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In this course, you will learn:
- The essential math that you need to grasp—and implement—in order to write machine learning algorithms in Python.
- Review fundamental algebraic concepts; derivatives and optimization; statistics; and the basics of probability.
Syllabus:
- Introduction
- Explore core mathematical concepts
- Preparing for the labs
1. Equations, Graphs, and Functions
- Getting started with equations
- The distributive property
- Introduction to linear equations
- Intercepts and slope
- Systems of equations
- Exponentials, radicals, and logarithms
- Polynomials
- Polynomial operations
- Factorization
- Factoring squares
- Introduction to quadratic equations
- Functions
- 2. Derivatives and Optimization
- Rates of change
- Introduction to limits
- Continuity
- Finding limits
- Introduction to differentiation
- Differentiability
- Derivative rules and operations
- Using derivatives to analyze functions
- Second-order derivatives
- Optimizing functions
- Multivariate differentiation
- Introduction to integration
3. Vectors and Matrices
- Introduction to vectors
- Vector addition
- Vector multiplication
- Introduction to matrices
- Matrix multiplication
- Identity matrices
- Matrix division
- Solving systems of equations with matrices
- Matrix transformations
- Eigenvalues and eigenvectors
4. Statistics and Probability
- Data
- Visualizing data
- Measures of central tendency
- Measures of variance
- Comparing data
- Probability basics
- Conditional probability and dependence
- Binomial variables and distributions
- Sample and sampling distributions
- Confidence intervals
- Hypothesis testing
