Learn optimization for machine learning, including gradients, convex optimization, and gradient descent. Explore constrained optimization and advanced methods using NumPy and SciPy.

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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.

Optimization for Machine Learning with NumPy and SciPy

 \frac{df(x_0)}{dx} = \lim_{\epsilon \rightarrow 0} \frac{f(x_0+\epsilon) - f(x_0)}{\epsilon}

 f(x) = f(x_0) + \frac{df(x_0)}{dx} (x-x_0)

 f(x) = f(x_0) + \nabla_xf(x_0) (x - x_0)

Learn how to approximate functions using the Taylor series. 

Linearization and the Taylor Series

Learn how to approximate functions using the Taylor series.

Introduction to Optimization

Vector Calculus

Convex Optimization

Gradient Descent for Non-Convex Optimization

Use Particle Swarm Optimizer to Optimize a Non-convex Function

Constrained Optimization

Miscellaneous Methods

Course Conclusion

Test Your Concepts of Optimization

Training Support Vector Machines (SVMs)

Linearization and the Taylor Series

Linearization