Mathematical Foundation For Machine Learning and AI

Mathematical Foundation For Machine Learning and AI, available at $49.99, has an average rating of 4.4, with 19 lectures, based on 1282 reviews, and has 7488 subscribers.

You will learn about 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 This course is ideal for individuals who are Any one who wants to refresh or learn the mathematical tools required for AI and machine learning will find this course very useful It is particularly useful for Any one who wants to refresh or learn the mathematical tools required for AI and machine learning will find this course very useful.

Enroll now: Mathematical Foundation For Machine Learning and AI

Summary

Title: Mathematical Foundation For Machine Learning and AI

Price: $49.99

Average Rating: 4.4

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Linear Algebra

Lecture 1: Scalars, Vectors, Matrices, and Tensors

Lecture 2: Vector and Matrix Norms

Lecture 3: Vectors, Matrices, and Tensors in Python

Lecture 4: Special Matrices and Vectors

Lecture 5: Eigenvalues and Eigenvectors

Lecture 6: Norms and Eigendecomposition

Chapter 3: Multivariate Calculus

Lecture 1: Introduction to Derivatives

Lecture 2: Basics of Integration

Lecture 3: Gradients

Lecture 4: Gradient Visualization

Lecture 5: Optimization

Chapter 4: Probability Theory

Lecture 1: Intro to Probability Theory

Lecture 2: Probability Distributions

Lecture 3: Expectation, Variance, and Covariance

Lecture 4: Graphing Probability Distributions in R

Lecture 5: Covariance Matrices in R

Chapter 5: Probaility Theory

Lecture 1: Special Random Variables

Lecture 2: Bonus Lecture: More Interesting Stuff, Offers and Discounts

Instructors

Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses

Eduonix-Tech .

Rating Distribution

1 stars: 42 votes

2 stars: 48 votes

3 stars: 209 votes

4 stars: 459 votes

5 stars: 524 votes

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