If you're looking to become a data scientist, it's important to understand the mathematics behind data science and machine learning. The Mathematical Foundations of Machine Learning course is a great place to start. This course is led by Dr. Jon Krohn, a deep learning guru, and covers essential linear algebra and calculus. By the end of the course, you'll have a solid understanding of the math underlying machine learning algorithms and data science models.
Why Understanding Math is Important
High-level libraries like Scikit-learn and Keras make it easy to get started in data science. These libraries give you the tools to build models and analyze data without requiring you to have a deep understanding of the mathematics behind the algorithms.
But if you want to become an outstanding data scientist, it's important to have a solid grasp of linear algebra and calculus. Understanding the math behind the algorithms in these libraries can open up a world of opportunities. By understanding the math, you can identify potential modeling issues and invent new and more powerful solutions. It can also improve your ability to communicate with other data scientists and stakeholders who have different levels of technical understanding.
Course Content
The Mathematical Foundations of Machine Learning course covers the essential linear algebra and calculus needed to succeed in data science and machine learning. The course is broken up into ten sections:
- Linear Algebra Data Structures: This section introduces you to the fundamental concepts in linear algebra, such as vectors, matrices, and tensors. You'll learn how to represent and manipulate data using these structures. There are practical exercises to help you solidify your understanding.
- Tensor Operations: This section dives deeper into tensor manipulation. You'll learn how to perform basic tensor operations and how to use Python libraries like NumPy, TensorFlow, and PyTorch to work with tensors.
- Matrix Properties: This section covers the properties of matrices and how they relate to linear algebra. You'll learn about matrix dimensions, matrix inverses, and matrix transposes. You'll also learn how to use these properties to perform matrix operations.
- Eigenvectors and Eigenvalues: In this section, you'll learn about eigenvectors and eigenvalues. These concepts are fundamental to many machine learning algorithms. You'll learn how to calculate eigenvectors and eigenvalues, and how to use them to transform data.
- Matrix Operations for Machine Learning: This section builds on the previous sections and shows you how to use matrix operations to build machine learning models. You'll learn how to use matrices to represent datasets and model parameters.
- Limits: This section focuses on calculus, starting with limits. You'll learn how to calculate limits and how they relate to derivatives and integrals.
- Derivatives and Differentiation: This section covers derivatives and differentiation. You'll learn how to calculate derivatives, and how they relate to optimization problems.
- Automatic Differentiation: Automatic differentiation is a technique used to numerically evaluate derivatives. In this section, you'll learn how to use automatic differentiation to evaluate derivatives efficiently.
- Partial-Derivative Calculus: Partial derivatives are used in multivariate calculus. This section teaches you how to calculate partial derivatives and how they relate to gradient descent — a popular optimization algorithm.
- Integral Calculus: The final section covers integral calculus. You'll learn about Riemann sums and how to use them to solve integrals. You'll also learn how to use integrals to compute probabilities in machine learning.
Each section includes plenty of hands-on assignments, Python code demos, and practical exercises to help you build your skills.
Bonus Content
In addition to the main course, enrollment includes free, unlimited access to over 25 hours of future course content in related subjects beyond math. These subjects include probability, statistics, data structures, algorithms, and optimization.
By taking the Mathematical Foundations of Machine Learning course, you'll not only gain the essential linear algebra and calculus skills you need to succeed as a data scientist, but you'll also have access to a wealth of bonus content to help you continue to grow and expand your knowledge.
Course Reviews
The course has been reviewed by over 4500 students, with an aggregate rating of 4.56 out of 5. The reviews are overwhelmingly positive, with students calling the course "eye-opening", "insightful", and "challenging but rewarding".
Many students praise Dr. Krohn's teaching style, noting that he is clear, patient, and engaging. Students appreciate the hands-on nature of the course and the practical exercises that help solidify their understanding of the material.
Some students note that the course can be challenging, particularly for those who may not have a strong background in math. But even these students say that the challenge is worth it and that the course has improved their understanding of machine learning and data science.
Enrollment
The Mathematical Foundations of Machine Learning course is available now. It is self-paced, so you can start and finish whenever it is convenient for you. Enrollment includes unlimited access to the course content, so you can review the material as many times as you need.
By taking this course, you'll gain the essential math skills you need to succeed in data science and machine learning. Plus, with access to bonus content in related subjects, you'll continue to grow your knowledge and become an outstanding data scientist.
