After completing this course, learners will be able to: â€¢ Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients â€¢ Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newtonâ€™s method) iterative methods â€¢ Visually interpret differentiation of different types of functions commonly used in machine learning â€¢ Perform gradient descent in neural networks with different activation and cost functions Mathematics for Machine Learning and Data science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where youâ€™ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills.Â This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, youâ€™ll understand the mathematics behind all the most common algorithms and data analysis techniques â€” plus the know-how to incorporate them into your machine learning career. This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where theyâ€™re most applicable to machine learning and data science.