United States
United States
Maths for AI is a course designed to prepare students for the mathematical challenges involved in tackling artificial intelligence problems. It provides a comprehensive overview of the mathematics necessary to solve problems related to AI, such as optimization, linear algebra, probability, and statistics. The course covers principles of machine learning, neural networks, and big data, as well as deep learning applications. Core topics in this course include data processing and analysis, supervised and unsupervised learning, univariate and multivariate linear regression, optimization and convex optimization, deep learning, and decision trees. The skills and abilities acquired through this course will enable students to better understand and evaluate the algorithms used in AI and enable them to develop, debug, and maintain high-quality software in the field.
This is a Rare Course and it can be take up to 3 weeks to arrange the training.
Get 1on-1 session with our expert trainers at a date & time of your convenience.
Flexible Dates
Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.
4-Hour Sessions
Training never been so convenient- attend training sessions 4-hour long for easy learning.
Destination Training
Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.| Live Online Training (Duration : 36 Hours) Fee On Request | |||
|---|---|---|---|
|
|||
Most AI courses don't require any specific math prerequisites, although having a working knowledge of linear algebra, calculus and statistics is helpful. The more math background the better, but these specific subjects are not necessarily essential.
The target audience for Mathematics for AI training would be people from a variety of backgrounds including software engineers, data scientists and those with a technical background. However, the course could also be suitable for general students looking to develop a better understanding or knowledge of the principles and fundamentals of AI, as well as professionals in fields such as finance, retail and health care who may wish to develop their skills in AI and use it in their business. The course is designed with a particular focus on the practical application of mathematical concepts as they relate to AI; this means that all students, regardless of their level of knowledge of mathematics, should be able to benefit from the course. In addition, the course should provide a clear and straightforward roadmap for those who wish to pursue further studies in AI and mathematics.
1. Learn basic numbers and operations: Familiarize yourself with the fundamentals of math such as addition, subtraction, multiplication, division, and exponents.
2. Analyze data using statistics and probability: Understand the basics of probability and statistics, and how to use them to interpret data.
3. Work with equations and functions: Practice working with linear and quadratic equations, polynomials, and other types of equations.
4. Understand algorithms: Learn arithmetic and logic algorithms, including sorting algorithms, dynamic programming, and heuristics.
5. Develop a deeper understanding of AI: Develop a better understanding of how AI algorithms work and how to apply a number of AI techniques.
6. Learn vector and matrix calculations: Grasp the basics of vectors and matrices, and understand how to use them to solve equations.
7. Analyze data sets with calculus: Use calculus to analyze data sets and solve differential equations.
8. Employ optimization techniques: Employ optimization techniques, like linear programming and gradient descent, to solve different problem sets.
