Mathematics for ML & DS Specialization

Whether you’re a novice or an experienced professional, our curated Machine Learning courses cater to diverse skill levels, providing a comprehensive and hands-on learning experience.

Skills You Will Gain

Vectors and Matrices

Matrix product

Linear Transformations

Rank, Basis, and Span

Eigenvectors and Eigenvalues




Gradient Descent

Gradient Descent in Neural Networks

Newton’s Method


Random Variables

Bayes Theorem

Gaussian Distribution

Variance and Covariance

Sampling and Point Estimates

Maximum Likelihood Estimation

This course includes

Syllabus Overview

Mathematics for ML & DS Specialization

Mastering the Mathematical Foundations: A Comprehensive Course in Mathematics for Machine Learning and Data Science

  • Linear Algebra for Machine Learning and Data Science:

    Week 1: Systems of Linear Equations

    Lesson 1: Systems of Linear equations: two variables

    • Machine learning motivation
    • Systems of sentences
    • Systems of equations
    • Systems of equations as lines
    • A geometric notion of singularity
    • Singular vs nonsingular matrices
    • Linear dependence and independence
    • The determinant

    Lesson 2: Systems of Linear Equations: three variables

    • Systems of equations (3×3)
    • Singular vs non-singular (3×3)
    • Systems of equations as planes (3×3)
    • Linear dependence and independence (3×3)
    • The determinant (3×3)

    Week 2: Solving systems of Linear Equations

    Lesson 1: Solving systems of Linear Equations: Elimination

    • Machine learning motivation
    • Solving non-singular systems of linear equations
    • Solving singular systems of linear equations
    • Solving systems of equations with more variables
    • Matrix row-reduction
    • Row operations that preserve singularity

    Lesson 2: Solving systems of Linear Equations: Row Echelon Form and Rank

    • The rank of a matrix
    • The rank of a matrix in general
    • Row echelon form
    • Row echelon form in general
    • Reduced row echelon form

    Week 3: Vectors and Linear Transformations

    Lesson 1: Vectors

    • Norm of a vector
    • Sum and difference of vectors
    • Distance between vectors
    • Multiplying a vector by a scalar
    • The dot product
    • Geometric Dot Product
    • Multiplying a matrix by a vector
    • Lab: Vector Operations: Scalar Multiplication, Sum and Dot Product of Vectors

    Lesson 2: Linear transformations

    • Matrices as linear transformations
    • Linear transformations as matrices
    • Matrix multiplication
    • The identity matrix
    • Matrix inverse
    • Which matrices have an inverse?
    • Neural networks and matrices

    Week 4: Determinants and Eigenvectors

    Lesson 1: Determinants In-depth

    • Machine Learning Motivation
    • Singularity and rank of linear transformation
    • Determinant as an area
    • Determinant of a product
    • Determinants of inverses

    Lesson 2: Eigenvalues and Eigenvectors

    • Bases in Linear Algebra
    • Span in Linear Algebra
    • Interactive visualization: Linear Span
    • Eigenbases
    • Eigenvalues and eigenvectors
  • Exploring lists, sets, dictionaries, tuples.
  • Classes, objects, inheritance, polymorphism.
  • Try-except blocks, custom exceptions, and best practices.
  • Probability & Statistics for Machine Learning & Data Science:

    Week 7: Introduction to probability and random variables

    Lesson 1: Introduction to probability

    • Concept of probability: repeated random trials
    • Conditional probability and independence 
    • Discriminative learning and conditional probability
    • Bayes theorem 

    Lesson 2: Random variables

    • Random variables
    • Cumulative distribution function
    • Discrete random variables: Bernoulli distribution
    • Discrete random variables: Binomial distribution
    • Probability mass function
    • Continuous random variables: Uniform distribution 
    • Continuous random variables: Gaussian distribution
    • Continuous random variables: Chi squared distribution
    • Probability distribution function

    Week 8-9: Describing distributions and random vectors

    Lesson 1: Describing distributions

    • Measures of central tendency: mean, median, mode
    • Expected values
    • Quantiles and box-plots 
    • Measures of dispersion: variance, standard deviation

    Lesson 2: Random vectors

    • Joint distributions
    • Marginal and conditional distributions
    • Independence
    • Measures of relatedness: covariance
    • Multivariate normal distribution

    Week 10-11: Introduction to statistics

    Lesson 1: Sampling and point estimates

    • Population vs. sample 
    • Describing samples: sample proportion and sample mean
    • Distribution of sample mean and proportion: Central Limit Theorem 
    • Point estimates
    • Biased vs Unbiased estimates 

    Lesson 2: Maximum likelihood estimation

    • ML motivation example: Linear Discriminant Analysis
    • Likelihood
    • Intuition behind maximum likelihood estimation
    • MLE: How to get the maximum using calculus

    Lesson 3: Bayesian statistics 

    • ML motivation example: Naive Bayes
    • Frequentist vs. Bayesian statistics
    • A priori/ a posteriori distributions
    • Bayesian estimators: posterior mean, posterior median, MAP

    Week 12: Interval statistics and Hypothesis testing

    Lesson 1: Confidence intervals

    • Margin of error
    • Interval estimation
    • Confidence Interval for mean of population
    • CI for parameters in linear regression
    • Prediction Interval

    Lesson 2: Hypothesis testing

    • ML Motivation: AB Testing
    • Criminal trial
    • Two types of errors
    • Test for proportion and means
    • Two sample inference for difference between groups 
    • ANOVA
    • Power of a test

Transform Your Skills: Enroll Now to Master Mathematics of ML & DS

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