Learn matrix inversion, solving systems of linear equations, and elementary linear algebra using NumPy and SciPy in this video tutorial by Charles Kelly. These are explained in the context of computer science and data science to technologists and students
- [Instructor] The linear algebra file…in your exercises files folder is pre populated…with an import statement and a matrix named,…my first matrix.…When college mathematics departments offer…a linear algebra course, courses are typically taught…using a theoretical perspective.…When engineering departments offer a course,…courses are often used a computational perspective.…In either case, the course usually lasts one semester…and sometimes serves as an introduction…to more advanced mathematics courses.…
Why then am I offering a linear algebra as a short example?…The answer is that I assume that you already…understand linear algebra,…and the goal of this video is to teach you to use…num pies linear algebra capabilities.…Begin using this notebook,…go to the cell menu, and type run all.…Notice that the result of my first matrix…is a matrix, not an array or an ND array.…The documentation tells us that the constructor…for the MP.matrix function returned a matrix…from an array like object, or from a string of data.…
- Using Jupyter Notebook
- Creating NumPy arrays from Python structures
- Slicing arrays
- Using Boolean masking and broadcasting techniques
- Plotting in Jupyter notebooks
- Joining and splitting arrays
- Rearranging array elements
- Creating universal functions
- Finding patterns
- Building magic squares and magic cubes with NumPy and Python
Skill Level Intermediate
2. Create NumPy Arrays
3. Index, Slice, and Iterate
4. Plots: Matplotlib and Pyplot
5. Manipulate Arrays
6. Short Examples
7. Extended Examples
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