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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
Contents
Having problems? check the errata
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Introduction 24m 2s What is Machine Learning, Supervised vs Unsupervised Learning and the Model Building Process |
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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 |
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Recommender Systems 29m 7s Matrix Factorisation and how to build a model in SparkML |
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