About:

Math exercises paralleling my coursework in Linear Algebra, Statistics and Machine Learning with Python.

Repository Contents:

1. 01_dot_product_vectors.ipynb:

   • Linear Algebra: Computing Dot Products from Matrix Columns as Vectors

   • This Python Jupyter notebook consists of my solution to an exercise from Mike X. Cohen's Linear Algebra course on Udemy.

2. 02_histogram_proportion.ipynb:

   • Statistics: Converting a Distribution from Raw Count to Proportion

   • This Python Jupyter notebook consists of my solution to an exercise from Mike X. Cohen's Statistics & Machine Learning course on Udemy.

3. 03_linear_v_log_plots.ipynb:

   • Statistics: Comparing Linear and Log-Scaled Plots

   • This Python Jupyter notebook consists of my solution to an exercise from the Data Visualization section of Mike X. Cohen's Statistics & Machine Learning course on Udemy.

4. 04_trace_lineear.ipynb:

   • Linear Algebra: Is Trace a Linear Operator?

   • This Python Jupyter notebook consists of my solution to an exercise from Mike X. Cohen's Linear Algebra course on Udemy.

5. 05_centr_tend_comparisons.ipynb:

   • Comparing MEAN vs. MEDIAN Relationships between Distributions with:

     1. Small vs. Large Outliers

     2. Small vs. Large Dataset Sizes

   • This Python Jupyter notebook consists of my solution to an exercise from the Data Visualization section of Mike X. Cohen's Statistics & Machine Learning course on Udemy.

6. 06_3D_transform_matrix.ipynb:

   • 3D Transformation Matrices in Matrix-Vector Multiplication: Pure Stretch vs. Rotate & Stretch vs. Pure Rotation.

   • This Python Jupyter notebook consists of suggested extra work to supplement a lesson in 2D Transformation Matrices from Mike X. Cohen's Linear Algebra course on Udemy.

7. 07_geomtrans_matmult.ipynb:

   • Performing 3D Transformations via Matrix Multiplications: Generate a Circle and Experiment with Different Transformation Matrices.

   • This Python Jupyter notebook consists of suggested extra work to supplement a matrix transformation multiplication coding challenge from Mike X. Cohen's Linear Algebra course on Udemy.

8. 08_poisson_pop_samp.ipynb:

   • Comparing Population vs. Sample Variance in Poisson Distrubutions.

   • This analysis supplements a lesson from the Descriptive Statistics section of Mike X. Cohen's Statistics & Machine Learning course on Udemy.

9. 09_fourier_trans_mult.ipynb:

   • Matrix Multiplication: Fourier Transform.

   • This Python Jupyter notebook consists of my solution to a math/coding challenge from Mike X. Cohen's Linear Algebra course on Udemy.

About Me:

• My name: Andrew Blais

• My website & Python/JavaScript webDev portfolio: https://www.andrewblais.dev/

• Studying Software Engineering since 2022.

• Hoping to find a Junior-Programmer Position or Internship in the next year.

• Interested in working with others toward AI Alignment and Safety.

Courses:

• Completed two comprehensive Python bootcamps

• Currently studying two JavaScript Web Development courses

• Also studying Linear Algebra, Statistics and Machine Learning through theory and Python implementation.

Programming Skills:

• Python:

  • All the Python basics and intermediates, including OOP

  • Full-Stack Development, specializing in Flask

  • NumPy, SymPy, Matplotlib, Seaborn, Plotly, Jupyter Notebooks

  • Data Science, matrix manipulation, mathematical calculation with Python

  • LaTeX formatting and outputting formatted math equations programatically

  • Currently working on Data Structures and Algorithms for general skill and coding interviews

• JavaScript:

  • Full-Stack JavaScript Development

  • Comfortable working with CSS and HTML

  • Node.js and Express, ejs