Manifold AI Learning
Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills – Most Importantly Linear Algebra?
Then, this course is for you.
With the growing learners of Machine Learning, Data Science, and Deep Learning.
The Common mistake by a data scientist is Applying the tools without the intuition of how it works and behaves.
Having the solid foundation of mathematics will help you to understand how each algorithm work, its limitations and its underlying assumptions.
With this, you will have an edge over your peers and makes you more confident in all the applications of Machine Learning, Data Science, and Deep Learning.
As a common saying:
It always pays to know the machinery under the hood, rather than being a guy who is just behind the wheel with no knowledge about the car.
Linear Algebra is one of the areas where everyone agrees to be a starting point in the learning curve of Machine Learning, Data Science, and Deep Learning.. Its basic elements Vectors and Matrices are where we store our data for input as well as output.
Any operation or Processing involving storing and processing the huge number of data in Machine Learning, Data Science, and Artificial intelligence, would mostly use Linear Algebra in the backend.
Instructor Details
Courses : 4
Specification: Linear Algebra Mathematics for Machine Learning Data Science
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19 reviews for Linear Algebra Mathematics for Machine Learning Data Science
Add a review
| Price | $15.99 |
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| Provider | |
| Duration | 21 hours |
| Year | 2021 |
| Level | All |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
$15.99
Isaac –
Excellent explanation.
Kate Hayes –
Hard to understand the narrator, mateiral is fine.
Sipho Motha –
The instructor is explains the concepts so clearly that even if you don’ t have background in mathematics, you still grasp the concepts.
Santosh –
very impressed by the way of teaching.
karthik jakkala –
This is great course on Linear Algebra, I like the mathematical derivations solved in this course. Great thanks for the course, please continue to add more courses. One request from my side is, speak little slow because in derivations explanation is too fast. Once again thank you …
Anan Suebsomran –
fast
Kishan –
it is good. but requires little more deep drive in terms of python practice.
Ruthira Sekar –
It’s good to watch the video comparing with what we learned in school time.
Isabel Nadine de Santana –
Subtitles not generated dinamically
Bhavesh –
the best course on linear algebra, have covered all the linear algebra related topics for data science & Machine learning. Thank you
Sangeetha Shetty –
good coverage of topics
Edward Sanchiaz –
instructor teaching way is commanding, can follow and understand easily
Siva Prasad –
Good course on maths part for data science
Charith Amarasinghe –
good explanations
kumar piyush –
Lot of half explanations …
Saurabh Rastogi –
There are spelling mistakes in the content and this is a put down for me.
Steven Miller –
This is exactly the information about matrix algebra that I wanted to know to augment the work I am doing in data science. I appreciate the concise explanations. I can’t wait for the Google Pagerank Algorithm and beyond.
Jacob James –
Good instruction of the topics
Shaquan Howsie –
information is great and the orator is very knowledgeable however, it was difficult to comprehend his accent.