# Mathematics For Machine Learning Course (FREE)

## Course Instructor:

Fabio Mardero is a data scientist from Italy. He graduated in physics and statistical and actuarial sciences. He is currently working at a well-known Italian insurance company as a data scientist and Non-Life technical provisions evaluator.

## Course Overview & Lectures

Duration: 12+ hours

## Linear Algebra and Mathematical Foundation:Maths For ML Series Part 1

Linear Algebra and Mathematical Foundation: This course covers machine learning key elements, vector space, matrices, linear independence and basis and linear maps.

Lecture 1: 01 01 Intro (1 min)

Lecture 2: 01 02 Machine learning Basics (14 mins)

Lecture 3: 01 03 Vector Spaces (13 mins)

Lecture 4: 01 04 Matrices (31 mins)

Lecture 5: 01 05 Linear Independence and Basis (23 mins)

Lecture 6: 01 06 Linear Maps (40 mins)

Total Time: ~ 2 hour

## Analytic Geometry:Maths For ML Series Part 2

Analytic Geometry: This course covers Lengths and Distances, Angles and Orthogonality, Orthogonal Projections and Rotations.

Lecture 1: 02 01 Length Distance (25 mins)

Lecture 2: 02 02 Angles (28 mins)

Lecture 3: 02 03 Projections (23 mins)

Lecture 4: 02 04 Rotations (15 mins)

Total Time: 1 hour 30 minutes

## Matrix Decomposition:Maths For ML Series Part 3

Matrix Decomposition: This course covers Matrix Determinant, Eigenvalues and Eigenvectors, Cholesky Decomposition and Eigen decomposition, and Singular Value Decomposition

Lecture 1: 03 01 Determinant (30 min)

Lecture 2: 03 02 Eigenvalues Eigenvectors (20 min)

Lecture 3: 03 03 Cholesky Decomposition and Eigendecomposition (17 min)

Lecture 4: 03 04 SVD (Singular Value Decomposition) (17 min)

Total Time: 1 hour 20 mins

## Vector Calculus:Maths For ML Series Part 4

Vector Calculus: This course covers Topology, Differentiation, Approximations and Automatic Differentiation and Integration.

Lecture 1: 04 01 Topology (1 hour)

Lecture 2: 04 02 Differentiation (40 min)

Lecture 3: 04 03 Approximations and Automatic Differentiation (15 min)

Lecture 4: 04 04 Integration (27 min)

Total Time: 2 hour 20 mins

## Statistics:Maths For ML Series Part 5

Statistics: This course covers Measure Theory, Probability and Distributions, Statistical Inference and Data Science Tools

Lecture 1: 05 01 Measure Theory (55 min)

Lecture 2: 05 02 Probability Distributions (1 hour 10 min)

Lecture 3: 05 03 Statistical Inference (45 min)

Lecture 4: 05 04 Data Science Tools (30 min)

Total Time: 3 hour 20 mins

## Empirical Risk Minimization Theory:Maths For ML Series Part 6

Empirical Risk Minimization Theory: This course covers Machine learning formalization, supervised learning, loss function, risk function, optimization problems, sampling pitfalls, bias-variance trade-off

Lecture 1: 06 01 Intro (17 min)

Lecture 2: 06 02 General Overview Machine Learning (3 min)

Lecture 3: 06 03 Supervised Problem (48 min)

Lecture 4: 06 04 Model Validation (54 min)

Total Time: 2 hour 40 min