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[] Udemy - Practical Machine Learning by Example in Python

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文件列表

  1. 4. Foundations NumPy/6. Linear Regression Example.mp4 65MB
  2. 2. Python Quick Start/12. Classes.mp4 62MB
  3. 7. Foundations Pandas/2. Loading and inspecting data example.mp4 59MB
  4. 3. Example Logistic Regression/3. Data analysis.mp4 59MB
  5. 9. Example Sentiment Analysis/11. Transfer Learning Example.mp4 58MB
  6. 6. Example Image recognition/14. Hyperparameter tuning example.mp4 52MB
  7. 2. Python Quick Start/3. String formatting.mp4 52MB
  8. 4. Foundations NumPy/5. Introduction to Linear Regression.mp4 52MB
  9. 6. Example Image recognition/8. Model training.mp4 50MB
  10. 10. Example Fraud detection/9. Making predictions.mp4 49MB
  11. 3. Example Logistic Regression/8. Gradient descent.mp4 46MB
  12. 9. Example Sentiment Analysis/5. Data Preparation.mp4 44MB
  13. 8. Example Recommendations/8. Model definition.mp4 42MB
  14. 10. Example Fraud detection/2. Data analysis.mp4 42MB
  15. 3. Example Logistic Regression/12. Making predictions.mp4 40MB
  16. 7. Foundations Pandas/5. Sorting and transforming data example.mp4 40MB
  17. 9. Example Sentiment Analysis/12. Fine Tuning and Prediction.mp4 40MB
  18. 6. Example Image recognition/2. Data analysis.mp4 38MB
  19. 3. Example Logistic Regression/6. The forward function.mp4 38MB
  20. 8. Example Recommendations/5. Data preparation.mp4 36MB
  21. 1. Course Structure and Development Environment/8. Sharing Colab Notebooks.mp4 36MB
  22. 7. Foundations Pandas/7. Visualizing data.mp4 35MB
  23. 2. Python Quick Start/2. Basic Syntax.mp4 35MB
  24. 7. Foundations Pandas/3. Indexing and selecting data example.mp4 35MB
  25. 9. Example Sentiment Analysis/8. Model Training.mp4 35MB
  26. 2. Python Quick Start/11. Defining functions.mp4 35MB
  27. 7. Foundations Pandas/6. Aggregations example.mp4 34MB
  28. 2. Python Quick Start/13. File IO and Modules.mp4 34MB
  29. 4. Foundations NumPy/10. Visualizing data.mp4 34MB
  30. 1. Course Structure and Development Environment/2. Course Quick Tips.mp4 32MB
  31. 2. Python Quick Start/10. Dictionaries.mp4 32MB
  32. 4. Foundations NumPy/11. Images.mp4 32MB
  33. 6. Example Image recognition/7. Model definition.mp4 32MB
  34. 1. Course Structure and Development Environment/1. Course Structure and Development Environment.mp4 31MB
  35. 9. Example Sentiment Analysis/2. Data Analysis.mp4 31MB
  36. 3. Example Logistic Regression/11. Model training.mp4 30MB
  37. 8. Example Recommendations/12. Making predictions.mp4 30MB
  38. 8. Example Recommendations/9. Model training.mp4 29MB
  39. 8. Example Recommendations/2. Data analysis.mp4 29MB
  40. 9. Example Sentiment Analysis/7. Model Definition.mp4 29MB
  41. 5. Foundations Tensorflow/2. Model example.mp4 29MB
  42. 10. Example Fraud detection/4. Unsupervised learning.mp4 29MB
  43. 10. Example Fraud detection/11. Common questions.mp4 29MB
  44. 3. Example Logistic Regression/1. The problem.mp4 28MB
  45. 6. Example Image recognition/16. Common questions.mp4 28MB
  46. 9. Example Sentiment Analysis/4. Supervised Learning.mp4 28MB
  47. 3. Example Logistic Regression/17. Improving the model.mp4 27MB
  48. 1. Course Structure and Development Environment/4. Jupyter notebook Text Cells.mp4 27MB
  49. 6. Example Image recognition/5. Data preparation.mp4 27MB
  50. 8. Example Recommendations/4. Model selection.mp4 27MB
  51. 1. Course Structure and Development Environment/9. Artificial Intelligence, Machine Learning, and Deep Learning.mp4 25MB
  52. 10. Example Fraud detection/7. Model training.mp4 25MB
  53. 4. Foundations NumPy/2. Creating data with NumPy.mp4 25MB
  54. 3. Example Logistic Regression/10. Backpropagation.mp4 24MB
  55. 9. Example Sentiment Analysis/10. Transfer Learning with BERT.mp4 24MB
  56. 5. Foundations Tensorflow/5. Training example.mp4 24MB
  57. 2. Python Quick Start/7. Flow control.mp4 24MB
  58. 5. Foundations Tensorflow/12. The Three Body Problem.mp4 23MB
  59. 1. Course Structure and Development Environment/6. Jupyter notebook Math Markup and Magic Commands.mp4 23MB
  60. 6. Example Image recognition/6. CNN Model Layers.mp4 23MB
  61. 6. Example Image recognition/4. Model selection.mp4 23MB
  62. 6. Example Image recognition/13. Hyperparameter tuning.mp4 22MB
  63. 2. Python Quick Start/8. Lists.mp4 22MB
  64. 10. Example Fraud detection/1. The problem.mp4 22MB
  65. 3. Example Logistic Regression/5. The model.mp4 22MB
  66. 10. Example Fraud detection/6. Model definition.mp4 21MB
  67. 3. Example Logistic Regression/7. Loss and cost functions.mp4 20MB
  68. 2. Python Quick Start/6. Type conversion.mp4 20MB
  69. 8. Example Recommendations/15. Common questions.mp4 20MB
  70. 6. Example Image recognition/1. The problem.mp4 19MB
  71. 5. Foundations Tensorflow/4. Activation functions.mp4 19MB
  72. 5. Foundations Tensorflow/7. Loss functions.mp4 19MB
  73. 3. Example Logistic Regression/15. Test vs. train accuracy.mp4 19MB
  74. 8. Example Recommendations/13. Error analysis.mp4 19MB
  75. 3. Example Logistic Regression/16. Speeding up training.mp4 18MB
  76. 5. Foundations Tensorflow/1. About this section.mp4 18MB
  77. 8. Example Recommendations/7. Embedding layers.mp4 17MB
  78. 8. Example Recommendations/1. The problem.mp4 17MB
  79. 4. Foundations NumPy/9. Statistics and linear algebra.mp4 17MB
  80. 1. Course Structure and Development Environment/3. Introduction to Jupyter Notebook.mp4 17MB
  81. 10. Example Fraud detection/5. Data preparation.mp4 17MB
  82. 5. Foundations Tensorflow/8. Optimizers.mp4 16MB
  83. 2. Python Quick Start/4. Literal string interpolation.mp4 16MB
  84. 5. Foundations Tensorflow/11. Saving and restoring models.mp4 16MB
  85. 6. Example Image recognition/11. Error analysis.mp4 15MB
  86. 1. Course Structure and Development Environment/5. Jupyter notebook Code Cells.mp4 15MB
  87. 4. Foundations NumPy/3. Basic operations.mp4 15MB
  88. 3. Example Logistic Regression/2. Machine Learning Development Process.mp4 14MB
  89. 4. Foundations NumPy/8. More Complex Models.mp4 14MB
  90. 5. Foundations Tensorflow/3. Model layers.mp4 13MB
  91. 9. Example Sentiment Analysis/1. The Problem.mp4 13MB
  92. 6. Example Image recognition/10. Making predictions.mp4 12MB
  93. 11. Next steps/1. Next steps.mp4 12MB
  94. 4. Foundations NumPy/13. Reshaping data.mp4 11MB
  95. 7. Foundations Pandas/1. What is Pandas and why is it useful.mp4 9MB
  96. 4. Foundations NumPy/1. What is NumPy and why it is needed.mp4 8MB
  97. 2. Python Quick Start/1. About this section.mp4 7MB
  98. 2. Python Quick Start/15. Prompting for passwords.mp4 7MB
  99. 5. Foundations Tensorflow/10. Prediction example.mp4 7MB
  100. 8. Example Recommendations/11. Predictions.mp4 6MB
  101. 11. Next steps/2. Thank you.mp4 3MB
  102. 2. Python Quick Start/12. Classes.srt 16KB
  103. 4. Foundations NumPy/6. Linear Regression Example.srt 16KB
  104. 4. Foundations NumPy/5. Introduction to Linear Regression.srt 14KB
  105. 3. Example Logistic Regression/3. Data analysis.srt 12KB
  106. 10. Example Fraud detection/9. Making predictions.srt 12KB
  107. 3. Example Logistic Regression/12. Making predictions.srt 12KB
  108. 3. Example Logistic Regression/8. Gradient descent.srt 12KB
  109. 8. Example Recommendations/8. Model definition.srt 11KB
  110. 2. Python Quick Start/11. Defining functions.srt 11KB
  111. 6. Example Image recognition/2. Data analysis.srt 11KB
  112. 9. Example Sentiment Analysis/11. Transfer Learning Example.srt 10KB
  113. 2. Python Quick Start/13. File IO and Modules.srt 10KB
  114. 9. Example Sentiment Analysis/5. Data Preparation.srt 10KB
  115. 2. Python Quick Start/3. String formatting.srt 9KB
  116. 9. Example Sentiment Analysis/12. Fine Tuning and Prediction.srt 9KB
  117. 9. Example Sentiment Analysis/8. Model Training.srt 9KB
  118. 7. Foundations Pandas/2. Loading and inspecting data example.srt 9KB
  119. 3. Example Logistic Regression/11. Model training.srt 9KB
  120. 1. Course Structure and Development Environment/2. Course Quick Tips.srt 9KB
  121. 2. Python Quick Start/2. Basic Syntax.srt 9KB
  122. 8. Example Recommendations/5. Data preparation.srt 9KB
  123. 6. Example Image recognition/14. Hyperparameter tuning example.srt 8KB
  124. 1. Course Structure and Development Environment/8. Sharing Colab Notebooks.srt 8KB
  125. 10. Example Fraud detection/2. Data analysis.srt 8KB
  126. 4. Foundations NumPy/11. Images.srt 8KB
  127. 3. Example Logistic Regression/6. The forward function.srt 7KB
  128. 7. Foundations Pandas/5. Sorting and transforming data example.srt 7KB
  129. 2. Python Quick Start/4. Literal string interpolation.srt 7KB
  130. 2. Python Quick Start/7. Flow control.srt 7KB
  131. 6. Example Image recognition/8. Model training.srt 7KB
  132. 2. Python Quick Start/8. Lists.srt 7KB
  133. 7. Foundations Pandas/7. Visualizing data.srt 7KB
  134. 9. Example Sentiment Analysis/7. Model Definition.srt 7KB
  135. 2. Python Quick Start/10. Dictionaries.srt 7KB
  136. 8. Example Recommendations/12. Making predictions.srt 7KB
  137. 3. Example Logistic Regression/17. Improving the model.srt 7KB
  138. 7. Foundations Pandas/3. Indexing and selecting data example.srt 7KB
  139. 5. Foundations Tensorflow/2. Model example.srt 7KB
  140. 9. Example Sentiment Analysis/10. Transfer Learning with BERT.srt 7KB
  141. 8. Example Recommendations/4. Model selection.srt 6KB
  142. 3. Example Logistic Regression/10. Backpropagation.srt 6KB
  143. 8. Example Recommendations/9. Model training.srt 6KB
  144. 10. Example Fraud detection/7. Model training.srt 6KB
  145. 10. Example Fraud detection/11. Common questions.srt 6KB
  146. 3. Example Logistic Regression/15. Test vs. train accuracy.srt 6KB
  147. 4. Foundations NumPy/10. Visualizing data.srt 6KB
  148. 6. Example Image recognition/6. CNN Model Layers.srt 6KB
  149. 4. Foundations NumPy/9. Statistics and linear algebra.srt 6KB
  150. 10. Example Fraud detection/4. Unsupervised learning.srt 6KB
  151. 6. Example Image recognition/5. Data preparation.srt 6KB
  152. 6. Example Image recognition/11. Error analysis.srt 6KB
  153. 4. Foundations NumPy/2. Creating data with NumPy.srt 6KB
  154. 1. Course Structure and Development Environment/1. Course Structure and Development Environment.srt 6KB
  155. 1. Course Structure and Development Environment/9. Artificial Intelligence, Machine Learning, and Deep Learning.srt 6KB
  156. 3. Example Logistic Regression/5. The model.srt 5KB
  157. 8. Example Recommendations/13. Error analysis.srt 5KB
  158. 2. Python Quick Start/6. Type conversion.srt 5KB
  159. 9. Example Sentiment Analysis/2. Data Analysis.srt 5KB
  160. 10. Example Fraud detection/6. Model definition.srt 5KB
  161. 8. Example Recommendations/2. Data analysis.srt 5KB
  162. 9. Example Sentiment Analysis/4. Supervised Learning.srt 5KB
  163. 6. Example Image recognition/4. Model selection.srt 5KB
  164. 1. Course Structure and Development Environment/6. Jupyter notebook Math Markup and Magic Commands.srt 5KB
  165. 6. Example Image recognition/7. Model definition.srt 5KB
  166. 5. Foundations Tensorflow/5. Training example.srt 5KB
  167. 6. Example Image recognition/13. Hyperparameter tuning.srt 5KB
  168. 4. Foundations NumPy/3. Basic operations.srt 5KB
  169. 6. Example Image recognition/16. Common questions.srt 5KB
  170. 3. Example Logistic Regression/1. The problem.srt 5KB
  171. 5. Foundations Tensorflow/4. Activation functions.srt 5KB
  172. 3. Example Logistic Regression/7. Loss and cost functions.srt 5KB
  173. 5. Foundations Tensorflow/7. Loss functions.srt 4KB
  174. 4. Foundations NumPy/8. More Complex Models.srt 4KB
  175. 5. Foundations Tensorflow/11. Saving and restoring models.srt 4KB
  176. 8. Example Recommendations/7. Embedding layers.srt 4KB
  177. 7. Foundations Pandas/6. Aggregations example.srt 4KB
  178. 3. Example Logistic Regression/16. Speeding up training.srt 4KB
  179. 1. Course Structure and Development Environment/3. Introduction to Jupyter Notebook.srt 4KB
  180. 1. Course Structure and Development Environment/5. Jupyter notebook Code Cells.srt 4KB
  181. 10. Example Fraud detection/5. Data preparation.srt 4KB
  182. 6. Example Image recognition/1. The problem.srt 4KB
  183. 3. Example Logistic Regression/2. Machine Learning Development Process.srt 4KB
  184. 10. Example Fraud detection/1. The problem.srt 4KB
  185. 4. Foundations NumPy/13. Reshaping data.srt 4KB
  186. 5. Foundations Tensorflow/12. The Three Body Problem.srt 4KB
  187. 8. Example Recommendations/15. Common questions.srt 4KB
  188. 8. Example Recommendations/1. The problem.srt 3KB
  189. 1. Course Structure and Development Environment/4. Jupyter notebook Text Cells.srt 3KB
  190. 6. Example Image recognition/10. Making predictions.srt 3KB
  191. 5. Foundations Tensorflow/1. About this section.srt 3KB
  192. 9. Example Sentiment Analysis/1. The Problem.srt 3KB
  193. 5. Foundations Tensorflow/8. Optimizers.srt 3KB
  194. 5. Foundations Tensorflow/3. Model layers.srt 3KB
  195. 6. Example Image recognition/19. What you learned in this section.html 3KB
  196. 11. Next steps/1. Next steps.srt 2KB
  197. 5. Foundations Tensorflow/10. Prediction example.srt 2KB
  198. 2. Python Quick Start/15. Prompting for passwords.srt 2KB
  199. 7. Foundations Pandas/1. What is Pandas and why is it useful.srt 2KB
  200. 5. Foundations Tensorflow/13. What you learned in this section.html 2KB
  201. 3. Example Logistic Regression/18. What you learned in this section.html 2KB
  202. 4. Foundations NumPy/1. What is NumPy and why it is needed.srt 1KB
  203. 10. Example Fraud detection/13. What you learned in this section.html 1KB
  204. 2. Python Quick Start/1. About this section.srt 1KB
  205. 8. Example Recommendations/11. Predictions.srt 1KB
  206. 8. Example Recommendations/16. What you learned in this section.html 1KB
  207. 4. Foundations NumPy/14. What you learned in this section.html 823B
  208. 1. Course Structure and Development Environment/10. What you learned in this section.html 674B
  209. 2. Python Quick Start/16. What you learned in this section.html 584B
  210. 7. Foundations Pandas/9. What you learned in this section.html 555B
  211. 11. Next steps/2. Thank you.srt 538B
  212. 9. Example Sentiment Analysis/14. What you learned in this section.html 425B
  213. 5. Foundations Tensorflow/12.2 New Neural Network Could Solve The Three-Body Problem 100 Million Times Faster.html 174B
  214. 1. Course Structure and Development Environment/8.1 Saving notebooks to Github or Drive.html 170B
  215. 3. Example Logistic Regression/3.1 Github repo.html 159B
  216. 7. Foundations Pandas/5.1 Sorting data.html 153B
  217. 9. Example Sentiment Analysis/2.2 Github repo.html 149B
  218. 1. Course Structure and Development Environment/7. Introduction to Notebooks.html 148B
  219. 10. Example Fraud detection/10. Prediction and error analysis.html 148B
  220. 10. Example Fraud detection/12. Improving the model.html 148B
  221. 10. Example Fraud detection/3. Analyze credit card data set.html 148B
  222. 10. Example Fraud detection/8. Training the model.html 148B
  223. 2. Python Quick Start/14. Plot several math functions.html 148B
  224. 2. Python Quick Start/5. Experiment with string formatting.html 148B
  225. 2. Python Quick Start/9. Dot product.html 148B
  226. 3. Example Logistic Regression/13. Training a model.html 148B
  227. 3. Example Logistic Regression/14. Optional Wine Classification.html 148B
  228. 3. Example Logistic Regression/4. Analyze Iris flower data set.html 148B
  229. 3. Example Logistic Regression/9. Experiment with gradient descent.html 148B
  230. 4. Foundations NumPy/12. Visualizing data.html 148B
  231. 4. Foundations NumPy/4. Experiment with NumPy.html 148B
  232. 4. Foundations NumPy/7. Experiment with Linear Regression.html 148B
  233. 5. Foundations Tensorflow/6. Train a basic model.html 148B
  234. 5. Foundations Tensorflow/9. Experiment with optimizers.html 148B
  235. 6. Example Image recognition/12. Prediction and error analysis.html 148B
  236. 6. Example Image recognition/15. Model improvement.html 148B
  237. 6. Example Image recognition/17. Optional Real images.html 148B
  238. 6. Example Image recognition/18. Optional Other image types.html 148B
  239. 6. Example Image recognition/3. Analyze MNIST data set.html 148B
  240. 6. Example Image recognition/9. Training a model.html 148B
  241. 7. Foundations Pandas/4. Experiment with Pandas.html 148B
  242. 7. Foundations Pandas/8. Visualizing data with Pandas.html 148B
  243. 8. Example Recommendations/10. Training the model.html 148B
  244. 8. Example Recommendations/14. Making recommendations and error analysis.html 148B
  245. 8. Example Recommendations/3. Analyze MovieLens data set.html 148B
  246. 8. Example Recommendations/6. Prepare data.html 148B
  247. 9. Example Sentiment Analysis/13. Transfer Learning with BERT.html 148B
  248. 9. Example Sentiment Analysis/3. Analyze Sentiment Data Set.html 148B
  249. 9. Example Sentiment Analysis/6. Prepare Data.html 148B
  250. 9. Example Sentiment Analysis/9. Training the Model.html 148B
  251. 1. Course Structure and Development Environment/3.2 IBM Watson Studio Notebooks.html 147B
  252. 2. Python Quick Start/3.2 printf style formatting.html 139B
  253. 5. Foundations Tensorflow/4.2 Tensorflow activations.html 138B
  254. 5. Foundations Tensorflow/2.1 Sequential models.html 137B
  255. 5. Foundations Tensorflow/8.2 Tensorflow optimizers.html 137B
  256. 7. Foundations Pandas/7.1 Pandas visualization user guide.html 135B
  257. 5. Foundations Tensorflow/7.1 Loss functions.html 133B
  258. 5. Foundations Tensorflow/10.1 Model API.html 132B
  259. 5. Foundations Tensorflow/11.1 Model API.html 132B
  260. 7. Foundations Pandas/3.1 User Guide Indexing and Selecting Data.html 130B
  261. 9. Example Sentiment Analysis/2.1 Data set.html 129B
  262. 7. Foundations Pandas/6.1 Pandas group by API.html 128B
  263. [Tutorialsplanet.NET].url 128B
  264. 8. Example Recommendations/15.2 BellKor solution.html 126B
  265. 1. Course Structure and Development Environment/4.1 Markdown cheat sheet.html 125B
  266. 7. Foundations Pandas/2.2 Pandas IO.html 124B
  267. 4. Foundations NumPy/9.2 Statistics functions.html 122B
  268. 2. Python Quick Start/3.1 Format string syntax.html 120B
  269. 8. Example Recommendations/15.1 Other solutions.html 120B
  270. 10. Example Fraud detection/11.1 Building Autoencoders in Keras.html 118B
  271. 4. Foundations NumPy/9.1 Linear algebra.html 118B
  272. 5. Foundations Tensorflow/8.1 Stochastic gradient descent and related optimizers.html 118B
  273. 9. Example Sentiment Analysis/1.2 Natural Language Processing (NLP).html 118B
  274. 9. Example Sentiment Analysis/4.1 Natural Language Processing (NLP).html 118B
  275. 1. Course Structure and Development Environment/3.6 AWS Sagemaker Notebook Instances.html 117B
  276. 6. Example Image recognition/7.2 Sequential model guide.html 117B
  277. 8. Example Recommendations/8.1 Keras functional API.html 115B
  278. 8. Example Recommendations/2.1 Collaborative filtering article.html 114B
  279. 4. Foundations NumPy/11.3 Image manipulation with NumPy.html 113B
  280. 4. Foundations NumPy/11.1 Hughes 500.html 112B
  281. 1. Course Structure and Development Environment/1.1 Github repo.html 110B
  282. 5. Foundations Tensorflow/2.2 Github repo.html 110B
  283. 5. Foundations Tensorflow/4.1 Activation functions.html 110B
  284. 5. Foundations Tensorflow/12.1 Three Body Problem.html 109B
  285. 9. Example Sentiment Analysis/1.1 Sentiment Analysis.html 109B
  286. 1. Course Structure and Development Environment/6.1 LaTeX syntax.html 108B
  287. 4. Foundations NumPy/11.2 Aviation.html 104B
  288. 8. Example Recommendations/15.3 Netflix prize.html 104B
  289. 9. Example Sentiment Analysis/5.1 GloVe Vectors.html 101B
  290. 6. Example Image recognition/7.1 Keras CNN layers.html 99B
  291. 1. Course Structure and Development Environment/3.5 Kaggle Notebooks.html 96B
  292. 8. Example Recommendations/7.1 Keras Embedding Layers documentation.html 96B
  293. 1. Course Structure and Development Environment/3.1 Google Colaboratory.html 95B
  294. 10. Example Fraud detection/2.1 Github repo.html 94B
  295. 4. Foundations NumPy/10.1 Matplotlib home page.html 94B
  296. 6. Example Image recognition/1.1 The MNIST database of handwritten digits.html 94B
  297. 6. Example Image recognition/2.1 Example Github repository.html 94B
  298. 6. Example Image recognition/4.1 MNIST models and their accuracy.html 94B
  299. 7. Foundations Pandas/2.1 Github repo.html 94B
  300. 8. Example Recommendations/2.2 Github repo.html 94B
  301. 6. Example Image recognition/10.1 Keras Model API.html 91B
  302. 1. Course Structure and Development Environment/3.4 Microsoft Azure Notebooks.html 89B
  303. 4. Foundations NumPy/2.2 NumPy documentation.html 88B
  304. 5. Foundations Tensorflow/1.1 Tensorflow home page.html 87B
  305. 7. Foundations Pandas/1.1 Pandas Home Page.html 86B
  306. 1. Course Structure and Development Environment/3.3 CoCalc.html 80B
  307. 4. Foundations NumPy/2.1 NumPy home page.html 79B
  308. 1. Course Structure and Development Environment/[DesireCourse.Net].url 51B
  309. 6. Example Image recognition/[DesireCourse.Net].url 51B
  310. 9. Example Sentiment Analysis/[DesireCourse.Net].url 51B
  311. 1. Course Structure and Development Environment/[CourseClub.Me].url 48B
  312. 6. Example Image recognition/[CourseClub.Me].url 48B
  313. 9. Example Sentiment Analysis/[CourseClub.Me].url 48B