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Zero To Mastery

Machine Learning with Spark ML

via Zero To Mastery Path

Overview

Learn how to use Spark ML to build scalable machine learning solutions. Get hands-on with regression, classification, feature engineering, model evaluation, hyperparameter tuning and more, plus learn deep learning integration with Apache Spark.
  • Use Spark ML to build scalable machine learning models
  • Apply regression and classification algorithms
  • Apply feature engineering techniques at scale
  • Evaluate model performance with the right metrics
  • Optimize models through hyperparameter tuning
  • Integrate deep learning tools with Spark
  • Handle big data Machine Learning workflows efficiently
  • Build production-ready Machine Learning pipelines

Syllabus

  •   Introduction
    • Introduction: Machine Learning with SparkML
    • What Is Machine Learning?
    • Exercise: Meet Your Classmates and Instructor
    • Course Resources
  •   Setup and Useful Resources
    • [Optional] Using Windows
    • [Optional] UNIX CLI Commands
    • Installing Software for the Course
    • [Optional] What Is a Virtualenv?
  •   Machine Learning with Spark ML: Create a Data Pipeline, Train a Model + more
    • Regression Algorithms
    • Building a Regression Model
    • Training a Model
    • Model Evaluation
    • Testing a Regression Model
    • Model Lifecycle
    • Feature Engineering
    • Improving a Regression Model
    • Machine Learning Pipelines
    • Creating a Pipeline
    • [Exercise] House Price Estimation
    • [Exercise] House Price Estimation - Solution
    • Classification
    • Classifiers Evaluation
    • Training a Classifier
    • Hyperparameters
    • Optimizing a Model
    • [Exercise] Loan Approval
    • [Exercise] Loan Approval - Solution
    • Deep Learning
  •   Where To Go From Here?
    • Let's Keep Learning Together!
    • Review This Byte!

Taught by

Ivan Mushketyk

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