MATHEMATICAL FOUNDATION FOR MACHINE LEARNING AND AI

Looking to Learn core mathematical concepts for machine learning and learn to implement them in R and python then this course belongs to you.

 

What you’ll learn

After downloading this course, you will be able to learn the following.

  • You will be able to Refresh the mathematical concepts for AI and Machine Learning
  • You will learn how to implement algorithms in a python programming language.
  • You will be able to understand how the concepts extend for real-world ML problems

Course content

all 19 lectures 04:16:13

Requirements

  • Basic knowledge of python is assumed as concepts are coded in python and R

Description

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 beating 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!

Who this course is for:

  • Anyone who wants to refresh or learn the mathematical tools required for AI and machine learning will find this course very useful

Size: 1.79G





This course was created by Eduonix Learning Solutions, Eduonix-Tech.

Language: English

 

 

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