About:

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

Repo Note:

• This repository is broken into two parts/directories:

  • ./studyProjects

    • Consists of exercises/problems based directly or closely extrapolating on my coursework.

      • See Mike X. Cohen's courses at Udemy.

  • ./capstoneProjects

    • Entirely original problems/plots/code.

/studyProjects:

1. ./studyProjects/stu01_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. ./studyProjects/stu02_hist_perc_prop.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. ./studyProjects/stu03_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. ./studyProjects/stu04_trace_linear.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. ./studyProjects/stu05_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. ./studyProjects/stu06_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. ./studyProjects/stu07_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. ./studyProjects/stu08_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. ./studyProjects/stu09_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.

10. ./studyProjects/stu10_mat_red_rank_sca_mult.ipynb:

    • Matrix Multiplication:

      • Creating Reduced-Rank Matrices

      • Investigating Effect of Scalar on Rank as a Linear Operator

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

11. ./studyProjects/stu11_histogram_bins.ipynb:

    • Number of Histogram Bins: Different Methods of Calculating k

    • This Python Jupyter Notebook is my extrapolation on a coding lesson from the Descriptive Statistics section of Mike X. Cohen's Statistics & Machine Learning course on Udemy.

12. ./studyProjects/stu12_vec_col_mat.ipynb:

    • Visualization: Is some Vector in the Column Space of some Matrix?

      • $\textsf{v}\in\textsf{\textit{C}(\textbf{M})}?$

    • This Python Jupyter notebook extrapolates from an exercise in Mike X. Cohen's Linear Algebra course on Udemy.

13. ./studyProjects/stu13_dual_violins.ipynb:

    • Constraining, Measuring and Plotting exp(Gaussian) Distributions

    • This Python Jupyter Notebook is my extrapolation on a coding lesson from the Descriptive Statistics section of Mike X. Cohen's Statistics & Machine Learning course on Udemy.

14. ./studyProjects/stu14_entropy_hist_bins.ipynb:

    • Computing Entropy Based on an Increasing Number of Histogram Bins

    • This Python Jupyter Notebook is my extrapolation on a coding lesson from the Descriptive Statistics section of Mike X. Cohen's Statistics & Machine Learning course on Udemy.

15. ./studyProjects/stu15_invert_minmax.ipynb:

    • Convert a Distribution using Min-Max Scaling, Invert Back and Compare

    • This Python Jupyter Notebook is my answer to a coding challenge from the Data Normalization and Outliers section of Mike X. Cohen's Statistics & Machine Learning course on Udemy.

/capstoneProjects:

1. ./capstoneProjects/cap01_tbd.ipynb:

   • Just a template for now, soon to be completed...

About Me:

• My name: Andrew Blais

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

• Studying Software Engineering and related Mathematics (Statistics, Linear Algebra, Calculus) 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

  • Currently learning React