Navigating PySpark MLlib Essentials
Explore PySpark MLlib and develop essential machine learning skills. Prepare datasets, train models, make predictions, and evaluate performance, gaining confidence in deploying models with PySpark's powerful MLlib capabilities.
Lessons and practices
Lesson 1: Preparing Dataset with MLlib
Complete the Data Preprocessing
Adjust Dataset Split Ratio
Fixing PySpark Preprocessing Issues
Convert Categorical Labels with StringIndexer
Master Feature Vectorization with MLlib
Lesson 2: Training a Classification Model with MLlib
Train a Model with PySpark
Fix Mistakes in Model Training
Complete PySpark Model Training
Switch Models in PySpark
Lesson 3: Making Predictions and Evaluating Model Performance
Complete the Model Evaluation
Switch Metric to Evaluate Model
Debugging Model Evaluation Code
Implement Model Evaluation
Lesson 4: Saving and Loading Trained MLlib Models
Complete Model Persistence with PySpark
Fix the Model Persistence Error
Saving Your Model Efficiently
Master Model Persistence with PySpark
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