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
Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills - Most ImportantlyLinear 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.
Even Deep Learning and Neural Networks - Employs the Matrices to store the inputs like image, text etc. to give the state of the art solution to complex problems.
Keeping in mind the significance of Linear Algebra in a Data Science career, we have tailor-made this curriculum such that you will be able to build a strong intuition on the concepts in Linear Algebra without being lost inside the complex mathematics.
At the end of this course, you will also learn, how the Famous Google PageRank Algorithm works, using the concepts of Linear Algebra which we will be learning in this course.
In this course. you will not only learn analytically, but you will also see its working by running in Python as well.
So, with this course, you will learn, build intuition, and apply to some of the interesting real-world applications.
Click on the Enroll Button to start Learning.
I look forward to seeing you in Lecture 1
Course Contents:
In this course you will take a step by step journey in mastering theLinear Algebra that you would require for Data Science, Machine Learning , Natural Language Processing andDeep Learning.
Below lists down the content, and keep in mind - its a hands-on course.
Vectors Basics :
Vector Projections:
Basis ofVectors
Matrices Basics
Matrix Transformations
GaussianElimination
Einstein Summation Convention
Eigen Problems
Google Page Rank Algorithm
SVD - Singular Value Decomposition
Pseudo Inverse
Matrix Decomposition
Solve Linear Regression using Matrix Methods
Linear Regression from Scratch
Linear Algebra in NaturalLanguage Processing
Linear Algebra for Deep Learning
Linear Regression using PyTorch
Bonus (PythonBasics & Python for Data Science)
