Python: matrix multiplication 2x3 3x1 by hand and NumPy matmul. Dimensions compatible. Size Result.

When multiplying two matrices, it's important to understand the dimensional compatibility of the matrices, as well as the rules of matrix multiplication. In this case, you are multiplying a 2x3 matrix by a 3x1 matrix, so the resulting product will be a 2x1 matrix.

Here are some of the key concepts and skills you should learn when multiplying two matrices of this size:

Matrix Dimensions: Understanding the dimensions of the matrices you are working with and ensuring that they are compatible for multiplication. In this case, the number of columns in the first matrix (3) must match the number of rows in the second matrix (3).

Matrix Entry Computation: Calculating the entries of the resulting matrix by multiplying corresponding elements from each matrix and summing them up.

Matrix Multiplication Rules: Following the rules of matrix multiplication, including the order of operations, to ensure that the calculation is done correctly.

Numpy Matmul Function: Familiarizing yourself with the numpy matmul function, which provides an efficient and convenient way to perform matrix multiplication in Python.

By learning these skills and concepts, you will gain a deeper understanding of matrix multiplication and be able to perform complex computations accurately and efficiently. This will be an important step in your journey towards becoming a proficient data scientist.

In short, whether you are a beginner or an experienced data scientist, learning how to multiply matrices by hand and using the numpy matmul function is an essential step in your journey towards becoming a proficient data scientist. So, take the time to learn these skills and you will be well on your way to unlocking the full potential of data science.

Table of Contents
----------------------------
00:00 Hey Team
00:17 Intro to problem
00:21 Determine if dimensions are compatible
00:47 Figure out the size of the result matrix
00:59 Calculate by Hand
01:42 Solve using NumPy matmul
02:52 Closing message

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