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种子名称： [CourseClub.Me] Pluralsight - Building Machine Learning Solutions With Java - Learning Paths
文件类型： 视频
文件数目： 126个文件
文件大小： 1.45 GB
收录时间： 2024-3-16 05:29
已经下载： 3次
资源热度： 296
最近下载： 2025-1-1 05:15

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

1. Preparing Data for Machine Learning with Java/1. Course Overview/1. Course Overview.mp44.7MB

1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/1. Introduction.mp414.9MB

1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/2. What Is Data Preparation.mp41.94MB

1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/3. Ingesting CSV and Excel Files.mp410.97MB

1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/4. Ingesting JSON and XML Files.mp47.55MB

1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/5. Demo - Ingestion.mp428.71MB

1. Preparing Data for Machine Learning with Java/2. Ingesting Data from Files in Various Formats/6. Summary.mp41.01MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/1. Introduction.mp412.06MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/2. Folder Monitoring.mp49.63MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/3. Task Scheduling.mp46.11MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/4. Demo - Using the File Watcher API.mp425.96MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/5. Demo - Using the Quartz Scheduler Library.mp424.66MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/6. Selenium.mp45.67MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/7. Demo - Using the Selenium IDE.mp46.55MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/8. Demo - Coding for Selenium.mp426.96MB

1. Preparing Data for Machine Learning with Java/3. Automating Data Collection and Scheduling/9. Summary.mp41.05MB

1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/1. Introduction.mp411.71MB

1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/2. Lambdas and Streams.mp48.77MB

1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/3. Regular Expressions Overview.mp47.47MB

1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/4. Using Regular Expressions in Java.mp48.49MB

1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/5. Demo - Data Cleaning Pipeline.mp419.62MB

1. Preparing Data for Machine Learning with Java/4. Data Cleaning Using Regex and Formatter/6. Summary.mp4640.11KB

1. Preparing Data for Machine Learning with Java/5. Data Transformation/1. Introduction.mp47.55MB

1. Preparing Data for Machine Learning with Java/5. Data Transformation/2. Data Transformation Basics.mp47.45MB

1. Preparing Data for Machine Learning with Java/5. Data Transformation/3. Scaling, Data Skew, and Data Bias.mp49.83MB

1. Preparing Data for Machine Learning with Java/5. Data Transformation/4. Demo - Data Transformation Pipeline.mp434.37MB

1. Preparing Data for Machine Learning with Java/5. Data Transformation/5. Summary.mp4818.19KB

1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/1. Introduction.mp413.88MB

1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/2. Distributed Data Pipelines.mp45.59MB

1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/3. Beam SDK Concepts.mp46.89MB

1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/4. Beam SDK Engines and GCP Dataflow.mp49.45MB

1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/5. Demo - Developing Beam SDK Pipelines.mp434.22MB

1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/6. Demo - Deploying Beam SDK Pipelines to GCP Dataflow.mp413.52MB

1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/7. Summary.mp4954.93KB

1. Preparing Data for Machine Learning with Java/6. Data Preparation at Scale/8. Wrap Up.mp42.8MB

2. Exploring Java Machine Learning Environments/1. Course Overview/1. Course Overview.mp43.85MB

2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/1. Version Check.mp4375.85KB

2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/2. Introduction.mp46.44MB

2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/3. Demo - Weka Showcase.mp410.44MB

2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/4. Demo - Deeplearning4j (DL4J) Showcase.mp414.78MB

2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/5. Demo - Spark MLlib Showcase.mp415.97MB

2. Exploring Java Machine Learning Environments/2. Understanding the Java Machine Learning Ecosystem/6. Summary.mp42.59MB

2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/1. Introduction.mp47.52MB

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

2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/3. Demo - Data Preprocessing with Programmatic Weka.mp44.1MB

2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/4. Demo - Implementing K-means with Programmatic Weka.mp43.61MB

2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/5. Demo - Evaluation and Visualization with Programmatic Weka.mp415.47MB

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

2. Exploring Java Machine Learning Environments/3. Implementing a Machine Learning Workflow with Weka/7. Summary.mp41006.71KB

2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/1. Introduction.mp47.27MB

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

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

2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/4. Demo - Data Preprocessing with DL4J.mp431.98MB

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

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

2. Exploring Java Machine Learning Environments/4. Implementing a Machine Learning Workflow with DL4J/7. Summary.mp41.12MB

2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/1. Introduction.mp48.35MB

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

2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/3. Demo - Data Preprocessing with Spark MLlib.mp418.44MB

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

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

2. Exploring Java Machine Learning Environments/5. Implementing a Machine Learning Workflow with Spark MLlib/6. Summary.mp4943.38KB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/01. Version Check.mp4516.19KB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/02. Prerequisites and Course Outline.mp43.62MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/03. Introducing Weka.mp43.98MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/04. Demo - Environment and Project Setup.mp411.15MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/05. Demo - Exploring the Weka Workbench.mp414.99MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/06. Demo - Loading and Exploring the Dataset.mp414.73MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/07. Demo - Training and Evaluating a Regression Model.mp418.08MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/08. Demo - Training and Evaluating a Multiple Regression Model.mp424.79MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/09. Demo - Feature Selection and Ranking.mp423.08MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/10. Demo - Processing and Saving Processed Data.mp420.27MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/11. Demo - Evaluating a Model Using Cross Validation.mp47.77MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/12. Demo - Regression Using Support Vector Machines and Multilayer Perceptrons.mp411.86MB

3. Implementing Machine Learning Workflow with Weka/02. Implementing Regression Models/13. Demo - Serializing and Visualizing a Decision Tree Model.mp420.87MB

3. Implementing Machine Learning Workflow with Weka/1. Course Overview/1. Course Overview.mp43.97MB

3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/1. Demo - Feature Selection and Data Processing.mp421.16MB

3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/2. Demo - Building and Evaluating a Classification Model.mp422.4MB

3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/3. Demo - Building and Visualizing a Decision Tree Model.mp414.72MB

3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/4. Demo - Encoding Text Data in Numeric Form.mp422.9MB

3. Implementing Machine Learning Workflow with Weka/3. Implementing Classification Models/5. Demo - Performing Classification on Text Data.mp423.74MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/1. Demo - Normalizing and Visualizing Data.mp420.05MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/2. Demo - Performing K-means Clustering.mp412.43MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/3. Demo - Visualizing Cluster Assignments.mp424.84MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/4. Demo - Exploring and Visualizing Data.mp49.96MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/5. Demo - Performing Hierarchical Clustering.mp414.95MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/6. Demo - Performing EM Clustering.mp410.26MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/7. Demo - Serializing Trained Model Parameters.mp412.57MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/8. Demo - Deploying a Model Using SpringBoot.mp421.85MB

3. Implementing Machine Learning Workflow with Weka/4. Implementing Clustering Models/9. Summary and Further Study.mp42.58MB

4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/01. Version Check.mp4546.45KB

4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/02. Prerequisites and Course Outline.mp43.83MB

4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/03. Introducing RapidMiner.mp44.42MB

4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/04. Demo - Download and Setup RapidMiner.mp410.4MB

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

4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/06. Demo - Exploring the Dataset.mp419.09MB

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

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

4. Implementing Machine Learning Workflow with RapidMiner/02. Implementing Machine Learning Models with RapidMiner Studio/09. Demo - Perform Attribute Selection.mp411.32MB

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

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

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

4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/01. Introducing JSAT.mp45.31MB

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

4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/03. Demo - Loading and Exploring Data.mp421.14MB

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

4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/05. Demo - Evaluating a Regression Model.mp412.09MB

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

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

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

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

4. Implementing Machine Learning Workflow with RapidMiner/03. Using JSAT to Implement Machine Learning Models/10. Demo - Serializing and Deserializing Trained Models.mp414.34MB

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

4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/01. Introducing DJL.mp45.48MB

4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/02. Brief Overview of Neural Networks.mp45.11MB

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

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

4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/05. Demo - Training the Image Classification Model.mp48.96MB

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

4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/07. Brief Overview of Transfer Learning.mp46.2MB

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

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

4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/10. Introducing Google BERT.mp42.3MB

4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/11. Demo - Answering Questions with Google BERT.mp411.74MB

4. Implementing Machine Learning Workflow with RapidMiner/04. Using DJL to Implement Machine Learning Models/12. Summary and Further Study.mp42.5MB

4. Implementing Machine Learning Workflow with RapidMiner/1. Course Overview/1. Course Overview.mp44.03MB