Description
What you’ll 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
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
selfdriving 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!
Who this course is for:
 Any one who wants to refresh or learn the mathematical tools required for AI and machine learning will find this course very useful
Course content
 Introduction
 Linear Algebra
 Multivariate Calculus
 Probability Theory
 Probaility Theory
Donny Phan –
Super practical. Lessons are catered towards anyone looking to find work in this industry. It felt very comprehensive and gave me a broad understanding of the programming spectrum
Madhav raj Verma –
Thanks for your great effort. i am fully satisfied with this course the way you teach and your explanation are very clear ,The content you provide in your course no one can do this at this price.
Sachin Gupta –
I really didn’t want to leave a low rating as Angela is a great teacher. The 1st half of this course was terrific. The 2nd half was terrible. Under the justification of “teaching students how to figure things out on their own”, pretty much all videos and all explanations were dropped. You were just told what to do, given links to documentation and told to figure it out on your own. I understand doing that to some degree, but to revert to that entirely for nearly half the content barely makes this a course. It’s just a list of things for you to learn, then you’re left on your own to learn them. The 2nd half was so bad, especially the data science component, that I didn’t bother finishing the course.
Vincent Beaudet –
Amazing 40 days course.
Angela is a great teacher.
The other 60 days are all about web developement, interacting with web pages, on your own with little to no explanations. I did not expect that at all. I wanted to learn more about software and scripting.
This left me disappointed , confused and i started to doubt myself. Not a fun experience after the amount of effort i’v put in this course.
Exercices format and explanations for the first 40 days were worth it tho.
Ben K –
Not just an introduction to python, but really helps you learn fundamental aspects of python and coding in general. Some parts may require some knowledge on the subject (data science comes to mind) and there is quite some web development in the course. So, a few areas were not completely to my liking (I would have liked to see it done differently), but this course deserves the 5 stars in my opinion.
Omid Alikhel –
I found the method a bit difficult when a code is written and then changed back to something different, with no enough explanation of how something happened and where it came from or a step by step explanation of why something is happening, i have no doubt in the instructors talent, but we are beginners!
Devang Jain –
The course is not updated and most of the solution codes don’t work and there are no video solutions towards the end
Szymon Kozak –
I think that the course tutor is really good in giving right information to learn at the right time. Thanks to this fact, my understanding of coding in python after 29 days of learning is above my expectations.
Begoña Ruiz Diaz –
Ha sido la mejor elección que podría haber hecho.
Vaibhav Sachdeva –
I want to thank Angela for making such an amazing course. It really helped me explore more things with python.