Machine Learning
Ctrlk
  • Introduction
  • Self LEarning
  • Why Statstics in ML Or Data Science
  • How important is interpretability for a model in Machine Learning?
  • What are the most important machine learning techniques to master at this time?
  • Learning
  • Steps in machine learning model development and deployment
  • Statistical fundamentals and terminology
  • Statistics
  • Spark MLib
    • Data Types
    • Comparing algorithms supported by MLlib
      • Classification
    • When and why should you use MLlib (versus scikit-learn versus TensorFlow versus foo package)
    • Pipeline
    • References
    • Linear algebra in Spark
  • Terminology
  • Machine Learning Steps
  • Preprocessing and Feature selection techniues
  • The importance of variables feature selection/attribute selection
  • Feature engineering
  • Hyperplanes
  • cross-validation
  • Machine learning losses
  • When to stop tuning machine learning models
  • Train, validation, and test data
  • input data structure
  • Why are matrices/vectors used in machine learning/data analysis?
  • OverView
  • Data scaling and normalization
  • Questions
  • Which machine learning algorithm should I use?
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  1. Spark MLib
  2. Comparing algorithms supported by MLlib

Classification

https://www.safaribooksonline.com/library/view/machine-learning-with/9781785889936/de8a04c5-b741-46d2-8c89-c4741340b6bb.xhtml

PreviousComparing algorithms supported by MLlibNextWhen and why should you use MLlib (versus scikit-learn versus TensorFlow versus foo package)

Last updated 6 years ago

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