Mathematics For Machine Learning Course (FREE)

Course Instructor:

Fabio Mardero is a knowledge scientist from Italy. He graduated in physics and statistical and actuarial sciences. He is at present working at a widely known Italian insurance coverage firm as a knowledge 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 studying key components, vector area, matrices, linear independence and foundation and linear maps.

Lecture 1: 01 01 Intro (1 min)

Lecture 2: 01 02 Machine studying Basics (14 minutes)

Lecture 3: 01 03 Vector Spaces (13 minutes)

Lecture 4: 01 04 Matrices (31 minutes)

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

Lecture 6: 01 06 Linear Maps (40 minutes)

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 minutes)

Lecture 2: 02 02 Angles (28 minutes)

Lecture 3: 02 03 Projections (23 minutes)

Lecture 4: 02 04 Rotations (15 minutes)

Total Time: 1 hour half-hour

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 minutes

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 minutes

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 minutes

Empirical Risk Minimization Theory: Maths For ML Series Part 6

Empirical Risk Minimization Theory: This course covers Machine studying formalization, supervised studying, loss perform, danger perform, optimization issues, 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

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