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LinkedIn Learning

Foundational Math for Machine Learning

via LinkedIn Learning Path

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Overview

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Understanding key mathematical concepts is essential for implementing machine learning algorithms effectively. Delve into core concepts from linear algebra to calculus, probability, and statistics. Whether you're a beginner or an experienced practitioner, this learning path equips you with vital skills to tackle complex ML projects confidently.
  • Master linear algebra fundamentals.
  • Grasp calculus concepts for machine learning.
  • Harness the power of probability in machine learning.
  • Unlock insights with statistical analysis.

Syllabus

Courses under this program:
Course 1: Machine Learning Foundations: Linear Algebra
-Explore the fundamentals of linear algebra, the mathematical foundation of machine learning algorithms.

Course 2: Machine Learning Foundations: Calculus
-Learn the basics of calculus concepts and techniques used to design and implement ML algorithms.

Course 3: Machine Learning Foundations: Probability
-Get an in-depth introduction to probability, find out why it’s a prerequisite for machine learning, and learn how to use it to design and implement machine learning algorithms.

Course 4: Machine Learning Foundations: Statistics
-Learn how statistics can help you troubleshoot issues, optimize performance, and innovate, creating new machine learning models that are more efficient.

Courses

Taught by

Terezija Semenski

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