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- Level: Advanced
- Duration: 01h 57m 08s
- Release date: 2021-10-26
- Author: Janani Ravi
- Provider: Pluralsight
Predictive Analytics Using Apache Spark MLlib on Databricks
Description
Content
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.
- Course Overview02m
- Course Overview02m
- Getting Started with Machine Learning with Apache Spark on Databricks38m
- Prerequisites and Course Outline02m
- Machine Learning on Apache Spark05m
- Demo: Configuring the Workspace and Setting up a Notebook03m
- Demo: Exploring the Diabetes Dataset04m
- Demo: Standardization and Scaling05m
- Demo: Normalization03m
- Demo: Converting Continuous Values to Categorical Values02m
- Demo: Tokenizing Text Data03m
- Demo: Label Encoding and One-hot Encoding05m
- Demo: Feature Selection06m
- Performing Regression on Batch Data45m
- Quick Overview of Linear Regression05m
- Lasso Ridge and Elastic Net Regression04m
- Demo: Exploring the Life Expectancy Dataset04m
- Demo: Building and Evaluating a Linear Regression Model06m
- Demo: Hyperparameter Tuning04m
- Quick Overview of Ensemble Learning03m
- Averaging and Boosting02m
- Machine Learning Pipelines03m
- Demo: Exploring the CO2 Emissions Dataset04m
- Demo: Random Forest Regression05m
- Demo: Gradient Boosted Tree Regression05m
- Implementing Classification on Streaming Data36m
- Quick Overview of Logistic Regression06m
- Demo: Exploring the Loan Dataset03m
- Demo: Logistic Regression04m
- Demo: Performing Predictions on Streaming Data05m
- Quick Overview of Decision Trees03m
- Demo: Exploring the Bank Marketing Campaign Dataset03m
- Demo: Decision Tree Classifier07m
- Demo: Hyperparameter Tuning with Cross Validation03m
- Summary and Further Study02m
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