Spark Module 3 Machine Learning SparkML

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Spark Module 3 Machine Learning SparkML

Machine Learning for Big Data

The course will take on average 3 days to complete, including practical work

  • Learn the basics of Machine Learning and how to apply to big data with SparkML
  • Supervised vs Unsupervised Learning
  • Linear Regressions
  • Logistic Regressions
  • Decision Trees
  • K-Means Clusters
  • Random Forests
  • Recommender Systems
We assume you're already familiar with Spark Core from modules 1 and 2.

Contents

Having problems? check the errata

Introduction 24m 2s

What is Machine Learning, Supervised vs Unsupervised Learning and the Model Building Process

Preview

Building a Linear Regression 30m 40s

Assembling vectors of features and Model Fitting

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Training Data 26m 33s

Training vs Test and Holdout Data, Using data from Kaggle, RMSE and R2 tests

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Model Fitting Parameters 25m 41s

Setting Linear Regression Parameters

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Feature Selection 36m 23s

Correlation of features, Identifying duplicate features, data preparation

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Non Numeric Data 25m 48s

Using OneHotEncoding and Vectors

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Pipelines 19m 42s

How to build a pipeline in SparkML

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Case Study 34m 51s

A full practical exercise

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Logistic Regression 26m 12s

True and False Negatives and Postives, Coding a Logistic Regression Model

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Decision Trees 46m 21s

Building a decicision tree model, Interpreting a tree and Random Forests

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Unsupervised Learning: K-Means Clustering 10m 49s

K-Means Clustering and how to implement in SparkML

Watch

Recommender Systems 29m 7s

Matrix Factorisation and how to build a model in SparkML

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