Welcome to the Machine Learning repository!
This repo includes clear, concise explanations and implementations of essential Machine Learning algorithms and concepts — perfect for beginners and those revising core topics.
Machine Learning (ML) is a subset of Artificial Intelligence (AI) that allows systems to learn and improve from experience without being explicitly programmed. ML focuses on the development of algorithms that can learn from and make predictions on data.
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Naive Bayes
- K-Means Clustering
- Hierarchical Clustering
- PCA (Principal Component Analysis)
- Bagging
- Boosting (AdaBoost, XGBoost)
- Perceptron
- Feedforward Neural Network
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Confusion Matrix
- Accuracy, Precision, Recall, F1 Score
- ROC Curve, AUC
- Python
- NumPy, Pandas
- Scikit-learn
- Matplotlib, Seaborn
- TensorFlow / PyTorch (for Deep Learning)
To clone this repository:
git clone https://github.com/pathkalavadiya/Machine_Learning.git
cd Machine_Learning