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.

Topics include:

  • 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


  • 英文名称:Applied Machine Learning: Foundations
  • 时长:2小时38分
  • 字幕:英语


  1. Leveraging machine learning
  2. What you should know
  3. What tools you need
  4. Using the exercise files
  5. What is machine learning?
  6. What kind of problems can this help you solve?
  7. Why Python?
  8. Machine learning vs. Deep learning vs. Artificial intelligence
  9. Demos of machine learning in real life
  10. Common challenges
  11. Why do we need to explore and clean our data?
  12. Exploring continuous features
  13. Plotting continuous features
  14. Continuous data cleaning
  15. Exploring categorical features
  16. Plotting categorical features
  17. Categorical data cleaning
  18. Why do we split up our data?
  19. Split data for train/validation/test set
  20. What is cross-validation?
  21. Establish an evaluation framework
  22. Bias/Variance tradeoff
  23. What is underfitting?
  24. What is overfitting?
  25. Finding the optimal tradeoff
  26. Hyperparameter tuning
  27. Regularization
  28. Overview of the process
  29. Clean continuous features
  30. Clean categorical features
  31. Split data into train/validation/test set
  32. Fit a basic model using cross-validation
  33. Tune hyperparameters
  34. Evaluate results on validation set
  35. Final model selection and evaluation on test set
  36. Next steps