This repo is organized into various folders, each covering different aspects of machine learning.

Inside each folder, you'll find code examples and explanations to help you understand and practice each topic.

Folder	Description
01-python-for-ml	Basic Python libraries for machine learning.
Numpy	Introduction to Numpy basics.
Pandas	Introduction to Pandas basics.
Matplotlib	Introduction to Matplotlib basics.
SciPy	Introduction to Scipy basics.
02-data-preprocessing	Techniques and methods for preprocessing data before applying machine learning algorithms.
Data Preprocessing	Notebook on data preprocessing techniques.
03-exploratory-data-analysis	Exploratory Data Analysis (EDA) techniques to understand and visualize data.
EDA	Notebook on exploratory data analysis.
04-supervised-learning	Supervised learning methods including classification and regression techniques.
Classification	Notebook on classification techniques.
Regression	Notebook on regression techniques.
05-unsupervised-learning	Unsupervised learning techniques including clustering and dimensionality reduction methods.
Clustering	Notebook on clustering techniques.
Dimensionality Reduction	Notebook on dimensionality reduction techniques.
06-model-evaluation	Techniques for evaluating the performance of machine learning models.
Model Evaluation	Notebook on model evaluation techniques.