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[CourseClub.Me] Pluralsight - Building Machine Learning Solutions With Java - Learning Paths

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CourseClub Me Pluralsight - Building Machine Learning Solutions With Java - Learning Paths

文件列表
  1. 1. Preparing Data for Machine Learning with Java/1. Course Overview/1. Course Overview.mp44.7MB
  2. 1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/1. Introduction.mp414.9MB
  3. 1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/2. What Is Data Preparation.mp41.94MB
  4. 1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/3. Ingesting CSV and Excel Files.mp410.97MB
  5. 1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/4. Ingesting JSON and XML Files.mp47.55MB
  6. 1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/5. Demo - Ingestion.mp428.71MB
  7. 1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/6. Summary.mp41.01MB
  8. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/1. Introduction.mp412.06MB
  9. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/2. Folder Monitoring.mp49.63MB
  10. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/3. Task Scheduling.mp46.11MB
  11. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/4. Demo - Using the File Watcher API.mp425.96MB
  12. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/5. Demo - Using the Quartz Scheduler Library.mp424.66MB
  13. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/6. Selenium.mp45.67MB
  14. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/7. Demo - Using the Selenium IDE.mp46.55MB
  15. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/8. Demo - Coding for Selenium.mp426.96MB
  16. 1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/9. Summary.mp41.05MB
  17. 1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/1. Introduction.mp411.71MB
  18. 1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/2. Lambdas and Streams.mp48.77MB
  19. 1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/3. Regular Expressions Overview.mp47.47MB
  20. 1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/4. Using Regular Expressions in Java.mp48.49MB
  21. 1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/5. Demo - Data Cleaning Pipeline.mp419.62MB
  22. 1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/6. Summary.mp4640.11KB
  23. 1. Preparing Data for Machine Learning with Java/5. Data Transformation/1. Introduction.mp47.55MB
  24. 1. Preparing Data for Machine Learning with Java/5. Data Transformation/2. Data Transformation Basics.mp47.45MB
  25. 1. Preparing Data for Machine Learning with Java/5. Data Transformation/3. Scaling, Data Skew, and Data Bias.mp49.83MB
  26. 1. Preparing Data for Machine Learning with Java/5. Data Transformation/4. Demo - Data Transformation Pipeline.mp434.37MB
  27. 1. Preparing Data for Machine Learning with Java/5. Data Transformation/5. Summary.mp4818.19KB
  28. 1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/1. Introduction.mp413.88MB
  29. 1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/2. Distributed Data Pipelines.mp45.59MB
  30. 1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/3. Beam SDK Concepts.mp46.89MB
  31. 1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/4. Beam SDK Engines and GCP Dataflow.mp49.45MB
  32. 1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/5. Demo - Developing Beam SDK Pipelines.mp434.22MB
  33. 1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/6. Demo - Deploying Beam SDK Pipelines to GCP Dataflow.mp413.52MB
  34. 1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/7. Summary.mp4954.93KB
  35. 1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/8. Wrap Up.mp42.8MB
  36. 2. Exploring Java Machine Learning Environments/1. Course Overview/1. Course Overview.mp43.85MB
  37. 2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/1. Version Check.mp4375.85KB
  38. 2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/2. Introduction.mp46.44MB
  39. 2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/3. Demo - Weka Showcase.mp410.44MB
  40. 2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/4. Demo - Deeplearning4j (DL4J) Showcase.mp414.78MB
  41. 2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/5. Demo - Spark MLlib Showcase.mp415.97MB
  42. 2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/6. Summary.mp42.59MB
  43. 2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/1. Introduction.mp47.52MB
  44. 2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/2. Demo - Data Preparation and Loading with Programmatic Weka.mp410.43MB
  45. 2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/3. Demo - Data Preprocessing with Programmatic Weka.mp44.1MB
  46. 2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/4. Demo - Implementing K-means with Programmatic Weka.mp43.61MB
  47. 2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/5. Demo - Evaluation and Visualization with Programmatic Weka.mp415.47MB
  48. 2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/6. Demo - The Full Workflow in One Go with Weka GUI.mp410.88MB
  49. 2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/7. Summary.mp41006.71KB
  50. 2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/1. Introduction.mp47.27MB
  51. 2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/2. Demo - Data Preparation and Loading with DL4J (Part 1 - Setup).mp412.16MB
  52. 2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/3. Demo - Data Preparation and Loading with DL4J (Part 2 - DL4J DataSetIt.mp428.14MB
  53. 2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/4. Demo - Data Preprocessing with DL4J.mp431.98MB
  54. 2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/5. Demo - Implementing a Twitter Sentiment Classifier with DL4J.mp420.44MB
  55. 2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/6. Demo - Performance and Evaluation and Visualization with DL4J.mp46.58MB
  56. 2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/7. Summary.mp41.12MB
  57. 2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/1. Introduction.mp48.35MB
  58. 2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/2. Demo - Data Preparation and Loading with Spark MLlib.mp412.09MB
  59. 2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/3. Demo - Data Preprocessing with Spark MLlib.mp418.44MB
  60. 2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/4. Demo - Implementing an Image Classifier with Spark MLlib.mp45.68MB
  61. 2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/5. Demo - Performance and Evaluation and Visualization with Spark .mp47.48MB
  62. 2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/6. Summary.mp4943.38KB
  63. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/01. Version Check.mp4516.19KB
  64. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/02. Prerequisites and Course Outline.mp43.62MB
  65. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/03. Introducing Weka.mp43.98MB
  66. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/04. Demo - Environment and Project Setup.mp411.15MB
  67. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/05. Demo - Exploring the Weka Workbench.mp414.99MB
  68. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/06. Demo - Loading and Exploring the Dataset.mp414.73MB
  69. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/07. Demo - Training and Evaluating a Regression Model.mp418.08MB
  70. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/08. Demo - Training and Evaluating a Multiple Regression Model.mp424.79MB
  71. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/09. Demo - Feature Selection and Ranking.mp423.08MB
  72. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/10. Demo - Processing and Saving Processed Data.mp420.27MB
  73. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/11. Demo - Evaluating a Model Using Cross Validation.mp47.77MB
  74. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/12. Demo - Regression Using Support Vector Machines and Multilayer Perceptrons.mp411.86MB
  75. 3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/13. Demo - Serializing and Visualizing a Decision Tree Model.mp420.87MB
  76. 3. Implementing Machine Learning Workflow with Weka/1. Course Overview/1. Course Overview.mp43.97MB
  77. 3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/1. Demo - Feature Selection and Data Processing.mp421.16MB
  78. 3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/2. Demo - Building and Evaluating a Classification Model.mp422.4MB
  79. 3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/3. Demo - Building and Visualizing a Decision Tree Model.mp414.72MB
  80. 3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/4. Demo - Encoding Text Data in Numeric Form.mp422.9MB
  81. 3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/5. Demo - Performing Classification on Text Data.mp423.74MB
  82. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/1. Demo - Normalizing and Visualizing Data.mp420.05MB
  83. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/2. Demo - Performing K-means Clustering.mp412.43MB
  84. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/3. Demo - Visualizing Cluster Assignments.mp424.84MB
  85. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/4. Demo - Exploring and Visualizing Data.mp49.96MB
  86. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/5. Demo - Performing Hierarchical Clustering.mp414.95MB
  87. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/6. Demo - Performing EM Clustering.mp410.26MB
  88. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/7. Demo - Serializing Trained Model Parameters.mp412.57MB
  89. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/8. Demo - Deploying a Model Using SpringBoot.mp421.85MB
  90. 3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/9. Summary and Further Study.mp42.58MB
  91. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/01. Version Check.mp4546.45KB
  92. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/02. Prerequisites and Course Outline.mp43.83MB
  93. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/03. Introducing RapidMiner.mp44.42MB
  94. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/04. Demo - Download and Setup RapidMiner.mp410.4MB
  95. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/05. Demo - Setting up a Repository and Importing Data.mp412.74MB
  96. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/06. Demo - Exploring the Dataset.mp419.09MB
  97. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/07. Demo - Build and Evaluate a Linear Regression Model.mp415.66MB
  98. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/08. Demo - Train Model on Training Data and Evaluate Using T.mp410.33MB
  99. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/09. Demo - Perform Attribute Selection.mp411.32MB
  100. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/10. Demo - Evaluate a Model Using Cross-validation.mp414.98MB
  101. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/11. Demo - Assign Roles and Perform Attribute Selection.mp416.53MB
  102. 4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/12. Demo - Train a Model with Normalized Data.mp418.55MB
  103. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/01. Introducing JSAT.mp45.31MB
  104. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/02. Demo - Getting Set up with a Maven Project.mp416.06MB
  105. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/03. Demo - Loading and Exploring Data.mp421.14MB
  106. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/04. Demo - Building and Training a Regression Model.mp416.35MB
  107. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/05. Demo - Evaluating a Regression Model.mp412.09MB
  108. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/06. Demo - Training and Evaluating a Ridge Regression Model.mp412.13MB
  109. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/07. Demo - Building and Evaluating a Logistic Regression Classification .mp421.69MB
  110. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/08. Demo - Building and Evaluating a Decision Tree Classification Model.mp46.38MB
  111. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/09. Demo - Performing Clustering and Evaluating Clustering Models.mp421.06MB
  112. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/10. Demo - Serializing and Deserializing Trained Models.mp414.34MB
  113. 4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/11. Demo - Making Predictions Using a Deployed Model.mp415.34MB
  114. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/01. Introducing DJL.mp45.48MB
  115. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/02. Brief Overview of Neural Networks.mp45.11MB
  116. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/03. Demo - Setting up the Maven Project and Dependencies.mp45.27MB
  117. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/04. Demo - Building a Fully Connected Neural Network for Image Classifica.mp413.57MB
  118. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/05. Demo - Training the Image Classification Model.mp48.96MB
  119. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/06. Demo - Performing Predictions Using the Classification Model.mp417.38MB
  120. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/07. Brief Overview of Transfer Learning.mp46.2MB
  121. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/08. Demo - Using a Pretrained Model for Image Classification.mp413.06MB
  122. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/09. Demo - Using a Pretrained Model for Image Segmentation.mp416.06MB
  123. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/10. Introducing Google BERT.mp42.3MB
  124. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/11. Demo - Answering Questions with Google BERT.mp411.74MB
  125. 4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/12. Summary and Further Study.mp42.5MB
  126. 4. Implementing Machine Learning Workflow with RapidMiner/1. Course Overview/1. Course Overview.mp44.03MB
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