Exploring Machine Learning
I’ve developed two libraries to delve into machine learning techniques. The ML repo delves into various methods, while MLtools provide in-depth exploration of commonly used libraries that assist in ML development, such as matplotlib, numpy, pandas, scipy, and more.
Machine Learning Techniques
The ML repo contains example code of most of the popular ML techniques. Created to be a useful quick reference quide for ML developement. Techniques ranging from:
- KNN
- Decision Tree
- Linear Regression
- Logistic Regression
- L1 and L2 Regularication
- Ensemble Methods
- Naive Bayes
- Supper Vector Regression (SVR)
- Random Forest
- Principal Component Analysis (PCA)
- CNN
Helpful tools and Libraries for ML
ML tools repo contain the breakdown of often used python libraries in conjuction with ML development. Created as a quick reference when trying to remember particular functions. The libraries include:
- Matplotlib
- Numpy
- Scipy
- Pandas
- Pivot Tables
- Pytube
- Mito
- Altair
- Tqdm
- Ipywidget
- Tmux
- breakdown of Python class and design structures