Mathematics forms the core of data science and machine learning. To excel as a data scientist, it's crucial to have a deep understanding of the most relevant mathematical concepts. While high-level libraries like Scikit-learn and Keras make it easy to start, comprehending the mathematics behind these algorithms can unlock endless possibilities.
Understanding the underlying math can help you identify modeling issues and create innovative and more powerful solutions, significantly enhancing your career impact. This course, led by deep learning expert Dr. Jon Krohn, provides a comprehensive understanding of the essential mathematics—including linear algebra and calculus—that supports machine learning algorithms and data science models.
Course Sections
Linear Algebra Data Structures: Explore the fundamental structures that form the basis of linear algebra in data science.
Tensor Operations: Delve into operations on tensors, which are generalizations of vectors and matrices.
Matrix Properties: Understand the essential properties of matrices and how they apply to data models.
Eigenvectors and Eigenvalues: Learn how these concepts are vital for dimensionality reduction and stability investigations.
Matrix Operations for Machine Learning: Discover how to apply matrix operations specifically in the context of machine learning.
Limits: Grasp the concept of limits, fundamental to understanding calculus.
Derivatives and Differentiation: Master the art of differentiation and learn how derivatives play a pivotal role in optimization algorithms.
Automatic Differentiation: Familiarize yourself with tools and techniques that streamline the differentiation process.
Partial-Derivative Calculus: Explore the nuances of partial derivatives, especially in functions of multiple variables.
Integral Calculus: Learn integral calculus and its applications in calculating areas, volumes, and solving differential equations.
Each section features hands-on assignments, Python code demos, and practical exercises designed to enhance your mathematical skills and apply them effectively in data science and machine learning projects.
