Back to Linear Algebra for Machine Learning and Data Science
Learner Reviews & Feedback for Linear Algebra for Machine Learning and Data Science by DeepLearning.AI
1,314 ratings
About the Course
Newly updated for 2024!
After completing this course, learners will be able to:
• Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.
• Apply common vector and matrix algebra operations like dot product, inverse, and determinants
• Express certain types of matrix operations as linear transformations
• Apply concepts of eigenvalues and eigenvectors to machine learning problems
Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning.
Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works.
Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.
This is a beginner-friendly program, with a recommended background of at least high school mathematics (functions, basic algebra). We also recommend a basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries), as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science. If you are already familiar with the concepts of linear algebra, Course 1 will provide a good review, or you can choose to take Course 2: Calculus for Machine Learning and Data Science and Course 3: Probability and Statistics for Machine Learning and Data Science, of this specialization.
Top reviews
NA
Jun 17, 2023
Very visual and application oriented and gives the context for machine learning and where linAL is applied in PCA and neural networks. The structure is really byte sized and fun to work with.
SP
Jul 26, 2023
This course is truly exceptional for individuals eager to strengthen their grasp of Linear Algebra concepts, paving the way for a deeper understanding of machine learning and data science.
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351 - 354 of 354 Reviews for Linear Algebra for Machine Learning and Data Science
By Ikram A
•Nov 5, 2023
only 1 star because, the quiz labs are not working properly.
By Atharva P
•Aug 7, 2023
Worst Linear Algebra course with all wrong coding solutions.
By Nivethitha S
•Oct 14, 2023
Last week 4 assignment didn't submitted
By Cameron S D
•May 11, 2023
Won't let me un-enroll!