Customized Trainings Solutions Pte. Ltd.

At the end of the workshop, Participants will be able to perform:

 

• Differentiate between supervised and unsupervised Machine Learning problems

• Apply various regression and classification models

• Train analytical models with Spark MLlib’s Data Frame-based estimators

• Implement linear regression, decision trees, logistic regression, and k-means.

• Understand purpose of Transformers to perform pre-processing on a dataset prior to training

• Write code to implement Transformers using standardization, normalization, one-hot encoding, and binarization.

• Create a processing pipeline including transformations, estimations, evaluation of analytical models.

• Using Spark Mlib evaluators to Evaluate model accuracy by dividing data into training and test datasets and computing metrics

• Tune training hyper-parameters by integrating cross-validation into Spark MLlib Pipelines.