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Training/Webinars/Workshops: Applied Machine Learning: Foundations



Applied Machine Learning: Foundations

Course details

  • 2h 38m Beginner + Intermediate Released: 5/10/2019

Anyone who can write basic Python is capable of fitting a simple machine learning model on a clean dataset. The competitive edge comes in the ability to customize and optimize those models for specific problems. The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem specific. In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem.

Learning objectives

  • What is machine learning (ML)?
  • ML vs. deep learning vs. AI
  • Handling common challenges in ML
  • Plotting continuous features
  • Continuous and categorical data cleaning
  • Measuring success
  • Overfitting and underfitting
  • Tuning hyperparameters
  • Evaluating a model