ML algorithms from Scratch!
Machine Learning algorithm implementations from scratch.
You can find Tutorials with the math and code explanations on my channel: Here
- Linear Regression
- Logistic Regression
- Naive Bayes
- Decision Tree
- Random Forest
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
Installation and usage.
This project has 2 dependencies.
numpyfor the maths implementation and writing the algorithms
Scikit-learnfor the data generation and testing.
Matplotlibfor the plotting.
Pandasfor loading data.
NOTE: Do note that, Only
numpy is used for the implementations. Others
help in the testing of code, and making it easy for us, instead of writing that
too from scratch.
You can install these using the command below!
# Linux or MacOS pip3 install -r requirements.txt # Windows pip install -r requirements.txt
You can run the files as following.
python -m mlfromscratch.<algorithm-file>
<algorithm-file> being the valid filename of the algorithm without the extension.
For example, If I want to run the Linear regression example, I would do
python -m mlfromscratch.linear_regression