Description
Mathematics for Machine Learning and AI I: Essentials. This course provides a comprehensive foundation in the mathematical concepts needed to understand and effectively apply machine learning and AI methods. The course begins with a tutorial on linear algebra, covering vectors, subspaces, eigenvalues, and orthogonality, and demonstrates their practical applications in machine learning algorithms using Python and MATLAB. It then moves on to multivariable calculus, examining functions, derivatives, partial derivatives, Hessian matrices, and Lagrange multipliers, which are critical to the learning and optimization process of models. The next section of the course focuses on probability and statistics, covering the basic concepts of probability, distributions, Bayes’ theorem, hypothesis testing, and mathematical expectation maximization, which are essential for probabilistic reasoning in AI systems. Finally, optimization topics including gradient descent, RMSProp, AdaGrad, KKT conditions, and linear programming are taught to provide a thorough understanding of how to train, constrain, and improve models. With a clear and practical structure, this course teaches both the theoretical aspects and practical implementation of concepts without requiring a specific background in programming or machine learning, and in the end, equips learners with the mathematical skills necessary to advance in the field of machine learning and artificial intelligence.
What you will learn
- Understand and apply key concepts in linear algebra, including vector operations, subspaces, and eigenvalues.
- Learn the basics of calculus and multivariable calculus, which are required for machine learning models.
- Mastery of probability theory, Bayes’ theorem, and statistical tools such as MLE and hypothesis testing.
- Apply essential optimization techniques such as gradient descent, KKT conditions, and linear programming in Python.
This course is suitable for people who:
- Aspiring data scientists, machine learning engineers, or AI enthusiasts looking for a solid foundation in mathematics.
- Students in engineering, computer science, or related fields who are preparing for advanced machine learning courses.
- Professionals looking to refresh or deepen their understanding of the core mathematical concepts used in artificial intelligence.
Course details for Mathematics for Machine Learning and AI I: Essentials
- Publisher: Udemy
- Instructor: Orforte Academy
- Training level: Beginner to advanced
- Training duration: 8 hours and 18 minutes
Course syllabus in 2025/8
Prerequisites for the course Mathematics for Machine Learning and AI I: Essentials
- Basic high school-level algebra and geometry knowledge is helpful but not required.
- A willingness to learn mathematical concepts with practical relevance to AI and data science.
Course images
Sample course video
Installation Guide
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Subtitles: None
Quality: 720p
Download link
File(s) password: www.downloadly.ir
File size
2.04 GB
