## What you would learn in Mathematical Foundations of Machine Learning course?

Mathematics is the foundation of machine learning and data science. Therefore, to become the most effective data scientist that you can be, you need to know the essential math.

Beginning your journey into data science is simple, thanks to high-level libraries such as Scikit-learn as well as Keras. However, knowing the math behind the algorithms used in these libraries will open up an innumerable variety of possibilities for you. From identifying modeling problems to generating new and more efficient solutions, knowing the math behind the algorithms will dramatically increase the impact, you make throughout your career.

The course is taught by the profound learning guru Professor Dr. Jon Krohn; this course will give you a solid understanding of the mathematic concepts -- specifically maths and linear algebra, which underlie machine learning algorithms and models for data science.

Sections of the Course:

Linear Algebra Data Structures

Tensor Operations

Matrix Properties

Eigenvectors and Eigenvalues

Matrix Operations for Machine Learning

Limits

Differentiation and Derivatives

Automatic Differentiation

Partial-Derivative Calculus

Integral Calculus

Through each of the sections in each section, you'll find practical assignments, Python code demos, and exercises that will help you improve your math skills to top shape!

A Mathematical Foundations of Machine Learning course is now complete and will be updated shortly. We plan to add bonus material from other related subjects beyond math, such as probability statistical data structures, algorithms optimization, and data structures. Registration now gives you free and unlimited access to future course material -- more than 25 hours total.

### Course Content:

- Learn the fundamentals of calculus and linear algebra. Fundamental mathematical concepts are the basis of the entire field of machine learning as well as data science.
- Utilize tensors to manipulate tensors with the help of all of the most potent Python Tensor libraries: NumPy, TensorFlow, and PyTorch
- How do you apply all the necessary functions of matrix and vector in data science and machine learning?
- Reduce the complexity of data into the most relevant elements by using eigenvectors and SVD and PCA
- Solve for unanswered questions using basic techniques (e.g., elimination, elimination) and more advanced methods (e.g., pseudo inversion)
- Discover how calculus works by starting with the basics, using interactive code demonstrations using Python
- Learn to comprehend complex differentiation rules such as the chain rule intimately.
- Calculate parts of the derivatives from machine learning cost functions using your hands and also using TensorFlow and PyTorch
- Know precisely what gradients are and understand the reason why they are crucial to enabling ML through gradient descent
- Utilize integral calculus to determine the area that is covered by a given curve
- Learn to be better aware of the details of cutting-edge machine learning research papers
- Learn about the workings of the machines that run the algorithms, which includes deep learning algorithms.

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