Complete Linear Algebra for Machine Learning – Data Science

Complete Linear Algebra for Machine Learning - Data Science

Master the Complete linear algebra for data science, machine learning & Deep Learning with Hands On Exercise in Python

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

Requirements

  • Pen and a paper to workout maths problem
  • Computer with Python to execute the code
  • Some programming experience

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 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

Who this course is for:

  • Data Scientists who wish to improve their career in Data Science.
  • Machine Learning Practitioners
  • Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning and Artificial intelligence
  • Any Data Science enthusiast
  • Any student or professional who wants to start or transition to a career in Data Science.
  • Students who want to refresh and learn important maths concepts required for Machine Learning , Deep Learning & Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.

Course content

21 sections • 121 lectures • 13h 5m total length
  • Vectors Basics
  • Vector Projections
  • Basis of Vectors
  • Matrix Basics from High school
  • Matrices – Setting up the stage – Transformations
  • Gaussian Elimination
  • Einstein Summation convention – Non Orthogonal basis – Gram Schmidt Process
  • Eigen Problems
  • Google Pagerank Algorithm
  • SVD – Singular Value Decomposition
  • Pseudo Inverse
  • Matrix Decompositions
  • Solving the Linear Regression using Matrix Decomposition methods
  • Linear Regression from Scratch
  • Linear Algebra in Natural Language Processing
  • Linear Algebra for Deep Learning – Getting started with Pytorch
  • Linear Regression Using Pytorch
  • Python Basics
  • Python for Data Science
  • Basics of Statistics
  • Bonus content

Created by: Manifold AI Learning ®
Last updated 9/2020
English
English [Auto]
6.4 GB

https://www.udemy.com/course/linear-algebra-for-data-science-machine-learning-ai/

 

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