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Mathematical Foundation For Machine Learning and AI

Learn the core mathematical concepts for machine learning and learn to implement them in R and python
Refresh the mathematical concepts for AI and Machine Learning
Learn to implement algorithms in python
Understand the how the concepts extend for real world ML problems

Artificial
Intelligence has gained importance in the last decade with a lot
depending on the development and integration of AI in our daily
lives. The progress that AI has already made is astounding with the
self-driving cars, medical diagnosis and even betting humans at
strategy games like Go and Chess.

The
future for AI is extremely promising and it isn’t far from when we
have our own robotic companions. This has pushed a lot of developers
to start writing codes and start developing for AI and ML programs.
However, learning to write algorithms for AI and ML isn’t easy and
requires extensive programming and mathematical knowledge.

Mathematics
plays an important role as it builds the foundation for programming
for these two streams. And in this course, we’ve covered exactly
that. We designed a complete course to help you master the
mathematical foundation required for writing programs and algorithms
for AI and ML.

The
course has been designed in collaboration with industry experts to
help you breakdown the difficult mathematical concepts known to man
into easier to understand concepts. The course covers three main
mathematical theories: Linear Algebra, Multivariate Calculus and
Probability Theory.

Linear
Algebra –
Linear algebra notation is used in Machine Learning
to describe the parameters and structure of different machine
learning algorithms. This makes linear algebra a necessity to
understand how neural networks are put together and how they are
operating.

It covers topics such
as:

  • Scalars, Vectors, Matrices, Tensors

  • Matrix Norms

  • Special Matrices and Vectors

  • Eigenvalues and Eigenvectors

Multivariate
Calculus –
This is used to supplement the learning part of
machine learning. It is what is used to learn from examples, update
the parameters of different models and improve the performance.

It covers topics such
as:

  • Derivatives

  • Integrals

  • Gradients

  • Differential Operators

  • Convex Optimization

Probability
Theory –
The theories are used to make assumptions about the
underlying data when we are designing these deep learning or AI
algorithms. It is important for us to understand the key probability
distributions, and we will cover it in depth in this course.

It covers topics such
as:

  • Elements of Probability

  • Random Variables

  • Distributions

  • Variance and Expectation

  • Special Random Variables

The
course also includes projects and quizzes after each section to help
solidify your knowledge of the topic as well as learn exactly how to
use the concepts in real life.

At
the end of this course, you will not have not only the knowledge to
build your own algorithms, but also the confidence to actually start
putting your algorithms to use in your next projects.

Enroll
now and become the next AI master with this fundamentals course!

Introduction

1
Introduction

Linear Algebra

1
Scalars, Vectors, Matrices, and Tensors
2
Vector and Matrix Norms
3
Vectors, Matrices, and Tensors in Python
4
Special Matrices and Vectors
5
Eigenvalues and Eigenvectors
6
Norms and Eigendecomposition

Multivariate Calculus

1
Introduction to Derivatives
2
Basics of Integration
3
Gradients
4
Gradient Visualization
5
Optimization

Probability Theory

1
Intro to Probability Theory
2
Probability Distributions
3
Expectation, Variance, and Covariance
4
Graphing Probability Distributions in R
5
Covariance Matrices in R

Probaility Theory

1
Special Random Variables
2
Bonus Lecture: More Interesting Stuff, Offers and Discounts
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Includes

4 hours on-demand video
1 article
Full lifetime access
Access on mobile and TV
Certificate of Completion