Math 0-1 - Matrix Calculus in Data Science and Machine Learning
Essential Guide to AI and Deep Learning for Python Coders
Created by Lazy Programmer
Explore the essential math behind data science and machine learning by mastering matrix calculus. Build a strong foundation in matrix and vector derivatives, optimization methods, and practical Python tools. Move from core concepts to advanced applications with a focus on real-world problem solving.
Packt | Jan 2024 | 376 min
What You Will Learn
You will start with the basics of matrix calculus and gradually tackle more complex topics like optimization and advanced derivatives. Through hands-on Python exercises and real-world examples, you will reinforce your understanding and see how these concepts power machine learning models. Each topic is paired with practical challenges to help you apply what you learn.
Key Features
- Gain practical skills in matrix and vector derivatives for machine learning tasks
- Apply optimization techniques like gradient descent and Newton's method in Python
- Set up and use key tools such as Numpy, Scipy, and TensorFlow for hands-on projects
Target Audience
Perfect for data science and machine learning enthusiasts who already know some linear algebra, calculus, and Python. If you want to deepen your understanding of the math that drives AI and improve your ability to implement algorithms, this course will help you bridge the gap between theory and practice.
