Course Details
Mathematics for Machine Learning
Data Science
Created On
July 02, 2023Description
Mathematics for Machine Learning is the application of mathematical concepts and techniques to develop algorithms and models used in machine learning. It involves using mathematical frameworks such as linear algebra, calculus, probability theory, and optimization to analyze and solve problems in machine learning. The course "Mathematics for Machine Learning" offers participants the benefits of gaining foundational knowledge, developing algorithms, solving complex problems, optimizing models, making informed decisions, and advancing their careers in the field of machine learning.
Overview
This course is designed to provide you with a comprehensive understanding of the mathematical foundations essential for machine learning and data science. Through a structured curriculum, you will delve into linear algebra, calculus, and their applications in machine learning. The course includes hands-on coding exercises using Python, enabling you to implement mathematical concepts in real-world scenarios.
Features
- Assignment and Quiz
- Downloadable Resources
- Regression Analysis
- Comprehensive Syllabus
- Real-world Applications
- Mathematical Foundations
- Practical Implementations
- Completion Certificate
What you'll learn
- Vector operation
- Matrix Manipulation
- Eigenvalue Decomposition
- Applications of Eigenvalue Decomposition
- Calculus: Differentiation
- Calculus: Partial Differentiation
- Calculus: Integration
- Real-world Implementations
Prerequisites
Curriculum
- 6 modules
Understanding the fundamentals of linear algebra.
Vectors, matrices, and tensors.
Vector operations, transposition, and norms.
Dot product, orthogonal vectors, and vector projection.
Matrix transposition and arithmetic operations.
Hadamard operations and matrix reduction.
Solving systems of linear equations.
Matrix norms, properties, and linear transformations.
Properties of eigenvalues and eigenvectors.
Eigen decomposition and its applications.
Matrix operations in machine learning.
Introduction to limits and their significance.
Rate of change, slope, and differentiation.
Differentiation rules and applications.
Auto-differentiation with PyTorch and TensorFlow.
Partial derivatives and chain rule.
Practical applications of derivatives.
Regression project theory and hands-on regression project.
Gradient descent for point and group regression.
Standard integrals and integration rules.
Indefinite and definite integrals.
Area under the curve (AUC) and ROC curve analysis using scikit-learn.
Hands-on ROC AUC project.
Instructors
Skoliko Faculty
- 6 Modules
- 9 Hours
- Data Science
Login to Purchase the Course