Welcome to the Machine Learning Projects Repository! This repository contains a diverse collection of machine learning projects and algorithms, providing resources for both beginners and experts to explore various aspects of machine learning.
1- Supervised Learning:
a- *Regression: Linear regression, polynomial regression, and more advanced techniques like Ridge and Lasso regression.
b- Classification: Implementations of algorithms such as logistic regression, support vector machines (SVM), decision trees, random forests, and k-nearest neighbors (KNN).
2- Unsupervised Learning:
a- Clustering: K-means, hierarchical clustering, DBSCAN, and Gaussian Mixture Models (GMM).
b- Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE, and UMAP.
3- Deep Learning:
a- Neural Networks: Basics of neural networks, multi-layer perceptrons (MLPs).
b- Convolutional Neural Networks (CNNs): For image classification and object detection tasks.
c- Recurrent Neural Networks (RNNs): For sequence prediction tasks, including LSTMs and GRUs.
d- Natural Language Processing (NLP): Text Preprocessing: Tokenization, stemming, lemmatization.
e- Models: Implementations of models like Word2Vec, TF-IDF, and transformer-based models like BERT.
4- Reinforcement Learning:
a- Basic Algorithms: Q-learning, SARSA.
b- Advanced Techniques: Deep Q Networks (DQN), policy gradients, and actor-critic methods.
4- Data Preprocessing and Feature Engineering:
a- Techniques for handling missing data, feature scaling, and encoding categorical variables.
b- Model Evaluation and Tuning: Cross-validation, hyperparameter tuning, and performance metrics.