Online-resources.md

• Accuracy, Precision, Recall or F1?

  • Calculating precision and recall for a model
  • Distinguishing the model accuracy on precision and recall

• One-Hot-Encoding, Multicollinearity and the Dummy Variable Trap

  • Defining One-Hot-Encoding and Multicollinearity
  • Avoiding dummy variable trap

• Diving Deep with Imbalanced Data

  • What is an imbalanced dataset
  • Techniques to deal with an imbalanced dataset

• Handling imbalanced datasets in machine learning

  • Detecting a naive behaviour
  • Undersampling, oversampling and generating synthetic data

• How to Handle Imbalanced Classes in Machine Learning

  • Effective ways to handle imbalanced classes
  • Changing the performance metric from accuracy to precision, recall, etc.

• Upsampling with SMOTE for Classification Projects

  • Difference between upsampling and SMOTE
  • Parameters to be used in SMOTE

• Cost-Sensitive Logistic Regression for Imbalanced Classification

  • How standard logistic regression does not support imbalanced classification.
  • How logistic regression can be modified to weight model error by class weight when fitting the coefficients.
  • How to configure class weight for logistic regression and how to grid search different class weight configurations.

• Rescaling Data for Machine Learning in Python with Scikit-Learn

  • Data Rescaling, Data Normalization, and Data Standardization
  • When to use Normalization and when to use Standardization

• How to customize Seaborn Correlation Heatmaps

  • Adjusting figure size, value of the color scale, etc.
  • Showing the value of earson correlation coefficiens

• 20 Machine Learning Interview Questions You Must Know for Data Scientists

  • Some important machine learning and data science interview questions
  • What is PCA, ensemble learning, etc.