Foundations of Machine Learning: Concepts, Tools, and Math

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This course equips learners with a solid foundation in machine learning, emphasizing core concepts, tools, and mathematical principles essential for modern AI applications. It introduces the evolution of AI, the role of computing, and practical coding skills to prepare learners for real-world challenges. Through hands-on exercises using Python and Google Colab, learners gain confidence in building and experimenting with models, understanding data structures, and implementing algorithms efficiently. The course bridges theory and practice, helping learners translate abstract concepts into actionable skills. What sets this course apart is its balanced approach combining mathematical rigor, Python programming, and practical exercises. Learners explore gradient descent, key algorithms, and model evaluation, ensuring they understand both theory and its applications. Ideal for aspiring data scientists, AI enthusiasts, and developers with basic Python knowledge, this course requires no prior advanced ML experience but benefits those with foundational programming skills. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. This Specialization is based on the book, Machine Learning For Dummies, by John Paul Mueller. From Machine Learning For Dummies Copyright © 2026 by John Wiley & Sons, Inc. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Used by arrangement with John Wiley & Sons, Inc.

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