Mathematics Foundation Course for Machine Learning & AI By Eduonix
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This document provides information about a course on the mathematical foundations of machine learning and AI. It discusses how mathematics is important for tasks like selecting algorithms, setting parameters, and identifying overfitting. The course will cover key topics from linear algebra, multivariate calculus, and probability theory that are necessary for understanding machine learning algorithms and building AI systems. It consists of 18 lectures totaling 4 hours and provides a certificate of completion upon finishing.
Mathematics Foundation Course for Machine Learning & AI By Eduonix
- 1. Mathematical Foundation For Machine Learning & AI EDUONIX LEARNING SOLUTIONS
- 2. ARTIFICIAL INTELLIGENCE & MACHINE LEARNING The integration of Artificial Intelligence is growing and multiple sectors are now looking to build technologies that include AI. With self-driving cars, smart robots, to even your coffee machines, AI has become a prominent technology that cannot be overlooked. Writing algorithms for AI and Machine Learning is difficult and requires extensive programming and mathematical knowledge. While these algorithms have the potential to solve a number of difficult problems that are currently plaguing the world, designing these algorithms to solve these problems requires intricate mathematical skills and experience. Eduonix Learning Solutions
- 3. MATHEMATICS FOR AI & ML To become a full stack AI/ML engineer, it is imperative that you have a complete grasp of the mathematical foundations of ML so that you can build upon concepts easily. The basic mathematical skills required are Linear Algebra, Matrix Algebra, Probability and some basic Calculus. Why Mathematics is important for ML? • Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features. • Choosing parameter settings and validation strategies. • Identifying underfitting and overfitting by understanding the Bias- Variance tradeoff. • Estimating the right confidence interval and uncertainty. Eduonix Learning Solutions
- 4. COURSE DESCRIPTION • In this course, we have tried to help you cover exactly that. The course delves deep into the world of mathematics and algorithms to help you get started understanding these complex concepts. • The course will help you learn the mathematical background you need to start working on building algorithms and networks for your next machine learning and AI projects. Eduonix Learning Solutions
- 5. What You Will Learn? Linear Algebra Multivariate Calculus Probability Theory Eduonix Learning Solutions
- 6. MATHEMATICAL CONCEPTS Linear Algebra Scalars, Vectors, Matrices, Tensors Matrix Norms Special Matrices and Vectors Eigenvalues and Eigenvectors Multivariate Calculus Derivatives Integrals Gradients Differential Operators Convex Optimization Probability Theory Elements of Probability Random Variables Distributions Variance and Expectation Special Random Variables Eduonix Learning Solutions
- 7. COURSE DETAILS Lectures:- 18 Video:- 4 hours Level:- All level Languages:- English Last Updated:- 0/2018 Includes:- 30 day money back guarantee! Lifetime Access. No Limits! Certificate of Completion Eduonix Learning Solutions
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