Want to understand and resolve Machine Learning problems?
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.