PluralSight - Predictive Analytics Using Apache Spark MLlib on Databricks

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[ CoursePig.com ] PluralSight - Predictive Analytics Using Apache Spark MLlib on Databricks
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 01. Course Overview
    • 01. Course Overview.mp4 (3.5 MB)
    02. Getting Started with Machine Learning with Apache Spark on Databricks
    • 02. Prerequisites and Course Outline.mp4 (3.4 MB)
    • 03. Machine Learning on Apache Spark.mp4 (7.6 MB)
    • 04. Demo-Configuring the Workspace and Setting up a Notebook.mp4 (5.0 MB)
    • 05. Demo-Exploring the Diabetes Dataset.mp4 (7.7 MB)
    • 06. Demo-Standardization and Scaling.mp4 (10.7 MB)
    • 07. Demo-Normalization.mp4 (6.4 MB)
    • 08. Demo-Converting Continuous Values to Categorical Values.mp4 (3.9 MB)
    • 09. Demo-Tokenizing Text Data.mp4 (5.3 MB)
    • 10. Demo-Label Encoding and One-hot Encoding.mp4 (10.1 MB)
    • 11. Demo-Feature Selection.mp4 (11.4 MB)
    03. Performing Regression on Batch Data
    • 12. Quick Overview of Linear Regression.mp4 (6.8 MB)
    • 13. Lasso Ridge and Elastic Net Regression.mp4 (5.9 MB)
    • 14. Demo-Exploring the Life Expectancy Dataset.mp4 (8.1 MB)
    • 15. Demo-Building and Evaluating a Linear Regression Model.mp4 (13.8 MB)
    • 16. Demo-Hyperparameter Tuning.mp4 (9.1 MB)
    • 17. Quick Overview of Ensemble Learning.mp4 (4.8 MB)
    • 18. Averaging and Boosting.mp4 (3.2 MB)
    • 19. Machine Learning Pipelines.mp4 (4.6 MB)
    • 20. Demo-Exploring the CO2 Emissions Dataset.mp4 (7.6 MB)
    • 21. Demo-Random Forest Regression.mp4 (9.8 MB)
    • 22. Demo-Gradient Boosted Tree Regression.mp4 (9.1 MB)
    04. Implementing Classification on Streaming Data
    • 23. Quick Overview of Logistic Regression.mp4 (9.3 MB)
    • 24. Demo-Exploring the Loan Dataset.mp4 (6.9 MB)
    • 25. Demo-Logistic Regression.mp4 (8.4 MB)
    • 26. Demo-Performing Predictions on Streaming Data.mp4 (11.9 MB)
    • 27. Quick Overview of Decision Trees.mp4 (4.1 MB)
    • 28. Demo-Exploring the Bank Marketing Campaign Dataset.mp4 (5.4 MB)
    • 29. Demo-Decision Tree Classifier.mp4 (14.2 MB)
    • 30. Demo-Hyperparameter Tuning with Cross Validation.mp4 (5.1 MB)
    • 31. Summary and Further Study.mp4 (2.1 MB)
    • Bonus Resources.txt (0.3 KB)
    • Exercise Files 02 demos HTML
      • DS_Store (6.0 KB)
      • demo-01-ProcessingNumericFeatures.html (3.0 MB)
      • demo-02-ProcessingCategoricalFeatures.html (10.6 MB)
      • demo-03-FeatureSelection.html (1.5 MB)
      • demo-04-MultipleRegressionWithHyperparameterTuning.html (1.4 MB)
      • demo-05-RandomForestAndGBTRegressor.html (2.8 MB)
      • demo-06-StreamingDataClassification.html (1.1 MB)
      • demo-07-DecisionTreeClassification.html (1.5 MB)
      datasets
      • DS_Store (8.0 KB)
      • amsterdam.csv (1.1 MB)
      • bank.csv (897.4 KB)
      • co2.csv (464.9 KB)
      • diabetes.csv (23.3 KB)
      • life_expectancy.csv (325.6 KB)
      • loan_data
        • DS_Store (6.0 KB)
        • loan_data-1.csv (13.1 KB)
        • loan_data-2.csv (12.4 KB)
        • loan_data-3.csv (9.4 KB)
        • loan_data.csv (211.5 KB)
      • superstore_data.csv (2.2 MB)
        • demo-01-ProcessingNumericFeatures.ipynb (3.2 MB)
        • demo-02-ProcessingCategoricalFeatures.ipynb (4.8 MB)
        • demo-03-FeatureSelection.ipynb (1.3 MB)
        • demo-04-MultipleRegressionWithHyperparameterTuning.ipynb (1.3 MB)
        • demo-05-RandomForestAndGBTRegressor.ipynb (3.5 MB)
        • demo-06-StreamingDataClassification.ipynb (1.3 MB)
        • demo-07-DecisionTreeClassification.ipynb (1.2 MB)
          • getting-started-with-machine-learning-with-apache-spark-on-databricks-slides.pdf (821.4 KB)
          • 03
            • performing-regression-on-batch-data-slides.pdf (1.6 MB)
            04
            • implementing-classification-on-streaming-data-slides.pdf (874.8 KB)

Description

Predictive Analytics Using Apache Spark MLlib on Databricks



https://CoursePig.com

Duration: 1h 57m | Updated: Oct 26, 2021 | Video: 1280x720, 48kHz | 272 MB
Genre: eLearning | Language: English | Level: Advanced
This course will teach you to understand and implement important techniques for predictive analytics such as regression and classification using Apache Spark MLlib APIs on Databricks.

The Spark unified analytics engine is one of the most popular frameworks for big data analytics and processing. Spark offers extremely comprehensive and easy to use APIs for machine learning which you can use to build predictive models for regression and classification and pre-process data to feed into these models.

In this course, Predictive Analytics Using Apache Spark MLlib on Databricks, you will learn to implement machine learning models using Spark ML APIs. First, you will understand the different Spark libraries available for machine learning, the older RDD-based library, and the newer DataFrame based library. You will then explore the range of transformers available in Spark for pre-processing data for machine learning - such as scaling and standardization transformers for numeric data and label encoding and one-hot encoding transformers for categorical data.

Next, you will use linear regression and ensemble models such as random forest and gradient boosted trees to build regression models. You will use these models for prediction on batch data. In addition, you will also see how you can use Spark ML Pipelines to chain together transformers and estimators to build a complete machine learning workflow.



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PluralSight - Predictive Analytics Using Apache Spark MLlib on Databricks


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271.9 MB
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PluralSight - Predictive Analytics Using Apache Spark MLlib on Databricks


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