Understanding The Math Behind AI
 
Are you eager to grasp the mathematical underpinnings of artificial intelligence (AI) and apply them to real-world challenges? Do you desire mastery in understanding AI mathematics straightforwardly, from concepts to formulas to methods to examples? If so, this course is tailor-made for you.

Welcome to the "Understanding the Math Behind AI" course, a comprehensive program that imparts essential mathematical concepts in AI. Covering calculus, probability, statistics, linear regression, logistic regression, neural networks, and deep learning, this course caters to individuals at all levels, from beginners to advanced enthusiasts. It's ideal for those aspiring to pursue a career or hobby in AI, including data scientists, machine learning engineers, AI researchers, or enthusiasts.

Throughout the course, you'll learn:

- The significance of math in AI and its fundamental role.
- Basic and advanced topics of calculus and their application in AI.
- Essential concepts of probability and statistics and their utilization in AI.
- How to implement linear regression and logistic regression in AI.
- Fundamentals of neural networks and deep learning and their application in AI.
- Challenges and solutions for optimizing neural networks and their practical implementation in AI.
- Applications and examples of neural networks and deep learning in AI.

Enroll in this course today and embark on the journey of learning the math behind AI. You'll be amazed at what you can achieve with this foundational knowledge. See you in the course!

What you'll learn:

- Understand basic and advanced calculus topics and how to use them in AI, such as derivatives, integrals, optimization, and multivariate calculus.
- Understand basic and advanced topics of probability and statistics and how to use them in AI, such as probability rules, random variables, etc.
- Linear regression and logistic regression and how to use them in AI, such as fitting, evaluating, and optimizing regression models for numerical or categorical data.
- Neural networks and deep learning and how to use them in AI, such as constructing, training, and optimizing neural networks.

Who this course is for:
This course is designed for individuals seeking a comprehensive understanding of the mathematics underpinning AI models.