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[] Udemy - Machine Learning, Data Science and Deep Learning with Python

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  • 文件大小:8GB
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文件列表

  1. 2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.mp4 148MB
  2. 6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.mp4 142MB
  3. 10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.mp4 142MB
  4. 8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.mp4 134MB
  5. 5. Recommender Systems/5. [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering.mp4 133MB
  6. 6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.mp4 132MB
  7. 7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.mp4 129MB
  8. 2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.mp4 129MB
  9. 10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.mp4 128MB
  10. 2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.mp4 125MB
  11. 1. Getting Started/11. Introducing the Pandas Library [Optional].mp4 123MB
  12. 10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.mp4 118MB
  13. 8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.mp4 118MB
  14. 2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.mp4 117MB
  15. 10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.mp4 115MB
  16. 2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.mp4 114MB
  17. 8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.mp4 112MB
  18. 2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.mp4 111MB
  19. 6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.mp4 110MB
  20. 5. Recommender Systems/3. [Activity] Finding Movie Similarities using Cosine Similarity.mp4 108MB
  21. 8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.mp4 106MB
  22. 10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.mp4 105MB
  23. 6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.mp4 103MB
  24. 8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.mp4 103MB
  25. 4. Machine Learning with Python/14. [Activity] XGBoost.mp4 102MB
  26. 1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4 101MB
  27. 3. Predictive Models/1. [Activity] Linear Regression.mp4 100MB
  28. 4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.mp4 99MB
  29. 8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).mp4 99MB
  30. 11. Final Project/2. Final project review.mp4 99MB
  31. 9. Experimental Design ML in the Real World/2. AB Testing Concepts.mp4 97MB
  32. 9. Experimental Design ML in the Real World/6. AB Test Gotchas.mp4 96MB
  33. 4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.mp4 96MB
  34. 5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.mp4 95MB
  35. 1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.mp4 94MB
  36. 10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).mp4 93MB
  37. 10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.mp4 92MB
  38. 8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.mp4 90MB
  39. 4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.mp4 89MB
  40. 10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.mp4 88MB
  41. 4. Machine Learning with Python/11. Decision Trees Concepts.mp4 87MB
  42. 5. Recommender Systems/1. User-Based Collaborative Filtering.mp4 86MB
  43. 5. Recommender Systems/6. [Exercise] Improve the recommender's results.mp4 84MB
  44. 8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.mp4 84MB
  45. 1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.mp4 83MB
  46. 9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.mp4 82MB
  47. 10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.mp4 81MB
  48. 10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.mp4 80MB
  49. 7. Dealing with Real-World Data/3. Data Cleaning and Normalization.mp4 79MB
  50. 6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.mp4 78MB
  51. 2. Statistics and Probability Refresher, and Python Practice/1. Types of Data (Numerical, Categorical, Ordinal).mp4 77MB
  52. 7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.mp4 76MB
  53. 2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions (Normal, Binomial, Poisson, etc).mp4 75MB
  54. 5. Recommender Systems/2. Item-Based Collaborative Filtering.mp4 75MB
  55. 10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.mp4 74MB
  56. 3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.mp4 74MB
  57. 4. Machine Learning with Python/5. K-Means Clustering.mp4 72MB
  58. 10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.mp4 70MB
  59. 10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).mp4 69MB
  60. 8. Apache Spark Machine Learning on Big Data/10. TF IDF.mp4 69MB
  61. 6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis (PCA).mp4 68MB
  62. 3. Predictive Models/2. [Activity] Polynomial Regression.mp4 67MB
  63. 7. Dealing with Real-World Data/1. BiasVariance Tradeoff.mp4 66MB
  64. 4. Machine Learning with Python/13. Ensemble Learning.mp4 65MB
  65. 9. Experimental Design ML in the Real World/3. T-Tests and P-Values.mp4 65MB
  66. 10. Deep Learning and Neural Networks/4. Deep Learning Details.mp4 64MB
  67. 10. Deep Learning and Neural Networks/5. Introducing Tensorflow.mp4 64MB
  68. 12. You made it!/1. More to Explore.mp4 64MB
  69. 2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.mp4 62MB
  70. 1. Getting Started/1. Introduction.mp4 60MB
  71. 2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.mp4 59MB
  72. 4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4 58MB
  73. 4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.mp4 57MB
  74. 2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.mp4 56MB
  75. 8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.mp4 55MB
  76. 11. Final Project/1. Your final project assignment Mammogram Classification.mp4 52MB
  77. 7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.mp4 49MB
  78. 7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp4 48MB
  79. 3. Predictive Models/4. Multi-Level Models.mp4 47MB
  80. 4. Machine Learning with Python/16. [Activity] Using SVM to cluster people using scikit-learn.mp4 47MB
  81. 4. Machine Learning with Python/15. Support Vector Machines (SVM) Overview.mp4 45MB
  82. 7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.mp4 42MB
  83. 4. Machine Learning with Python/3. Bayesian Methods Concepts.mp4 41MB
  84. 6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.mp4 40MB
  85. 10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.mp4 39MB
  86. 7. Dealing with Real-World Data/5. Normalizing numerical data.mp4 38MB
  87. 7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4 36MB
  88. 7. Dealing with Real-World Data/6. [Activity] Detecting outliers.mp4 36MB
  89. 4. Machine Learning with Python/7. Measuring Entropy.mp4 35MB
  90. 9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.mp4 35MB
  91. 10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.mp4 34MB
  92. 9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.mp4 33MB
  93. 1. Getting Started/7. Python Basics, Part 1 [Optional].mp4 33MB
  94. 2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.mp4 30MB
  95. 6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4 25MB
  96. 2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.mp4 22MB
  97. 1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].mp4 21MB
  98. 1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].mp4 21MB
  99. 1. Getting Started/2. Udemy 101 Getting the Most From This Course.mp4 20MB
  100. 10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4 18MB
  101. 4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.mp4 15MB
  102. 6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.mp4 15MB
  103. 1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].mp4 10MB
  104. 4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.mp4 7MB
  105. 4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.mp4 2MB
  106. 2. Statistics and Probability Refresher, and Python Practice/9. [Activity] Advanced Visualization with Seaborn.srt 30KB
  107. 2. Statistics and Probability Refresher, and Python Practice/8. [Activity] A Crash Course in matplotlib.srt 29KB
  108. 6. More Data Mining and Machine Learning Techniques/6. Reinforcement Learning.srt 28KB
  109. 6. More Data Mining and Machine Learning Techniques/2. [Activity] Using KNN to predict a rating for a movie.srt 28KB
  110. 2. Statistics and Probability Refresher, and Python Practice/11. [Exercise] Conditional Probability.srt 28KB
  111. 2. Statistics and Probability Refresher, and Python Practice/7. [Activity] Percentiles and Moments.srt 28KB
  112. 8. Apache Spark Machine Learning on Big Data/8. Introduction to Decision Trees in Spark.srt 28KB
  113. 4. Machine Learning with Python/14. [Activity] XGBoost.srt 27KB
  114. 2. Statistics and Probability Refresher, and Python Practice/10. [Activity] Covariance and Correlation.srt 26KB
  115. 2. Statistics and Probability Refresher, and Python Practice/4. [Activity] Variation and Standard Deviation.srt 26KB
  116. 3. Predictive Models/1. [Activity] Linear Regression.srt 26KB
  117. 11. Final Project/2. Final project review.srt 25KB
  118. 8. Apache Spark Machine Learning on Big Data/6. Spark and the Resilient Distributed Dataset (RDD).srt 24KB
  119. 7. Dealing with Real-World Data/4. [Activity] Cleaning web log data.srt 24KB
  120. 10. Deep Learning and Neural Networks/9. [Activity] Introducing Keras.srt 24KB
  121. 10. Deep Learning and Neural Networks/7. [Activity] Using Tensorflow, Part 1.srt 23KB
  122. 5. Recommender Systems/5. [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering.srt 23KB
  123. 6. More Data Mining and Machine Learning Techniques/7. [Activity] Reinforcement Learning & Q-Learning with Gym.srt 22KB
  124. 4. Machine Learning with Python/12. [Activity] Decision Trees Predicting Hiring Decisions.srt 22KB
  125. 9. Experimental Design ML in the Real World/6. AB Test Gotchas.srt 22KB
  126. 10. Deep Learning and Neural Networks/15. [Activity] Transfer Learning.srt 22KB
  127. 10. Deep Learning and Neural Networks/1. Deep Learning Pre-Requisites.srt 22KB
  128. 10. Deep Learning and Neural Networks/8. [Activity] Using Tensorflow, Part 2.srt 21KB
  129. 8. Apache Spark Machine Learning on Big Data/5. Spark Introduction.srt 21KB
  130. 6. More Data Mining and Machine Learning Techniques/4. [Activity] PCA Example with the Iris data set.srt 21KB
  131. 10. Deep Learning and Neural Networks/10. [Activity] Using Keras to Predict Political Affiliations.srt 21KB
  132. 3. Predictive Models/3. [Activity] Multiple Regression, and Predicting Car Prices.srt 21KB
  133. 4. Machine Learning with Python/11. Decision Trees Concepts.srt 21KB
  134. 4. Machine Learning with Python/1. Supervised vs. Unsupervised Learning, and TrainTest.srt 21KB
  135. 9. Experimental Design ML in the Real World/2. AB Testing Concepts.srt 20KB
  136. 5. Recommender Systems/3. [Activity] Finding Movie Similarities using Cosine Similarity.srt 20KB
  137. 5. Recommender Systems/2. Item-Based Collaborative Filtering.srt 20KB
  138. 10. Deep Learning and Neural Networks/11. Convolutional Neural Networks (CNN's).srt 20KB
  139. 10. Deep Learning and Neural Networks/18. The Ethics of Deep Learning.srt 20KB
  140. 10. Deep Learning and Neural Networks/5. Introducing Tensorflow.srt 20KB
  141. 6. More Data Mining and Machine Learning Techniques/5. Data Warehousing Overview ETL and ELT.srt 20KB
  142. 10. Deep Learning and Neural Networks/3. [Activity] Deep Learning in the Tensorflow Playground.srt 20KB
  143. 5. Recommender Systems/1. User-Based Collaborative Filtering.srt 19KB
  144. 10. Deep Learning and Neural Networks/2. The History of Artificial Neural Networks.srt 19KB
  145. 10. Deep Learning and Neural Networks/13. Recurrent Neural Networks (RNN's).srt 18KB
  146. 1. Getting Started/11. Introducing the Pandas Library [Optional].srt 18KB
  147. 8. Apache Spark Machine Learning on Big Data/9. [Activity] K-Means Clustering in Spark.srt 18KB
  148. 3. Predictive Models/2. [Activity] Polynomial Regression.srt 18KB
  149. 4. Machine Learning with Python/4. [Activity] Implementing a Spam Classifier with Naive Bayes.srt 17KB
  150. 4. Machine Learning with Python/5. K-Means Clustering.srt 17KB
  151. 7. Dealing with Real-World Data/3. Data Cleaning and Normalization.srt 17KB
  152. 1. Getting Started/4. [Activity] WINDOWS Installing and Using Anaconda & Course Materials.srt 17KB
  153. 10. Deep Learning and Neural Networks/14. [Activity] Using a RNN for sentiment analysis.srt 17KB
  154. 10. Deep Learning and Neural Networks/4. Deep Learning Details.srt 17KB
  155. 5. Recommender Systems/4. [Activity] Improving the Results of Movie Similarities.srt 17KB
  156. 4. Machine Learning with Python/16. [Activity] Using SVM to cluster people using scikit-learn.srt 17KB
  157. 7. Dealing with Real-World Data/2. [Activity] K-Fold Cross-Validation to avoid overfitting.srt 16KB
  158. 2. Statistics and Probability Refresher, and Python Practice/1. Types of Data (Numerical, Categorical, Ordinal).srt 16KB
  159. 2. Statistics and Probability Refresher, and Python Practice/6. Common Data Distributions (Normal, Binomial, Poisson, etc).srt 16KB
  160. 9. Experimental Design ML in the Real World/1. Deploying Models to Real-Time Systems.srt 15KB
  161. 1. Getting Started/6. [Activity] LINUX Installing and Using Anaconda & Course Materials.srt 15KB
  162. 2. Statistics and Probability Refresher, and Python Practice/3. [Activity] Using mean, median, and mode in Python.srt 15KB
  163. 4. Machine Learning with Python/13. Ensemble Learning.srt 15KB
  164. 7. Dealing with Real-World Data/1. BiasVariance Tradeoff.srt 14KB
  165. 7. Dealing with Real-World Data/8. Imputation Techniques for Missing Data.srt 14KB
  166. 7. Dealing with Real-World Data/10. Binning, Transforming, Encoding, Scaling, and Shuffling.srt 14KB
  167. 1. Getting Started/5. [Activity] MAC Installing and Using Anaconda & Course Materials.srt 14KB
  168. 8. Apache Spark Machine Learning on Big Data/10. TF IDF.srt 14KB
  169. 8. Apache Spark Machine Learning on Big Data/12. [Activity] Using the Spark 2.0 DataFrame API for MLLib.srt 14KB
  170. 10. Deep Learning and Neural Networks/12. [Activity] Using CNN's for handwriting recognition.srt 14KB
  171. 9. Experimental Design ML in the Real World/4. [Activity] Hands-on With T-Tests.srt 14KB
  172. 5. Recommender Systems/6. [Exercise] Improve the recommender's results.srt 13KB
  173. 9. Experimental Design ML in the Real World/3. T-Tests and P-Values.srt 13KB
  174. 4. Machine Learning with Python/2. [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.srt 13KB
  175. 2. Statistics and Probability Refresher, and Python Practice/2. Mean, Median, Mode.srt 13KB
  176. 8. Apache Spark Machine Learning on Big Data/11. [Activity] Searching Wikipedia with Spark.srt 13KB
  177. 6. More Data Mining and Machine Learning Techniques/3. Dimensionality Reduction; Principal Component Analysis (PCA).srt 12KB
  178. 8. Apache Spark Machine Learning on Big Data/3. [Activity] Installing Spark - Part 1.srt 12KB
  179. 10. Deep Learning and Neural Networks/17. Deep Learning Regularization with Dropout and Early Stopping.srt 12KB
  180. 7. Dealing with Real-World Data/7. Feature Engineering and the Curse of Dimensionality.srt 12KB
  181. 11. Final Project/1. Your final project assignment Mammogram Classification.srt 12KB
  182. 4. Machine Learning with Python/6. [Activity] Clustering people based on income and age.srt 12KB
  183. 2. Statistics and Probability Refresher, and Python Practice/13. Bayes' Theorem.srt 11KB
  184. 8. Apache Spark Machine Learning on Big Data/7. Introducing MLLib.srt 11KB
  185. 7. Dealing with Real-World Data/6. [Activity] Detecting outliers.srt 11KB
  186. 3. Predictive Models/4. Multi-Level Models.srt 11KB
  187. 8. Apache Spark Machine Learning on Big Data/4. [Activity] Installing Spark - Part 2.srt 11KB
  188. 6. More Data Mining and Machine Learning Techniques/9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).srt 10KB
  189. 4. Machine Learning with Python/15. Support Vector Machines (SVM) Overview.srt 10KB
  190. 7. Dealing with Real-World Data/9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.srt 10KB
  191. 6. More Data Mining and Machine Learning Techniques/8. Understanding a Confusion Matrix.srt 10KB
  192. 6. More Data Mining and Machine Learning Techniques/1. K-Nearest-Neighbors Concepts.srt 9KB
  193. 4. Machine Learning with Python/3. Bayesian Methods Concepts.srt 9KB
  194. 9. Experimental Design ML in the Real World/5. Determining How Long to Run an Experiment.srt 8KB
  195. 10. Deep Learning and Neural Networks/16. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.srt 8KB
  196. 1. Getting Started/7. Python Basics, Part 1 [Optional].srt 8KB
  197. 7. Dealing with Real-World Data/5. Normalizing numerical data.srt 8KB
  198. 1. Getting Started/8. [Activity] Python Basics, Part 2 [Optional].srt 8KB
  199. 2. Statistics and Probability Refresher, and Python Practice/5. Probability Density Function; Probability Mass Function.srt 8KB
  200. 12. You made it!/3. Bonus Lecture More courses to explore!.html 7KB
  201. 12. You made it!/1. More to Explore.srt 7KB
  202. 4. Machine Learning with Python/7. Measuring Entropy.srt 7KB
  203. 1. Getting Started/10. [Activity] Python Basics, Part 4 [Optional].srt 6KB
  204. 1. Getting Started/1. Introduction.srt 5KB
  205. 1. Getting Started/9. [Activity] Python Basics, Part 3 [Optional].srt 4KB
  206. 1. Getting Started/2. Udemy 101 Getting the Most From This Course.srt 4KB
  207. 2. Statistics and Probability Refresher, and Python Practice/12. Exercise Solution Conditional Probability of Purchase by Age.srt 4KB
  208. 8. Apache Spark Machine Learning on Big Data/2. Spark installation notes for MacOS and Linux users.html 4KB
  209. 10. Deep Learning and Neural Networks/19. Learning More about Deep Learning.srt 3KB
  210. 4. Machine Learning with Python/9. [Activity] MAC Installing Graphviz.srt 1KB
  211. 1. Getting Started/3. Installation Getting Started.html 1KB
  212. 4. Machine Learning with Python/10. [Activity] LINUX Installing Graphviz.srt 1KB
  213. 4. Machine Learning with Python/8. [Activity] WINDOWS Installing Graphviz.srt 689B
  214. 10. Deep Learning and Neural Networks/6. Important note about Tensorflow 2.html 644B
  215. 8. Apache Spark Machine Learning on Big Data/1. Warning about Java 11 and Spark 3!.html 602B
  216. 12. You made it!/2. Don't Forget to Leave a Rating!.html 564B
  217. 6. More Data Mining and Machine Learning Techniques/6.1 Pac-Man Example.html 145B
  218. 6. More Data Mining and Machine Learning Techniques/6.3 Cat and Mouse Example.html 140B
  219. 0. Websites you may like/[FCS Forum].url 133B
  220. 0. Websites you may like/[FreeCourseSite.com].url 127B
  221. 0. Websites you may like/[CourseClub.ME].url 122B
  222. 6. More Data Mining and Machine Learning Techniques/6.2 Python Markov Decision Process Toolbox.html 119B
  223. 8. Apache Spark Machine Learning on Big Data/3.1 winutils.exe.html 108B
  224. 8. Apache Spark Machine Learning on Big Data/4.1 winutils.exe.html 108B