Applied Optimization: Linear, Nonlinear, & Ml Focus
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 22m | Size: 2.95 GB
Model, solve, and code real-world optimization problems and explore their role in machine learning
What you'll learn
Understand core optimization concepts and mathematical modeling techniques
Apply linear and nonlinear optimization techniques using MATLAB and Python
Implement gradient descent algorithms for single-variable and multi-variable problems
Recognize the role of optimization in machine learning
Requirements
It is better if have some knowledge on Python and MATLAB. If you dont have not an issue
Description
Unlock the power of optimization with this practical, hands-on course designed for engineers, students, researchers, and anyone eager to solve real-world problems using mathematical optimization techniques.This course begins with the fundamentals-what optimization is, why it's important, and how to formulate real-world problems as mathematical models. You'll explore different types of optimization problems, including linear, nonlinear, constrained, and unconstrained cases.We guide you step by step through solving linear optimization problems using both Python (with SciPy) and MATLAB, providing clear explanations and code walkthroughs. You'll then dive into nonlinear constrained optimization using the Lagrange multiplier method, followed by an in-depth look at gradient descent algorithms for single-variable and multivariable functions.Throughout the course, you'll learn how to implement these techniques from scratch and using built-in functions, making it ideal for learners who want both conceptual clarity and practical coding skills.The final lecture explores how optimization plays a central role in machine learning, especially in training models and minimizing cost functions.Whether you're an engineering student, data science enthusiast, or academic researcher, this course equips you with the tools and confidence to solve optimization problems in MATLAB and Python.Start learning today and build a strong foundation in applied optimization!
Who this course is for
Are you looking to master optimization techniques and apply them in real-world scenarios using MATLAB and Python? Whether you're a student, engineer, researcher, or data science enthusiast, this course offers a practical and intuitive path to understanding and implementing optimization from the ground up. This course covers everything from the fundamentals of optimization to advanced problem-solving techniques using popular tools like MATLAB and Python. You'll learn how to mathematically model optimization problems, explore linear and nonlinear optimization, and implement gradient descent algorithms for single and multivariable functions. To make the learning experience even more impactful, this course includes a special lecture on how optimization plays a critical role in machine learning, helping you bridge the gap between theory and real-world applications.
Applied Optimization: Linear, Nonlinear, & Ml Focus
