Welcome to the "Machine Learning With Python" repository! This repository is a comprehensive collection of scripts and notebooks dedicated to the exploration and application of various machine learning algorithms, ranging from fundamental techniques to advanced methodologies. The repository is structured into two main sections: "ML Fundamentals" and "ML Advanced."

Repository Structure ML Fundamentals: This folder contains introductory and foundational scripts and notebooks in machine learning. Here, you will find implementations of basic machine learning algorithms, including different types of regression and classification models. This section is perfect for those who are new to machine learning or wish to revise basic concepts.

ML Advanced: In this folder, you will discover more sophisticated machine learning techniques. This includes scripts utilizing advanced algorithms and frameworks such as Keras, along with methodologies like k-means clustering and Principal Component Analysis (PCA). This section is tailored for individuals who are looking to dive deeper into complex machine learning concepts and applications.

Contents ML Fundamentals Linear Regression Logistic Regression Decision Trees Random Forest Classifier Support Vector Machines (SVM) Naive Bayes K-Nearest Neighbors (KNN) And more... ML Advanced Neural Networks with Keras Deep Learning Techniques K-Means Clustering PCA for Dimensionality Reduction Advanced Ensemble Techniques Gradient Boosting Machines Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) And more... Getting Started To get started with this repository, clone it to your local machine using:

bash Copy code git clone https://github.com/rezart95/Machine_Learning_with_Python.git Save to grepper Navigate into the desired folder (ML Fundamentals or ML Advanced) to access the scripts and notebooks. Each script is accompanied by comments and explanations to aid your understanding.

Prerequisites Before running the scripts, ensure you have the following installed:

Python 3.x Jupyter Notebook Required Python libraries: numpy, pandas, scikit-learn, keras, matplotlib, seaborn, etc. You can install the required libraries using the following command:

Contributing Contributions to this repository are welcome. Please feel free to submit pull requests or open issues to suggest improvements or add new content.