Machine Learning with Apache Spark

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This course teaches doing Machine Learning at Scale with the popular Apache Spark framework.

This course is intended for data scientists and software engineers. We assume no previous knowledge of Machine Learning – We teach popular Machine Learning algorithms from scratch.

For each machine learning concept, we first discuss the foundations, its applicability, and limitations. Then we explain the implementation and use, and specific use cases. This is achieved through a combination of about 50% lecture, 50% lab work.

Please note that this course does not cover the in-depth coverage of Math / Stats is behind Machine Learning.

This course is taught using Spark & Python.

Before taking this course, students should have a working knowledge of Apache Spark. If students are new to Apache Spark, we can offer one day of ‘Introduction to Spark’ training. Students need a programming background. Familiarity with Python would be a plus, but not required. No machine learning knowledge is assumed.

3 Days/Lecture & Lab

This course is designed for data scientists and software engineers.

ML Concepts

  • Regressions ?Linear Regression
  • Logistic Regressions
  • Classifications ?SVM
  • Decision Trees
  • Random Forest
  • Clustering (K-Means)
  • Principal Component Analysis (PCA)
  • Recommendations

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