mp466.23 Mb
  64. 30. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp465.98 Mb
  65. 4. Basics of Statistics/3. Describing data Graphically.mp465.39 Mb
  66. 2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.mp465.19 Mb
  67. 11. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp464.85 Mb
  68. 2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.mp464.43 Mb
  69. 21. Creating Support Vector Machine Model in Python/7. SVM Based classification model.mp464.13 Mb
  70. 34. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp464.12 Mb
  71. 36. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp463.72 Mb
  72. 7. Linear Regression/18. Subset selection in R.mp463.54 Mb
  73. 7. Linear Regression/5. Simple Linear Regression in Python.mp463.43 Mb
  74. 35. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp462.49 Mb
  75. 7. Linear Regression/11. Multiple Linear Regression in R.mp462.37 Mb
  76. 24. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp462.18 Mb
  77. 6. Data Preprocessing/7. EDD in Python.mp461.8 Mb
  78. 25. ANN in Python/12. Hyperparameter Tuning.mp460.63 Mb
  79. 22. Creating Support Vector Machine Model in R/4. Hyperparameter Tuning for Linear Kernel.mp460.5 Mb
  80. 24. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.34 Mb
  81. 2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.mp460.32 Mb
  82. 3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp460.1 Mb
  83. 37. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp459.84 Mb
  84. 36. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp459.47 Mb
  85. 15. Ensemble technique 1 - Bagging/3. Bagging in R.mp458.96 Mb
  86. 28. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp457.97 Mb
  87. 21. Creating Support Vector Machine Model in Python/8. Hyper Parameter Tuning.mp457.74 Mb
  88. 38. Time Series - Implementation in Python/1. Test Train Split in Python.mp457.41 Mb
  89. 27. CNN - Basics/1. CNN Introduction.mp456.75 Mb
  90. 22. Creating Support Vector Machine Model in R/6. Radial Kernel with Hyperparameter Tuning.mp456.68 Mb
  91. 38. Time Series - Implementation in Python/7. Moving Average model in Python.mp456.66 Mb
  92. 31. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp456.38 Mb
  93. 25. ANN in Python/3. Dataset for classification.mp456.19 Mb
  94. 19. Support Vector Classifier/1. Support Vector classifiers.mp456.16 Mb
  95. 7. Linear Regression/8. The F - statistic.mp455.98 Mb
  96. 9. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp455.7 Mb
  97. 6. Data Preprocessing/18. Variable transformation in R.mp455.42 Mb
  98. 6. Data Preprocessing/24. Correlation Analysis in Python.mp455.3 Mb
  99. 28. Creating CNN model in Python/3. CNN model in Python - Training and results.mp455.15 Mb
  100. 38. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp453.49 Mb
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