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Mathematics for Deep Learning - Accelerator

A fast-track program designed to build the essential mathematical foundations.

Tools you’ll work with
Python
SymPy
SciPy
NumPy
Matplotlib
15000
5 weeksBeginner
Instructor
Arun S
MSc AI10+ years • Ex - Microsoft

Course Overview

Description

25 lessons57 exercises4 exams~12 hours

Are you looking for a well-structured data science fundamentals course?

Do you want to gain a clear understanding of the data science field?

This is the perfect course for you.

If terms like traditional data, big data, business intelligence, and machine learning sound confusing, this course will help you understand both meaning and practical application.

25 lessons57 exercises4 exams12 hours

Course Curriculum

A structured, progressive curriculum designed to build depth, intuition, and real-world proficiency over time.

This section introduces key concepts and builds intuition through structured lessons and exercises.

Vectors, matrices, and matrix operations
Systems of linear equations
Eigenvalues and eigenvectors
Vector spaces and transformations
Matrix factorization concepts
Linear algebra applications in neural networks

This section introduces key concepts and builds intuition through structured lessons and exercises.

Functions, limits, and continuity
Differentiation and partial derivatives
Chain rule and gradients
Optimization using derivatives
Multivariable calculus concepts
Role of calculus in backpropagation

This section introduces key concepts and builds intuition through structured lessons and exercises.

Basics of ordinary differential equations
First and second-order differential equations
Solutions to linear differential equations
Numerical methods for solving ODEs
Differential equations in dynamic systems
Applications in learning models

This section introduces key concepts and builds intuition through structured lessons and exercises.

Probability theory and random variables
Probability distributions
Expectation and variance
Bayes’ theorem and conditional probability
Statistical thinking for ML models

This section introduces key concepts and builds intuition through structured lessons and exercises.

Symbolic mathematics using SymPy
Numerical computing with SciPy
Solving equations and integrals programmatically
Matrix computations and transformations
Visualizing mathematical concepts
Applying math concepts to ML problems

How You’ll Learn

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Live Classes
Quizzes
Assignments
Projects
Certification

Build Skills That Scale With You

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Learn deeply. Apply repeatedly.