Machine Learning Essentials

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This course introduces popular Machine Learning techniques.

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

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

  • Working knowledge of either R, Python or Apache Spark
  • Programming background
  • No previous machine learning knowledge is assumed

3 Days/Lecture & Lab

This class is designed for Data Scientists and Software Engineers.

  • Machine Learning (ML) Overview
  • Machine Learning Environment
  • Machine Learning Concepts
  • Feature Engineering (FE)
  • Linear regression
  • Logistic Regression
  • Classification: SVM (Supervised Vector Machines)
  • Classification: Decision Trees & Random Forests
  • Classification: Naive Bayes
  • Clustering (K-Means)
  • Principal Component Analysis (PCA)
  • Recommendation (Collaborative filtering)
  • Final workshop (time permitting)




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