Intermediate
9h
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.
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
Content
1.
Introduction to Optimization
8 Lessons
Get familiar with optimization techniques, algorithms, and their applications in machine learning.
2.
Vector Calculus
10 Lessons
Discover the logic behind differentiation, gradients, Hessian, Taylor series, and integrals in vector calculus.
Differentiation of the Univariate FunctionsPartial Derivatives and GradientsGradients of the Vector-Valued FunctionsGradients of MatricesHigher-Order Gradients: HessianLinearization and the Taylor SeriesIntegrals and the Trapezoidal RuleQuiz Yourself on Vector CalculusChallenge: The Taylor SeriesSolution: The Taylor Series
3.
Convex Optimization
14 Lessons
Examine convex optimization techniques, including gradient methods, ridge regression, and Fisher analysis.
4.
Gradient Descent for Non-Convex Optimization
10 Lessons
Grasp the fundamentals of advanced gradient descent techniques for optimizing non-convex problems.
5.
Constrained Optimization
10 Lessons
Explore constrained optimization using linear programming, simplex method, and Lagrange duality.
6.
Miscellaneous Methods
9 Lessons
Tackle diverse optimization techniques, including Newton's, Quasi-Newton, barrier methods, and SciPy utilities.
7.
Course Conclusion
1 Lessons
Build on essential optimization techniques for machine learning, enhancing models with advanced algorithms.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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