This repository follows a comprehensive Machine Learning course covering both theory and practical implementation.
It includes the implementation of key machine learning algorithms, experiments with datasets, and projects exploring different aspects of supervised and unsupervised learning.

The goal of this repository is to build a strong understanding of how learning algorithms work — from mathematical foundations to Python implementation — using tools like NumPy, Pandas, and scikit-learn.

This repository documents my journey through the Machine Learning course, where I explored and implemented key concepts, algorithms, and models using Python and essential libraries such as NumPy, Pandas, and scikit-learn.

Throughout the course, I focused on understanding both the theoretical foundations and practical applications of machine learning.
The notebooks and code in this repository cover a wide range of topics, including:

• Linear and Logistic Regression
• Support Vector Machines (SVM)
• Ensemble Models (Bagging, Boosting, Random Forests)
• Neural Networks and Convolutional Neural Networks (CNNs)
• Principal Component Analysis (PCA)
• Clustering Techniques (K-Means, Hierarchical)
• Reinforcement Learning Basics

By completing this course and its associated projects, I have gained:

• A solid understanding of core machine learning algorithms and their mathematical intuition.
• Hands-on experience in building, training, and evaluating ML models using Python.
• Skills in data preprocessing, feature engineering, and model selection.
• The ability to analyze, visualize, and interpret machine learning results.
• Practical experience in applying ML concepts to real-world problems.

This repository serves as both a learning log and a reference collection for implementing and experimenting with different machine learning techniques.

• Linear Regression (from scratch and with scikit-learn)
• Logistic Regression
• Support Vector Machines (SVM)
• k-Nearest Neighbors (k-NN)
• Decision Trees and Random Forests

• Ensemble Methods (Bagging, Boosting)
• Neural Networks and CNNs
• PCA and Dimensionality Reduction
• Clustering (K-Means, Hierarchical)

• Data preprocessing and feature scaling
• Model evaluation and cross-validation
• Hyperparameter tuning
• Visualizing model performance

• Language: Python 3.x
• Libraries: NumPy, Pandas, Matplotlib, scikit-learn, TensorFlow / PyTorch (optional)
• Tools: Jupyter Notebook, Git, VS Code