A documentation of each project, each skill set, and new algorithm I learned throughout my time studying the Machine Learning world. I am currently starting with a blank slate; some conceptual understanding from prior attempts at ML but overall - not much. This will be a repo reflecting on my progress and proof of the skills I am embarking on.
Each Python file will be coated in comments - comments may vary from descriptive to comedic - nonetheless, they will be informative on debugging struggles and newly learned concepts. Each file will hold these requirements with integrity. Also, it goes without saying that there will be CSV files provided.
- Regression ... everything from scratch
- Univariate with Gradient Descent ( y = mx + b )
- Multiple Variable with Gradient Descent ( y = mx[] + b )
- Non-Linear multi-variable projects
- Classification
- Logistic Regression (1/(1+e^-z)) ... scratch
- Neural Network Binary Classification ... tensorflow
... to be extended