Essential Math for AI Programming & Data Structures

Overview

Essential Math for AI:

This foundational course is the first part of a two-part series aimed at ensuring learners possess necessary mathematical skills for advanced AI studies. Covering discrete math, calculus, linear algebra, and probability theory, it prepares students for more complex AI subjects by reviewing key concepts applicable to artificial intelligence.

Participants will gain confidence to advance in AI courses by bridging any gaps in essential math knowledge.

Skills developed will include recalling and applying basic concepts in math to solve AI-related challenges. Learn from Columbia Engineering's distinguished Professor Daniel Bauer, and leverage this opportunity to shine in the rapidly evolving AI sector.

Programming & Data Structures:

As the second course in the series, this course refreshes key programming and data structure concepts essential for AI.

It focuses on harnessing Python's built-in data structures and programming concepts for effective algorithm development and data manipulation.

By course completion, students will adeptly employ Python packages for data analysis, visualization, numeric computing, and machine learning. Get hands-on experience with coding, debugging, and utilizing Python's libraries like NumPy and Sci-kit learn, ensuring robust skills for AI applications.

This expert-led course by Professor Daniel Bauer from Columbia University offers unparalleled access to one of the top engineering schools.

Elevate your professional profile in AI with this comprehensive program.

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