Welcome to ml-breadth’s documentation!

• Notations, Cost Function, Gradient Descent
  • Linear Regression
  • Univariate regression
  • Cost function
  • Minimizing the cost function
  • Gradient Descent
  • Implementation of Gradient Descent
  • Predictions
• Vectorization
  • Vectors
  • Vectors in NumPy
  • Vector Creation
  • Operations on Vectors
  • Vector Vector dot product
  • Matrices
  • Matrices as NumPy Arrays
  • Matrix Creation
  • Operations on Matrices
• Multiple Variable Linear Regression
  • Model Prediction With Multiple Variables
  • Compute Cost With Multiple Variables
  • Gradient Descent With Multiple Variables
• Evaluation in Machine Learning
  • Precision
  • Recall
  • Recall vs Precision
• Common Interview Questions
  • Gradient Descent and Backpropagation
  • Loss Functions
  • Training in Machine Learning
  • Regularization
  • Model Architecture
  • Decision Trees, Random Forest, Gradient Boosting