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[FreeCourseSite.com] Udemy - Introduction to Machine Learning & Deep Learning in Python

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视频 2019-12-11 12:45 2024-5-26 14:34 98 1.67 GB 140
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FreeCourseSite com Udemy - Introduction to Machine Learning & Deep Learning in Python

文件列表
  1. 1. Introduction/1. Introduction.mp43.48MB
  2. 1. Introduction/2. Introduction to machine learning.mp48.05MB
  3. 10. Boosting/1. Boosting introduction - basics.mp48.39MB
  4. 10. Boosting/2. Boosting introduction - illustration.mp48.17MB
  5. 10. Boosting/3. Boosting introduction - equations.mp413.71MB
  6. 10. Boosting/4. Boosting introduction - final formula.mp413.01MB
  7. 10. Boosting/5. Boosting implementation I - iris dataset.mp412.33MB
  8. 10. Boosting/6. Boosting implementation II -tuning.mp410.35MB
  9. 10. Boosting/7. Boosting vs. bagging.mp45.21MB
  10. 11. Clustering/1. Principal component anlysis introduction.mp48.58MB
  11. 11. Clustering/10. Hierarchical clustering example.mp411.96MB
  12. 11. Clustering/2. Principal component analysis example.mp414MB
  13. 11. Clustering/3. K-means clustering introduction I.mp413.67MB
  14. 11. Clustering/4. K-means clustering introduction II.mp49.47MB
  15. 11. Clustering/5. K-means clustering example.mp49.43MB
  16. 11. Clustering/6. K-means clustering - text clustering.mp418.86MB
  17. 11. Clustering/7. DBSCAN introduction.mp411.05MB
  18. 11. Clustering/8. DBSCAN example.mp47.88MB
  19. 11. Clustering/9. Hierarchical clustering introduction.mp413.66MB
  20. 12. Neural Networks/11. Feedforward neural networks.mp418.42MB
  21. 12. Neural Networks/12. Optimization - cost function.mp425.89MB
  22. 12. Neural Networks/13. Simplified feedforward network.mp419.42MB
  23. 12. Neural Networks/14. Feedforward neural network topology.mp414.73MB
  24. 12. Neural Networks/15. The learning algorithm.mp413.26MB
  25. 12. Neural Networks/16. Error calculation.mp413.74MB
  26. 12. Neural Networks/17. Gradient calculation I - output layer.mp420.28MB
  27. 12. Neural Networks/18. Gradient calculation II - hidden layer.mp49.18MB
  28. 12. Neural Networks/19. Backpropagation.mp412.67MB
  29. 12. Neural Networks/2. Axons and neurons in the human brain.mp419.24MB
  30. 12. Neural Networks/20. Backpropagation II.mp44.68MB
  31. 12. Neural Networks/21. Applications of neural networks I - character recognition.mp48.78MB
  32. 12. Neural Networks/22. Applications of neural networks II - stock market forecast.mp49.53MB
  33. 12. Neural Networks/23. Deep learning.mp49.47MB
  34. 12. Neural Networks/25. Building networks.mp412.75MB
  35. 12. Neural Networks/26. Building networks II.mp412.02MB
  36. 12. Neural Networks/27. Handling datasets.mp46.21MB
  37. 12. Neural Networks/28. Neural network example I - XOR problem.mp417.61MB
  38. 12. Neural Networks/29. Neural network example II - iris dataset.mp435.59MB
  39. 12. Neural Networks/3. Modeling human brain.mp416.17MB
  40. 12. Neural Networks/4. Learning paradigms.mp46.51MB
  41. 12. Neural Networks/5. Artificial neurons - the model.mp416.55MB
  42. 12. Neural Networks/6. Artificial neurons - activation functions.mp414.24MB
  43. 12. Neural Networks/7. Artificial neurons - an example.mp411.37MB
  44. 12. Neural Networks/8. Neural networks - the big picture.mp410.78MB
  45. 12. Neural Networks/9. Applications of neural networks.mp45.23MB
  46. 13. Machine Learning in Finance/1. Stock market basics.mp45.63MB
  47. 13. Machine Learning in Finance/2. Fetching data from Yahoo Finance.mp47.96MB
  48. 13. Machine Learning in Finance/3. Predicting stock prices logistic regression.mp410.76MB
  49. 13. Machine Learning in Finance/4. Predicting stock prices k-nearest neighbor.mp47.1MB
  50. 13. Machine Learning in Finance/5. Predicting stock prices support vector machine.mp48.71MB
  51. 13. Machine Learning in Finance/6. Predicting stock prices - conclusion.mp43.51MB
  52. 14. Computer Vision - Face Detection/1. Computer vision introduction.mp45.76MB
  53. 14. Computer Vision - Face Detection/10. Face detection implementation IV - tuning the parameters.mp48.73MB
  54. 14. Computer Vision - Face Detection/2. Viola-Jones algorithm.mp420.94MB
  55. 14. Computer Vision - Face Detection/3. Haar-features.mp412.64MB
  56. 14. Computer Vision - Face Detection/4. Integral images.mp49.58MB
  57. 14. Computer Vision - Face Detection/5. Boosting in computer vision.mp412.32MB
  58. 14. Computer Vision - Face Detection/6. Cascading.mp46.23MB
  59. 14. Computer Vision - Face Detection/7. Face detection implementation I - installing OpenCV.mp410.56MB
  60. 14. Computer Vision - Face Detection/8. Face detection implementation II - CascadeClassifier.mp415.92MB
  61. 14. Computer Vision - Face Detection/9. Face detection implementation III - CascadeClassifier parameters.mp48.6MB
  62. 15. Deep Learning/1. Types of neural networks.mp45.49MB
  63. 16. Deep Neural Networks/1. Deep neural networks.mp47.65MB
  64. 16. Deep Neural Networks/11. Multiclass classification implementation I.mp411.1MB
  65. 16. Deep Neural Networks/12. Multiclass classification implementation II.mp410.31MB
  66. 16. Deep Neural Networks/2. Activation functions revisited.mp415.42MB
  67. 16. Deep Neural Networks/3. Loss functions.mp410.39MB
  68. 16. Deep Neural Networks/4. Gradient descent stochastic gradient descent.mp412.26MB
  69. 16. Deep Neural Networks/5. Hyperparameters.mp48.26MB
  70. 16. Deep Neural Networks/7. Deep neural network implementation I.mp415.09MB
  71. 16. Deep Neural Networks/8. Deep neural network implementation II.mp415.81MB
  72. 16. Deep Neural Networks/9. Deep neural network implementation III.mp418.4MB
  73. 17. Convolutional Neural Networks/10. Handwritten digit classification I.mp416.48MB
  74. 17. Convolutional Neural Networks/11. Handwritten digit classification II.mp415.65MB
  75. 17. Convolutional Neural Networks/12. Handwritten digit classification III.mp410.44MB
  76. 17. Convolutional Neural Networks/2. Convolutional neural networks basics.mp49.58MB
  77. 17. Convolutional Neural Networks/3. Feature selection.mp46.95MB
  78. 17. Convolutional Neural Networks/4. Convolutional neural networks - kernel.mp46.34MB
  79. 17. Convolutional Neural Networks/5. Convolutional neural networks - kernel II.mp47.79MB
  80. 17. Convolutional Neural Networks/6. Convolutional neural networks - pooling.mp49.85MB
  81. 17. Convolutional Neural Networks/7. Convolutional neural networks - flattening.mp48.41MB
  82. 17. Convolutional Neural Networks/8. Convolutional neural networks - illustration.mp46.02MB
  83. 18. Recurrent Neural Networks/10. Stock price prediction example III.mp44.98MB
  84. 18. Recurrent Neural Networks/11. Stock price prediction example IV.mp414.55MB
  85. 18. Recurrent Neural Networks/12. Stock price prediction example V.mp46.74MB
  86. 18. Recurrent Neural Networks/13. Stock price prediction example VI.mp415.2MB
  87. 18. Recurrent Neural Networks/14. Stock price prediction example VII.mp47.21MB
  88. 18. Recurrent Neural Networks/2. Why do recurrent neural networks are important.mp47.52MB
  89. 18. Recurrent Neural Networks/3. Recurrent neural networks basics.mp412.89MB
  90. 18. Recurrent Neural Networks/4. Vanishing and exploding gradients problem.mp419.63MB
  91. 18. Recurrent Neural Networks/5. Long-short term memory (LTSM) model.mp417.03MB
  92. 18. Recurrent Neural Networks/6. Gated recurrent units (GRUs).mp45.03MB
  93. 18. Recurrent Neural Networks/8. Stock price prediction example I.mp411.09MB
  94. 18. Recurrent Neural Networks/9. Stock price prediction example II.mp418.37MB
  95. 2. Installations/1. Installing Anaconda.mp44.32MB
  96. 2. Installations/2. Installing Spyder.mp42.8MB
  97. 2. Installations/3. Installing Keras and TensorFlow.mp45.95MB
  98. 3. Linear Regression/1. Linear regression introduction.mp426.43MB
  99. 3. Linear Regression/2. Linear regression theory - optimization.mp442.28MB
  100. 3. Linear Regression/3. Linear regression theory - gradient descent.mp411.1MB
  101. 3. Linear Regression/4. Linear regression implementation I.mp416.69MB
  102. 3. Linear Regression/5. Linear regression implementation II.mp48.78MB
  103. 4. Logistic Regression/1. Logistic regression introduction.mp417.63MB
  104. 4. Logistic Regression/2. Logistic regression introduction II.mp46.67MB
  105. 4. Logistic Regression/3. Logistic regression example I - sigmoid function.mp413.04MB
  106. 4. Logistic Regression/4. Logistic regression example II- credit scoring.mp421.33MB
  107. 4. Logistic Regression/5. Logistic regression example III - credit scoring.mp410.87MB
  108. 4. Logistic Regression/6. Cross validation introduction.mp411.72MB
  109. 4. Logistic Regression/7. Cross validation example.mp44.15MB
  110. 5. K-Nearest Neighbor Classifier/1. K-nearest neighbor introduction.mp49.48MB
  111. 5. K-Nearest Neighbor Classifier/2. K-nearest neighbor introduction - lazy learning.mp48.11MB
  112. 5. K-Nearest Neighbor Classifier/3. K-nearest neighbor introduction - Euclidean-distance.mp48.61MB
  113. 5. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.mp46.95MB
  114. 5. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.mp49.96MB
  115. 5. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.mp47.93MB
  116. 6. Naive Bayes Classifier/1. Naive Bayes classifier introduction I.mp417.44MB
  117. 6. Naive Bayes Classifier/2. Naive Bayes classifier introduction II - illustration.mp48.43MB
  118. 6. Naive Bayes Classifier/3. Naive Bayes classifier implementation.mp48MB
  119. 6. Naive Bayes Classifier/5. Text clustering - basics.mp422.12MB
  120. 6. Naive Bayes Classifier/6. Text clustering - inverse document frequency (TF-IDF).mp410.02MB
  121. 6. Naive Bayes Classifier/7. Naive Bayes example - clustering news.mp423.33MB
  122. 7. Support Vector Machine (SVM)/1. Support vector machine introduction I - linear case.mp420.76MB
  123. 7. Support Vector Machine (SVM)/2. Support vector machine introduction II - non-linear case.mp417.22MB
  124. 7. Support Vector Machine (SVM)/3. Support vector machine introduction III - kernels.mp49.9MB
  125. 7. Support Vector Machine (SVM)/4. Support vector machine example I - simple.mp410.48MB
  126. 7. Support Vector Machine (SVM)/5. Support vector machine example II - iris dataset.mp421.7MB
  127. 7. Support Vector Machine (SVM)/6. Support vector machine example III - digit recognition.mp416.43MB
  128. 8. Decision Trees/1. Decision trees introduction - basics.mp411.73MB
  129. 8. Decision Trees/2. Decision trees introduction - entropy.mp419.29MB
  130. 8. Decision Trees/3. Decision trees introduction - information gain.mp446.96MB
  131. 8. Decision Trees/4. Decision trees introduction - pros and cons.mp44.19MB
  132. 8. Decision Trees/5. Decision trees implementation.mp413.6MB
  133. 8. Decision Trees/6. Decision trees implementation II.mp46.66MB
  134. 8. Decision Trees/7. The Gini-index approach.mp418.75MB
  135. 9. Random Forest Classifier/1. Pruning introduction.mp49.83MB
  136. 9. Random Forest Classifier/2. Bagging introduction.mp411.72MB
  137. 9. Random Forest Classifier/3. Random forest classifier introduction.mp48.72MB
  138. 9. Random Forest Classifier/4. Random forests example I - iris dataset.mp411.36MB
  139. 9. Random Forest Classifier/5. Random forests example II - credit scoring.mp44.21MB
  140. 9. Random Forest Classifier/6. Random forests example III - parameter tuning.mp49.19MB
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