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Linear Algebra Math for AI – Artificial Intelligence

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Linear Algebra Math for AI – Artificial Intelligence, available at $94.99, has an average rating of 4.1, with 198 lectures, 1 quizzes, based on 371 reviews, and has 5052 subscribers.

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

Enroll now: Linear Algebra Math for AI – Artificial Intelligence

Summary

Title: Linear Algebra Math for AI – Artificial Intelligence

Price: $94.99

Average Rating: 4.1

Number of Lectures: 198

Number of Quizzes: 1

Number of Published Lectures: 198

Number of Published Quizzes: 1

Number of Curriculum Items: 199

Number of Published Curriculum Objects: 199

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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.

Target Audiences

  • 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.

Master Linear Algebra for Data Science, Machine Learning, and Deep Learning – Unleash the Power of Mathematics in AI Applications

Are you eager to enhance your skills in Machine Learning, Deep Learning, and Data Science by mastering the crucial foundation of Linear Algebra? Look no further – this comprehensive course is designed just for you.

With the increasing demand for expertise in Machine Learning and Deep Learning, it’s crucial to avoid the common mistake of relying solely on tools without a deep understanding of their underlying mathematical principles. This course is your key to developing a solid foundation in mathematics, providing you with a profound intuition of how algorithms work, their limitations, and the assumptions they rely on.

Why is a strong mathematical foundation important? Understanding the machinery under the hood is the key to becoming a confident practitioner in the fields of Machine Learning, Data Science, and Deep Learning. Linear Algebra is universally acknowledged as a fundamental starting point in the learning journey of these domains.

The basic elements of Linear Algebra – Vectors and Matrices – serve as the backbone for storing and processing data in various applications of Machine Learning, Data Science, and Artificial Intelligence. From basic operations to complex tasks involving massive datasets, Linear Algebra plays a pivotal role.

Even in advanced technologies like Deep Learning and Neural Networks, Matrices are employed to store inputs such as images and text, providing state-of-the-art solutions to complex problems.

Recognizing the paramount importance of Linear Algebra in a Data Science career, we have crafted a curriculum that ensures you build a strong intuition for the concepts without getting lost in complex mathematics.

By the end of this course, you will not only grasp the analytical aspects of Linear Algebra but also witness its practical implementation through Python. Additionally, you will gain insights into the functioning of the renowned Google PageRank Algorithm, utilizing the concepts learned throughout the course.

Here’s what the course covers:

  • Vectors Basics

  • Vector Projections

  • Basis of Vectors

  • Matrices Basics

  • Matrix Transformations

  • Gaussian Elimination

  • 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 Natural Language Processing

  • Linear Algebra for Deep Learning

  • Linear Regression using PyTorch

  • Bonus: Python Basics & Python for Data Science

This hands-on course takes you on a step-by-step journey, providing the essential Linear Algebra skills required for Data Science, Machine Learning, Natural Language Processing, and Deep Learning. Enroll now and embark on your journey to master the mathematical foundations powering AI applications. Click the ‘Enroll’ button to start your learning experience – I look forward to seeing you in Lecture 1!

Course Curriculum

Chapter 1: Vectors Basics

Lecture 1: Vectors – Basics

Lecture 2: Operation on Vectors

Lecture 3: Co- Ordination System

Lecture 4: Ready for Next Section

Chapter 2: Vector Projections

Lecture 1: Vector Magnitude and Direction

Lecture 2: Cosine Rule Triangle of Vectors

Lecture 3: Projection of Vector

Lecture 4: Operation of Vectors in Python

Lecture 5: Vector Norm / Magnitude

Chapter 3: Basis of Vectors

Lecture 1: Changing Basis of Vectors

Lecture 2: Understanding the Basis, Linear combination and Span

Chapter 4: Matrix Basics from High school

Lecture 1: Matrices Introduction

Lecture 2: Understanding the concept of Matrices

Lecture 3: Types of Matrices

Lecture 4: Operations on Matrices

Lecture 5: Matrix Multiplication

Lecture 6: Transpose of a Matrix

Lecture 7: Elementary Row Operations on Matrix

Lecture 8: Matrix Hands-on – Arithmetic Operations

Lecture 9: Types of Matrices

Lecture 10: Operations on Matrices

Chapter 5: Matrices – Setting up the stage – Transformations

Lecture 1: Setting the stage for Matrices

Lecture 2: how Matrices Transform

Lecture 3: Types of Matrix Transformation

Lecture 4: Combination or Composition

Chapter 6: Gaussian Elimination

Lecture 1: Gaussian Elimination – Solve Equations

Lecture 2: Vector Matrices relation

Lecture 3: Determinants

Chapter 7: Einstein Summation convention – Non Orthogonal basis – Gram Schmidt Process

Lecture 1: Einstein Summation convention

Lecture 2: Transforming non orthogonal basis

Lecture 3: Transforming to new Basis

Lecture 4: Orthogonal Matrices

Lecture 5: Gram-Schmidt Process

Lecture 6: Gram-Schmidt in Python

Chapter 8: Eigen Problems

Lecture 1: Vector Field

Lecture 2: Eigen Values

Lecture 3: Eigen – Mathematical sense

Lecture 4: Changing to Eigen Basis

Lecture 5: Changing Eigen Basis – Example

Lecture 6: Eigen Decomposition using Python

Lecture 7: Eigen Values Calculation – Recap

Lecture 8: Eigen values in Python

Chapter 9: Principal Component Analysis – Application of Eigen Values and Eigen Vectors

Lecture 1: PCA with Eigen Background

Lecture 2: LDA on Dimensionality Reduction

Chapter 10: Google Pagerank Algorithm

Lecture 1: Google Page Rank Algorithm

Lecture 2: Page Rank Algorithm – Python

Lecture 3: Damping

Chapter 11: SVD – Singular Value Decomposition

Lecture 1: SVD – Singular Value Decomposition

Lecture 2: SVD – Concept of Singular

Lecture 3: SVD – Building up for Reduced SVD

Lecture 4: Full SVD

Lecture 5: Compute terms of SVD

Lecture 6: Preparation for SVD in Python

Lecture 7: SVD

Lecture 8: SVD in Image Compression

Chapter 12: Pseudo Inverse

Lecture 1: Moore Penrose Pseudo inverse

Lecture 2: Pseudo Inverse – Python

Chapter 13: Matrix Decompositions

Lecture 1: Matrix Decompositions

Chapter 14: Solving the Linear Regression using Matrix Decomposition methods

Lecture 1: Solve Linear Equations Part 1

Lecture 2: Solve Linear Equations Part 2 – Normal Equation

Lecture 3: Solve Linear Equation Part 3 – Inverse QR

Chapter 15: Linear Regression from Scratch

Lecture 1: Loading the data

Lecture 2: Initialize & Predict

Lecture 3: Finding the cost

Lecture 4: Updation of Parameters – Finding the Partial Derivatives

Lecture 5: Gradient Descent with Python

Lecture 6: Linear Regression Run

Chapter 16: Linear Algebra in Natural Language Processing

Lecture 1: NLP Intro

Lecture 2: NLP – Data Exploration

Lecture 3: NLP – Count Vectorizer

Lecture 4: NLP – Apply Count Vectorizer on Text Data

Lecture 5: Understanding the Density of words

Lecture 6: Limit the number of Features

Lecture 7: Removal of Stop Words

Lecture 8: Porter Stemmer on plurals

Lecture 9: Stemming on a Sentence

Lecture 10: Stemming on Entire Text data

Lecture 11: Lemmatization on the text

Lecture 12: Applying Machine Learning Model on Text data

Lecture 13: One Hot Encoding on Text Data

Lecture 14: TFidf Vectorizer on Text Data

Chapter 17: Linear Algebra for Deep Learning – Getting started with Pytorch

Lecture 1: 1. Deep Learning Algebra Intro Installation

Instructors

Rating Distribution

  • 1 stars: 12 votes
  • 2 stars: 12 votes
  • 3 stars: 53 votes
  • 4 stars: 114 votes
  • 5 stars: 180 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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