Math 0-1- Linear Algebra for Data Science Machine Learning

Math 0-1: Linear Algebra for Data Science & Machine Learning, available at $69.99, has an average rating of 4.65, with 101 lectures, based on 170 reviews, and has 2100 subscribers.

You will learn about Solve systems of linear equations Understand vectors, matrices, and higher-dimensional tensors Understand dot products, inner products, outer products, matrix multiplication Apply linear algebra in Python Understand matrix inverse, transpose, determinant, trace Understand matrix rank and low-rank approximations (e.g. SVD) Understand eigenvalues and eigenvectors This course is ideal for individuals who are Anyone who wants to learn linear algebra quickly or Students and professionals interested in machine learning and data science but whove gotten stuck on the math It is particularly useful for Anyone who wants to learn linear algebra quickly or Students and professionals interested in machine learning and data science but whove gotten stuck on the math.

Enroll now: Math 0-1: Linear Algebra for Data Science & Machine Learning

Summary

Title: Math 0-1: Linear Algebra for Data Science & Machine Learning

Price: $69.99

Average Rating: 4.65

Number of Lectures: 101

Number of Published Lectures: 101

Number of Curriculum Items: 101

Number of Published Curriculum Objects: 101

Original Price: $69.99

Quality Status: approved

Status: Live

What You Will Learn

Solve systems of linear equations

Understand vectors, matrices, and higher-dimensional tensors

Understand dot products, inner products, outer products, matrix multiplication

Apply linear algebra in Python

Understand matrix inverse, transpose, determinant, trace

Understand matrix rank and low-rank approximations (e.g. SVD)

Understand eigenvalues and eigenvectors

Who Should Attend

Anyone who wants to learn linear algebra quickly

Students and professionals interested in machine learning and data science but whove gotten stuck on the math

Target Audiences

Anyone who wants to learn linear algebra quickly

Students and professionals interested in machine learning and data science but whove gotten stuck on the math

Common scenario: You try to get into machine learning and data science, but there’s SO MUCH MATH.

Either you never studied this math, or you studied it so long ago you’ve forgotten it all.

What do you do?

Well my friends, that is why I created this course.

Linear Algebra is one of the most important math prerequisites for machine learning. It’s required to understand probability and statistics, which form the foundation of data science.

The “data” in data science is represented using matricesand vectors, which are the central objects of study in this course.

If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know linear algebra.

In a normal STEM college program, linear algebra is split into multiple semester-long courses.

Luckily, I’ve refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of semesters.

This course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn’t normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI art) and LLMs (Large Language Models) using LoRA. We will even demonstrate many of the concepts in this course using the Python programming language (don’t worry, you don’t need to know Python for this course). In other words, instead of the dry old college version of linear algebra, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.

Are you ready?

Let’s go!

Suggested prerequisites:

Firm understanding of high school math (functions, algebra, trigonometry)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction and Outline

Lecture 2: How to Succeed in this Course

Lecture 3: Where to Get the Code

Lecture 4: How to Take this Course

Chapter 2: Linear Systems Review

Lecture 1: Lines and Planes

Lecture 2: 2 Equations and 2 Unknowns

Lecture 3: 3 Equations and 3 Unknowns

Lecture 4: Gaussian Elimination

Lecture 5: No Solutions

Lecture 6: Infinitely Many Solutions

Lecture 7: Review Summary

Lecture 8: Suggestion Box

Chapter 3: Vectors and Matrices

Lecture 1: What is a Vector?

Lecture 2: Adding and Subtracting Vectors

Lecture 3: Dot Product

Lecture 4: Dot Product (pt 2)

Lecture 5: Dot Product Exercises in Python

Lecture 6: Application: Neural Embeddings, Cosine Similarity (Optional)

Lecture 7: Exercise: Normalizing a Vector

Lecture 8: Exercise: The Vector Normal to a Plane

Lecture 9: What is a Matrix?

Lecture 10: Matrix Addition and Scalar Multiplication

Lecture 11: Matrix Multiplication

Lecture 12: Properties of Matrix Multiplication

Lecture 13: Matrix-Vector Product

Lecture 14: Application: Neural Networks

Lecture 15: Element-Wise Product

Lecture 16: Outer Product

Lecture 17: Application: Replicating GPT-4 (Optional)

Lecture 18: Matrix Exercises in Python

Lecture 19: Linear Systems Revisited

Lecture 20: Vectors and Matrices Summary

Chapter 4: Matrix Operations and Special Matrices

Lecture 1: Identity Matrix

Lecture 2: Diagonal Matrices

Lecture 3: Matrix Inverse

Lecture 4: Exercise: Inverse of the Inverse

Lecture 5: Singular Matrices

Lecture 6: Matrix Transpose

Lecture 7: Properties of the Matrix Transpose

Lecture 8: Symmetric Matrices

Lecture 9: Transpose in Higher Dimensions

Lecture 10: Orthogonal and Orthonormal Matrices and Vectors

Lecture 11: Exercise: Orthogonal Matrices

Lecture 12: Exercise: Inverse of a Product

Lecture 13: Exercise: Transpose of Inverse of Symmetric Matrix

Lecture 14: Exercise: Why Are Orthogonal Matrices Length- and Angle-Preserving?

Lecture 15: Determinants (pt 1)

Lecture 16: Determinants (pt 2)

Lecture 17: Determinant Formula (Optional)

Lecture 18: Determinant Identities (Optional)

Lecture 19: Exercise: Determinant of a Unitary Matrix

Lecture 20: Matrix Trace (Optional)

Lecture 21: Positive Definite and Negative Definite Matrices

Lecture 22: Exercise: Inverse of a Positive Definite Matrix

Lecture 23: Exercise: Complete the Square

Lecture 24: Matrix Operations Exercises in Python

Lecture 25: Matrix Operations and Special Matrices Summary

Chapter 5: Matrix Rank

Lecture 1: Linear Independence and Dependence

Lecture 2: Geometric Interpretation of Linear Combinations

Lecture 3: The Rank of a Matrix

Lecture 4: Matrix Decompositions (SVD, QR, LU, Cholesky)

Lecture 5: Rank After Multplication

Lecture 6: Low-Rank Approximations and Frobenius Norm

Lecture 7: Applications: Recommender Systems and Topic Modeling (Optional)

Lecture 8: Applications of SVD: Data Visualization and Feature Selection (Optional)

Lecture 9: Application: LoRA for Diffusion Models and LLMs (Optional)

Lecture 10: Exercise: Generating a Positive Semi-Definite Matrix

Lecture 11: Relationship Between Rank and Positive Definiteness

Lecture 12: Matrix Decompositions in Python

Lecture 13: Matrix Rank Summary

Chapter 6: Eigenvalues and Eigenvectors

Lecture 1: How to Find Eigenvalues and Eigenvectors (pt 1)

Lecture 2: How to Find Eigenvalues and Eigenvectors (pt 2)

Lecture 3: Exercise: Rotation Matrix

Lecture 4: Exercise: Why Do A^TA and AA^T Have the Same Eigenvalues?

Lecture 5: Exercise: Eigenvalues of the Inverse

Lecture 6: Conjugate Transpose and Hermitian Matrices

Lecture 7: Hermitian Matrices Have Real Eigenvalues

Lecture 8: Why Do Hermitian Matrices Have Orthogonal Eigenvectors?

Lecture 9: Diagonalization

Lecture 10: Test for Positive Definiteness Using Eigenvalues

Lecture 11: Determinant From Eigenvalues

Lecture 12: Invertibility From Eigenvalues (Positive Definite Matrices Are Invertible)

Lecture 13: Constructing the SVD (Proof of SVD)

Lecture 14: Matrix Powers

Lecture 15: Application: The Vanishing Gradient Problem

Lecture 16: Functions of Matrices (Optional)

Lecture 17: Eigenvalues in Python

Lecture 18: Quiz: Square Root of a Matrix

Lecture 19: Eigenvalues and Eigenvectors Summary

Chapter 7: Setting Up Your Environment (Appendix/FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Lecture 4: Where To Get the Code Troubleshooting

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

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3 stars: 2 votes

4 stars: 71 votes

5 stars: 98 votes

Frequently Asked Questions

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