Machine Learning Cheat Sheet

These Notebooks Contain:

Explaining Various machine learning algorithms concepts and implementing them using libraries like scikit learn.
Implementing various machine learning algorithms from scratch:
- Logistic Regression
- KNN
- Decision Tree
- Gaussian Mixture Models
- LDA
- QDA
- Naive Bayes
- PCA
How to use Pandas and Scikit Learn libraries for data wrangling and preprocessing.
How to perform hyperparameter tuning using various methods and packages.
Explanation for Feature Engineering, Feature Selection, and Feature Extraction and how to implement some of these methods.

NOTE: there is no deep learning here.
NOTE: still ongoing project (last update: August 2022)

Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
1-Data Wrangling and Preprocessing.ipynb		1-Data Wrangling and Preprocessing.ipynb
2-Distance Based Models.ipynb		2-Distance Based Models.ipynb
3-Linear Models (Gradient Descent Based).ipynb		3-Linear Models (Gradient Descent Based).ipynb
4-Probabilistic Models.ipynb		4-Probabilistic Models.ipynb
5-Tree Based Models.ipynb		5-Tree Based Models.ipynb
6-Dimentionality Reduction.ipynb		6-Dimentionality Reduction.ipynb
7-Hyperparameters Tuning.ipynb		7-Hyperparameters Tuning.ipynb
README.md		README.md

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