Python for Linear Algebra — Learning Portfolio

Author: Jeshurun Nana Kojo Ansah GitHub: @IntentionedReflex35 Course Platform: Udemy Status: In Progress

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Overview

This portfolio documents my personal learning journey through a Udemy course on Python for Linear Algebra. It contains hands-on Jupyter Notebook assignments covering core concepts from basic arithmetic to matrix operations — building a strong mathematical and computational foundation using Python.

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Notebook Index

#	Notebook	Topics Covered	Last Modified
1	1variablesandarithmetic.ipynb	Python variables, arithmetic operators, expressions	Apr 2025
2	2TheNumpymodule.ipynb	NumPy arrays, array operations, broadcasting	Mar 2026
3	3TheMatplotlibmodule.ipynb	Data visualization, plotting vectors & matrices	Feb 2025
4	4vectorandscalar.ipynb	Scalars vs vectors, vector notation, NumPy vectors	Jun 2025
5	5TheVectorDotProduct.ipynb	Dot product, geometric interpretation, applications	Dec 2025
6	6Matrices.ipynb	Matrix structure, indexing, basic matrix operations	Jan 2026
7	7TransposingVectors&Matrices.ipynb	Transpose operation, row vs column vectors	Jan 2026
8	8MatrixMultiplication.ipynb	Matrix multiplication rules, NumPy matmul, use cases	Jan 2026
9	9TheMatrixInverse.ipynb	Inverse matrices, conditions for invertibility, numpy.linalg	Feb 2026

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Tools & Technologies

• Language: Python 3
• Environment: Jupyter Notebook
• Key Libraries:
  • NumPy — numerical computing and array operations
  • Matplotlib — plotting and visualization
  • numpy.linalg — linear algebra operations

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Key Concepts Learned

• Fundamental Python programming for mathematical computation
• Working with scalars, vectors, and matrices using NumPy
• Visualizing mathematical concepts with Matplotlib
• Understanding and computing the dot product
• Performing matrix multiplication and verifying with manual calculation
• Transposing vectors and matrices
• Computing matrix inverses and understanding when they exist

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Learning Goals

• Understand Python basics for numerical computing
• Master NumPy for linear algebra operations
• Visualize vectors and matrices graphically
• Implement core linear algebra operations from scratch and with libraries
• Apply these concepts to machine learning foundations
• Extend portfolio with real-world data projects

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How to Run

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git

2. Navigate to the project folder:

   cd Python-For-Linear-Algebra

3. Install dependencies:

   pip install numpy matplotlib jupyter

4. Launch Jupyter Notebook:

   jupyter notebook

5. Open any .ipynb file and run the cells sequentially.

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Notes

This portfolio was built as part of a structured Udemy course. Each notebook reflects my personal work on the course assignments, written and executed independently as part of my self-directed learning practice.

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Last updated: May 2026