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The Math Required for Machine Learning

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Brief Description: Before discussing the 4 math skills needed in machine learning, let’s first of all describe the machine learning process. The machine learning process includes 4 main stages:

Most of the math skills you need for building a machine learning model are used in stages 2, 3, and 4, which is Data Analysis, Model Building, and Application.

The 4 Math Skills for Machine Learning
Statistics and Probability

Statistics and Probability is used for visualization of features, data pre-processing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with:

  • Mean
  • Median
  • Mode
  • Standard deviation/variance
  • Correlation coefficient and the covariance matrix
  • Correlation coefficient and the covariance matrix
  • Probability distributions (Binomial, Poisson, Normal)
  • p-value
  • Baye’s Theorem (Precision, Recall, Positive Predictive Value, Negative Predictive Value, Confusion Matrix, ROC Curve)
  • A/B Testing
  • Monte Carlo Simulation
Multivariable Calculus

Most machine learning models are built with a data set having several features or predictors. Hence familiarity with multivariable calculus is extremely important for building a machine learning model. Here are the topics you need to be familiar with:

  • Functions of several variables
  • Derivatives and gradients
  • Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function
  • Cost function
  • Plotting of functions
  • Minimum and Maximum values of a function
Linear Algebra

Linear algebra is the most important math skill in machine learning. A data set is represented as a matrix. Linear algebra is used in data pre-processing, data transformation, and model evaluation. Here are the topics you need to be familiar with:

  • Vectors
  • Matrices
  • Transpose of a matrix
  • The inverse of a matrix
  • The determinant of a matrix
  • Dot product
  • Eigenvalues
  • Eigenvectors
Optimization Methods

Most machine learning algorithms perform predictive modelling by minimizing an objective function, thereby learning the weights that must be applied to the testing data in order to obtain the predicted labels. Here are the topics you need to be familiar with:

  • Cost function/Objective function
  • Likelihood function
  • Error function
  • Gradient Descent Algorithm and its variants (e.g. Stochastic Gradient Descent Algorithm)