Math 0-1: Linear Algebra for Data Science & Machine Learning
Math 0-1: Linear Algebra for Data Science & Machine Learning
A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers
This Udemy Math 0-1: Linear Algebra for Data Science & Machine Learning Course created by Lazy Programmer with 19.5 hours on-demand video, Full lifetime access and Certificate of completion. 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.
What you’ll 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.
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GMAT Focus 43Hrs| Quant & Data Insights| GMAT 760 Instructor Best seller
We will even demonstrate many of the concepts in this Math 0-1: Linear Algebra for Data Science & Machine Learning 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.
