**Data Science complete guide on Linear Algebra –** DeepLearning 3.9 (107 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.

What you’ll learn

- Build Mathematical intuition required for Data Science and Machine Learning
- The linear algebra intuition required to become a Data Scientist
- How to take their Data Science career to the next level
- Hacks, tips & tricks for their Data Science career
- Implement Machine Learning Algorithms better
- Apply Linear Algebra in Data Analysis
- Learn core concept to Implement in Machine Learning

Description

Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills ?

Then, this course is for you.

With the growing learners of **Machine Learning , Data Science and Artificial intelligence**.

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 algorithms 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 **Artificial intelligence*

As a common saying:

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 area where everyone agrees to be a starting point in learning curve of *Machine Learning, Data Science **and *** Artificial intelligence**. Its basic elements – Vectors and Matrices are where we store our data for input as well as output.

Even ** Deep Learning and Neural Networks** – Employs the Matrices to store the inputs like image, text etc.