Optimization for Machine Learning with NumPy and SciPy
Learn optimization for machine learning, including gradients, convex optimization, and gradient descent. Explore constrained optimization and advanced methods using NumPy and SciPy.
62 Lessons
9h
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In this course, you will learn about optimization, one of the fundamental pillars of mathematics and machine learning. Machine learning depends heavily on optimization because it allows the model to learn from data and generate precise predictions.
You will begin by introducing optimization. Then, you will learn about optimization basics, including gradients and integrals. Next, you will cover convex optimization. You will then learn how to compute gradient descent for non-convex optimization. Next, you’ll learn how to perform constrained optimization. You will finish the course by studying the miscellaneous methods of optimization, like Newton’s methods, quasi-Newton methods, and conjugate gradient descent.
After completing this course, you’ll have the practical skills to formulate, analyze, and implement optimization algorithms for machine learning using the NumPy and SciPy libraries. This will help you become a highly proficient data scientist or machine learning engineer.
In this course, you will learn about optimization, one of the fundamental pillars of mathematics and machine learning. Machine l...Show More
WHAT YOU'LL LEARN
An understanding of how real-world problems can be framed as optimization problems
Working knowledge about the taxonomy of optimization problems and techniques
Familiarity with the fundamental concepts of optimization
Hands-on experience implementing popular optimization algorithms
The ability to solve popular machine learning problems using optimization
An understanding of how real-world problems can be framed as optimization problems
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TAKEAWAY SKILLS
Learning Roadmap
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