Skills you'll gain

There are 12 modules in this course

By the end of this course, learners will be able to apply functions, matrices, and vectors to represent and analyze data relationships. Students will be able to use descriptive statistics and visualization techniques to explore and summarize datasets, solve systems of linear equations and model complex relationships using linear regression of single and multiple variables, and understand and implement foundational principles of probability, including Bayes' Theorem. The course builds to advanced mathematical techniques in Calculus, and develops derivatives and integrals to analyze rates of change and distributions, essential for optimization and modeling in AI. Concepts from Linear Algebra are used to explore advanced concepts like eigenvectors, determinants, and linear transformations for dimensionality reduction and classification algorithms. This course is specifically tailored for aspiring AI practitioners. Unlike traditional math courses, this curriculum focuses on mathematical techniques directly applicable to artificial intelligence and machine learning, bridging theory with practice. Through interactive modules, real-world datasets, and tools like Python and Excel, you’ll not only understand the concepts but also apply them to solve practical problems. With clearly defined modules such as Descriptive Statistics, Linear Algebra, Probability, and Optimization, this course allows you to build knowledge progressively while connecting each concept to AI use cases. Each topic is introduced with AI-related examples, like using linear regression to model salaries or applying optimization techniques in clustering algorithms, with then a focus on applications of the theory. This course equips you with the mathematical fluency necessary for more advanced AI courses and research, such as deep learning or natural language processing. Whether you’re an engineer, data scientist, or simply interested in breaking into AI, this course provides the mathematical foundation you need to understand and contribute to the rapidly evolving field of artificial intelligence.

Describing and Visualizing Data

Vectors, Matrices, and Linear Equations

Modeling with Linear Equations

Transforming Spaces through Linear Transformations

Vector Geometry: Exploring Relationships in Multidimensional Space

Determinants and Eigenvectors: Unlocking Matrix Insights

Discrete Probability Distributions

Derivatives and Rates of Change: Accelerating Understanding

Mastering Optimization

Integration and Probability: Bridging Calculus and Uncertainty

Partial Derivatives and the Gradient: The Landscape of AI

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