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Pluralsight | Building Machine Learning Solutions With Java - Learning Paths

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Pluralsight | Building Machine Learning Solutions With Java - Learning Paths (Size: 1.56 GB)
  0. Websites you may like
  1. OneHack.us Premium Cracked Accounts-Tutorials-Guides-Articles Community Based Forum.url 377 B
  2. FTUApps.com Download Cracked Developers Applications For Free.url 239 B
  1. Preparing Data for Machine Learning with Java
  1. Course Overview
  1. Course Overview.mp4 4.7 MB
  1. Course Overview.vtt 1.7 KB
  2. Ingesting Data from Files in Various Formats
  1. Introduction.mp4 14.9 MB
  1. Introduction.vtt 3.8 KB
  2. What Is Data Preparation.mp4 1.94 MB
  2. What Is Data Preparation.vtt 2.31 KB
  3. Ingesting CSV and Excel Files.mp4 10.97 MB
  3. Ingesting CSV and Excel Files.vtt 8.52 KB
  4. Ingesting JSON and XML Files.mp4 7.55 MB
  4. Ingesting JSON and XML Files.vtt 5.56 KB
  5. Demo - Ingestion.mp4 28.71 MB
  5. Demo - Ingestion.vtt 8.23 KB
  6. Summary.mp4 1.01 MB
  6. Summary.vtt 1.21 KB
  3. Automating Data Collection and Scheduling
  1. Introduction.mp4 12.06 MB
  1. Introduction.vtt 3.11 KB
  2. Folder Monitoring.mp4 9.63 MB
  2. Folder Monitoring.vtt 9.02 KB
  3. Task Scheduling.mp4 6.11 MB
  3. Task Scheduling.vtt 4.73 KB
  4. Demo - Using the File Watcher API.mp4 25.96 MB
  4. Demo - Using the File Watcher API.vtt 7.99 KB
  5. Demo - Using the Quartz Scheduler Library.mp4 24.66 MB
  5. Demo - Using the Quartz Scheduler Library.vtt 6.84 KB
  6. Selenium.mp4 5.67 MB
  6. Selenium.vtt 4.96 KB
  7. Demo - Using the Selenium IDE.mp4 6.55 MB
  7. Demo - Using the Selenium IDE.vtt 2.49 KB
  8. Demo - Coding for Selenium.mp4 26.96 MB
  8. Demo - Coding for Selenium.vtt 7.52 KB
  9. Summary.mp4 1.05 MB
  9. Summary.vtt 1.17 KB
  4. Data Cleaning Using Regex and Formatter
  1. Introduction.mp4 11.71 MB
  1. Introduction.vtt 2.92 KB
  2. Lambdas and Streams.mp4 8.77 MB
  2. Lambdas and Streams.vtt 8.74 KB
  3. Regular Expressions Overview.mp4 7.47 MB
  3. Regular Expressions Overview.vtt 5.2 KB
  4. Using Regular Expressions in Java.mp4 8.49 MB
  4. Using Regular Expressions in Java.vtt 7.22 KB
  5. Demo - Data Cleaning Pipeline.mp4 19.62 MB
  5. Demo - Data Cleaning Pipeline.vtt 7.53 KB
  6. Summary.mp4 640.11 KB
  6. Summary.vtt 585 B
  5. Data Transformation
  1. Introduction.mp4 7.55 MB
  1. Introduction.vtt 2.05 KB
  2. Data Transformation Basics.mp4 7.45 MB
  2. Data Transformation Basics.vtt 6.93 KB
  3. Scaling, Data Skew, and Data Bias.mp4 9.83 MB
  3. Scaling, Data Skew, and Data Bias.vtt 9.39 KB
  4. Demo - Data Transformation Pipeline.mp4 34.37 MB
  4. Demo - Data Transformation Pipeline.vtt 9.72 KB
  5. Summary.mp4 818.19 KB
  5. Summary.vtt 838 B
  6. Data Preparation at Scale
  1. Introduction.mp4 13.88 MB
  1. Introduction.vtt 3.54 KB
  2. Distributed Data Pipelines.mp4 5.59 MB
  2. Distributed Data Pipelines.vtt 5.15 KB
  3. Beam SDK Concepts.mp4 6.89 MB
  3. Beam SDK Concepts.vtt 5.52 KB
  4. Beam SDK Engines and GCP Dataflow.mp4 9.45 MB
  4. Beam SDK Engines and GCP Dataflow.vtt 9.01 KB
  5. Demo - Developing Beam SDK Pipelines.mp4 34.22 MB
  5. Demo - Developing Beam SDK Pipelines.vtt 8.43 KB
  6. Demo - Deploying Beam SDK Pipelines to GCP Dataflow.mp4 13.52 MB
  6. Demo - Deploying Beam SDK Pipelines to GCP Dataflow.vtt 6.22 KB
  7. Summary.mp4 954.93 KB
  7. Summary.vtt 880 B
  8. Wrap Up.mp4 2.8 MB
  8. Wrap Up.vtt 639 B
  exercise.zip 25.99 MB
  playlist.m3u 2.29 KB
  2. Exploring Java Machine Learning Environments
  1. Course Overview
  1. Course Overview.mp4 3.85 MB
  1. Course Overview.vtt 2.38 KB
  2. Understanding the Java Machine Learning Ecosystem
  1. Version Check.mp4 375.85 KB
  1. Version Check.vtt 7 B
  2. Introduction.mp4 6.44 MB
  2. Introduction.vtt 5.4 KB
  3. Demo - Weka Showcase.mp4 10.44 MB
  3. Demo - Weka Showcase.vtt 5.56 KB
  4. Demo - Deeplearning4j (DL4J) Showcase.mp4 14.78 MB
  4. Demo - Deeplearning4j (DL4J) Showcase.vtt 4.97 KB
  5. Demo - Spark MLlib Showcase.mp4 15.97 MB
  5. Demo - Spark MLlib Showcase.vtt 4.33 KB
  6. Summary.mp4 2.59 MB
  6. Summary.vtt 2.59 KB
  3. Implementing a Machine Learning Workflow with Weka
  1. Introduction.mp4 7.52 MB
  1. Introduction.vtt 5.48 KB
  2. Demo - Data Preparation and Loading with Programmatic Weka.mp4 10.43 MB
  2. Demo - Data Preparation and Loading with Programmatic Weka.vtt 4.03 KB
  3. Demo - Data Preprocessing with Programmatic Weka.mp4 4.1 MB
  3. Demo - Data Preprocessing with Programmatic Weka.vtt 1.7 KB
  4. Demo - Implementing K-means with Programmatic Weka.mp4 3.61 MB
  4. Demo - Implementing K-means with Programmatic Weka.vtt 1.37 KB
  5. Demo - Evaluation and Visualization with Programmatic Weka.mp4 15.47 MB
  5. Demo - Evaluation and Visualization with Programmatic Weka.vtt 4.74 KB
  6. Demo - The Full Workflow in One Go with Weka GUI.mp4 10.88 MB
  6. Demo - The Full Workflow in One Go with Weka GUI.vtt 5.43 KB
  7. Summary.mp4 1006.71 KB
  7. Summary.vtt 880 B
  4. Implementing a Machine Learning Workflow with DL4J
  1. Introduction.mp4 7.27 MB
  1. Introduction.vtt 6.55 KB
  2. Demo - Data Preparation and Loading with DL4J (Part 1 - Setup).mp4 12.16 MB
  2. Demo - Data Preparation and Loading with DL4J (Part 1 - Setup).vtt 5.19 KB
  3. Demo - Data Preparation and Loading with DL4J (Part 2 - DL4J DataSetIt.mp4 28.14 MB
  3. Demo - Data Preparation and Loading with DL4J (Part 2 - DL4J DataSetIt.vtt 8.09 KB
  4. Demo - Data Preprocessing with DL4J.mp4 31.98 MB
  4. Demo - Data Preprocessing with DL4J.vtt 9.94 KB
  5. Demo - Implementing a Twitter Sentiment Classifier with DL4J.mp4 20.44 MB
  5. Demo - Implementing a Twitter Sentiment Classifier with DL4J.vtt 5.85 KB
  6. Demo - Performance and Evaluation and Visualization with DL4J.mp4 6.58 MB
  6. Demo - Performance and Evaluation and Visualization with DL4J.vtt 2.52 KB
  7. Summary.mp4 1.12 MB
  7. Summary.vtt 1.04 KB
  5. Implementing a Machine Learning Workflow with Spark MLlib
  1. Introduction.mp4 8.35 MB
  1. Introduction.vtt 5.17 KB
  2. Demo - Data Preparation and Loading with Spark MLlib.mp4 12.09 MB
  2. Demo - Data Preparation and Loading with Spark MLlib.vtt 5.86 KB
  3. Demo - Data Preprocessing with Spark MLlib.mp4 18.44 MB
  3. Demo - Data Preprocessing with Spark MLlib.vtt 6.94 KB
  4. Demo - Implementing an Image Classifier with Spark MLlib.mp4 5.68 MB
  4. Demo - Implementing an Image Classifier with Spark MLlib.vtt 2.5 KB
  5. Demo - Performance and Evaluation and Visualization with Spark .mp4 7.48 MB
  5. Demo - Performance and Evaluation and Visualization with Spark .vtt 2.49 KB
  6. Summary.mp4 943.38 KB
  6. Summary.vtt 804 B
  exercise.zip 82.31 MB
  playlist.m3u 2.58 KB
  3. Implementing Machine Learning Workflow with Weka
  02. Implementing Regression Models
  01. Version Check.mp4 516.19 KB
  01. Version Check.vtt 7 B
  02. Prerequisites and Course Outline.mp4 3.62 MB
  02. Prerequisites and Course Outline.vtt 3.29 KB
  03. Introducing Weka.mp4 3.98 MB
  03. Introducing Weka.vtt 3.28 KB
  04. Demo - Environment and Project Setup.mp4 11.15 MB
  04. Demo - Environment and Project Setup.vtt 5.6 KB
  05. Demo - Exploring the Weka Workbench.mp4 14.99 MB
  05. Demo - Exploring the Weka Workbench.vtt 7.26 KB
  06. Demo - Loading and Exploring the Dataset.mp4 14.73 MB
  06. Demo - Loading and Exploring the Dataset.vtt 5.25 KB
  07. Demo - Training and Evaluating a Regression Model.mp4 18.08 MB
  07. Demo - Training and Evaluating a Regression Model.vtt 6.98 KB
  08. Demo - Training and Evaluating a Multiple Regression Model.mp4 24.79 MB
  08. Demo - Training and Evaluating a Multiple Regression Model.vtt 6.93 KB
  09. Demo - Feature Selection and Ranking.mp4 23.08 MB
  09. Demo - Feature Selection and Ranking.vtt 9.16 KB
  10. Demo - Processing and Saving Processed Data.mp4 20.27 MB
  10. Demo - Processing and Saving Processed Data.vtt 6.12 KB
  11. Demo - Evaluating a Model Using Cross Validation.mp4 7.77 MB
  11. Demo - Evaluating a Model Using Cross Validation.vtt 2.63 KB
  12. Demo - Regression Using Support Vector Machines and Multilayer Perceptrons.mp4 11.86 MB
  12. Demo - Regression Using Support Vector Machines and Multilayer Perceptrons.vtt 5.01 KB
  13. Demo - Serializing and Visualizing a Decision Tree Model.mp4 20.87 MB
  13. Demo - Serializing and Visualizing a Decision Tree Model.vtt 8.67 KB
  1. Course Overview
  1. Course Overview.mp4 3.97 MB
  1. Course Overview.vtt 3.08 KB
  3. Implementing Classification Models
  1. Demo - Feature Selection and Data Processing.mp4 21.16 MB
  1. Demo - Feature Selection and Data Processing.vtt 8.9 KB
  2. Demo - Building and Evaluating a Classification Model.mp4 22.4 MB
  2. Demo - Building and Evaluating a Classification Model.vtt 8.23 KB
  3. Demo - Building and Visualizing a Decision Tree Model.mp4 14.72 MB
  3. Demo - Building and Visualizing a Decision Tree Model.vtt 5.11 KB
  4. Demo - Encoding Text Data in Numeric Form.mp4 22.9 MB
  4. Demo - Encoding Text Data in Numeric Form.vtt 7.08 KB
  5. Demo - Performing Classification on Text Data.mp4 23.74 MB
  5. Demo - Performing Classification on Text Data.vtt 9.82 KB
  4. Implementing Clustering Models
  1. Demo - Normalizing and Visualizing Data.mp4 20.05 MB
  1. Demo - Normalizing and Visualizing Data.vtt 8.54 KB
  2. Demo - Performing K-means Clustering.mp4 12.43 MB
  2. Demo - Performing K-means Clustering.vtt 5.22 KB
  3. Demo - Visualizing Cluster Assignments.mp4 24.84 MB
  3. Demo - Visualizing Cluster Assignments.vtt 7.82 KB
  4. Demo - Exploring and Visualizing Data.mp4 9.96 MB
  4. Demo - Exploring and Visualizing Data.vtt 4.62 KB
  5. Demo - Performing Hierarchical Clustering.mp4 14.95 MB
  5. Demo - Performing Hierarchical Clustering.vtt 6.88 KB
  6. Demo - Performing EM Clustering.mp4 10.26 MB
  6. Demo - Performing EM Clustering.vtt 5.01 KB
  7. Demo - Serializing Trained Model Parameters.mp4 12.57 MB
  7. Demo - Serializing Trained Model Parameters.vtt 4.53 KB
  8. Demo - Deploying a Model Using SpringBoot.mp4 21.85 MB
  8. Demo - Deploying a Model Using SpringBoot.vtt 9.29 KB
  9. Summary and Further Study.mp4 2.58 MB
  9. Summary and Further Study.vtt 2.47 KB
  exercise.zip 2.19 MB
  playlist.m3u 2.27 KB
  4. Implementing Machine Learning Workflow with RapidMiner
  02. Implementing Machine Learning Models with RapidMiner Studio
  01. Version Check.mp4 546.45 KB
  01. Version Check.vtt 7 B
  02. Prerequisites and Course Outline.mp4 3.83 MB
  02. Prerequisites and Course Outline.vtt 3.49 KB
  03. Introducing RapidMiner.mp4 4.42 MB
  03. Introducing RapidMiner.vtt 3.84 KB
  04. Demo - Download and Setup RapidMiner.mp4 10.4 MB
  04. Demo - Download and Setup RapidMiner.vtt 5.19 KB
  05. Demo - Setting up a Repository and Importing Data.mp4 12.74 MB
  05. Demo - Setting up a Repository and Importing Data.vtt 5.74 KB
  06. Demo - Exploring the Dataset.mp4 19.09 MB
  06. Demo - Exploring the Dataset.vtt 9.92 KB
  07. Demo - Build and Evaluate a Linear Regression Model.mp4 15.66 MB
  07. Demo - Build and Evaluate a Linear Regression Model.vtt 7.64 KB
  08. Demo - Train Model on Training Data and Evaluate Using T.mp4 10.33 MB
  08. Demo - Train Model on Training Data and Evaluate Using T.vtt 4.86 KB
  09. Demo - Perform Attribute Selection.mp4 11.32 MB
  09. Demo - Perform Attribute Selection.vtt 5.36 KB
  10. Demo - Evaluate a Model Using Cross-validation.mp4 14.98 MB
  10. Demo - Evaluate a Model Using Cross-validation.vtt 6.34 KB
  11. Demo - Assign Roles and Perform Attribute Selection.mp4 16.53 MB
  11. Demo - Assign Roles and Perform Attribute Selection.vtt 7.37 KB
  12. Demo - Train a Model with Normalized Data.mp4 18.55 MB
  12. Demo - Train a Model with Normalized Data.vtt 8.86 KB
  03. Using JSAT to Implement Machine Learning Models
  01. Introducing JSAT.mp4 5.31 MB
  01. Introducing JSAT.vtt 4.38 KB
  02. Demo - Getting Set up with a Maven Project.mp4 16.06 MB
  02. Demo - Getting Set up with a Maven Project.vtt 5.76 KB
  03. Demo - Loading and Exploring Data.mp4 21.14 MB
  03. Demo - Loading and Exploring Data.vtt 7.02 KB
  04. Demo - Building and Training a Regression Model.mp4 16.35 MB
  04. Demo - Building and Training a Regression Model.vtt 6.23 KB
  05. Demo - Evaluating a Regression Model.mp4 12.09 MB
  05. Demo - Evaluating a Regression Model.vtt 4.82 KB
  06. Demo - Training and Evaluating a Ridge Regression Model.mp4 12.13 MB
  06. Demo - Training and Evaluating a Ridge Regression Model.vtt 5.09 KB
  07. Demo - Building and Evaluating a Logistic Regression Classification .mp4 21.69 MB
  07. Demo - Building and Evaluating a Logistic Regression Classification .vtt 8.12 KB
  08. Demo - Building and Evaluating a Decision Tree Classification Model.mp4 6.38 MB
  08. Demo - Building and Evaluating a Decision Tree Classification Model.vtt 2.54 KB
  09. Demo - Performing Clustering and Evaluating Clustering Models.mp4 21.06 MB
  09. Demo - Performing Clustering and Evaluating Clustering Models.vtt 8.38 KB
  10. Demo - Serializing and Deserializing Trained Models.mp4 14.34 MB
  10. Demo - Serializing and Deserializing Trained Models.vtt 5.43 KB
  11. Demo - Making Predictions Using a Deployed Model.mp4 15.34 MB
  11. Demo - Making Predictions Using a Deployed Model.vtt 8.27 KB
  04. Using DJL to Implement Machine Learning Models
  01. Introducing DJL.mp4 5.48 MB
  01. Introducing DJL.vtt 4.49 KB
  02. Brief Overview of Neural Networks.mp4 5.11 MB
  02. Brief Overview of Neural Networks.vtt 4.33 KB
  03. Demo - Setting up the Maven Project and Dependencies.mp4 5.27 MB
  03. Demo - Setting up the Maven Project and Dependencies.vtt 2.32 KB
  04. Demo - Building a Fully Connected Neural Network for Image Classifica.mp4 13.57 MB
  04. Demo - Building a Fully Connected Neural Network for Image Classifica.vtt 8.46 KB
  05. Demo - Training the Image Classification Model.mp4 8.96 MB
  05. Demo - Training the Image Classification Model.vtt 4.69 KB
  06. Demo - Performing Predictions Using the Classification Model.mp4 17.38 MB
  06. Demo - Performing Predictions Using the Classification Model.vtt 8.82 KB
  07. Brief Overview of Transfer Learning.mp4 6.2 MB
  07. Brief Overview of Transfer Learning.vtt 4.61 KB
  08. Demo - Using a Pretrained Model for Image Classification.mp4 13.06 MB
  08. Demo - Using a Pretrained Model for Image Classification.vtt 6.04 KB
  09. Demo - Using a Pretrained Model for Image Segmentation.mp4 16.06 MB
  09. Demo - Using a Pretrained Model for Image Segmentation.vtt 6.88 KB
  10. Introducing Google BERT.mp4 2.3 MB
  10. Introducing Google BERT.vtt 2.16 KB
  11. Demo - Answering Questions with Google BERT.mp4 11.74 MB
  11. Demo - Answering Questions with Google BERT.vtt 5.04 KB
  12. Summary and Further Study.mp4 2.5 MB
  12. Summary and Further Study.vtt 2.35 KB
  1. Course Overview
  1. Course Overview.mp4 4.03 MB
  1. Course Overview.vtt 3.1 KB
  exercise.zip 4.39 MB
  playlist.m3u 3.73 KB
  FreeCoursesOnline.me Download Udacity, Masterclass, Lynda, PHLearn, etc Free.url 290 B

Description


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Pluralsight - Building Machine Learning Solutions With Java - Learning Paths [FCO]

About

Machine learning has transformed our world, initiating new approaches to software development and applications. As a Java Developer to stay ahead of the upcoming changes we can start with implementing simple machine learning solutions. TensorFlow , Hadoop ,Spark and Kafka provide Java API’s . This path will explore a few useful resources for Java Developers to get started with Machine Learning.

- Exploring Java Machine Learning Environments
- Implementing Machine Learning workflow with RapidMiner
- Implementing Machine Learning workflow with Weka
- Preparing Data for Machine Learning with Java

Courses in this path

1. Preparing Data for Machine Learning with Java
2. Exploring Java Machine Learning Environments
3. Implementing Machine Learning Workflow with Weka
4. Implementing Machine Learning Workflow with RapidMiner

Experience

- Java Fundamentals
- Data Analytics Literacy
- Statistics
- Machine Learning Literacy

General Info:

Author(s): Federico Mestrone, Nicolae Caprarescu, Janani Ravi
Language: English
Updated: 2023
Videos Duration: 8 hours
Course Source: https://www.pluralsight.com/paths/building-machine-learning-solutions-with-java

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