Machine-Learning

Welcome to the Machine Learning repository! This collection of Jupyter notebooks and code provides a guide to common machine learning techniques such as regression, classification, data preprocessing, PCA, and hyperparameter tuning. It also contains a section for "Deep Learning". Whether you're new to machine learning or a seasoned practitioner, these notebooks can help you practice and apply the techniques more easily.

This repository is organized into folders. Each folder contains a README.md and notebooks that demonstrate a machine learning concept.

📌 Please, feel free to contribute by forking the repository or by simply opening an issue.

See "Machine Learning Tasks"and the "Machine Learning Techniques" below to get an idea of the content of each folder.

Datasets

• Popular websites to find datasets.

Regression

Predict a continuous value.
E.g.: House price.

• Decision Tree Regression
• Multiple Linear Regression
• Polynomial Regression
• Random Forest Regression
• Simple Linear Regression
• Support Vector Regression

Classification

Predict a discrete value.
E.g.: Whether a patient has breast Cancer or not.

• Decision Tree Classification
• K Nearest Neighbors
• Kernel SVM
• Logistic Regression
• Naive Bayes
• Random Forest Classification
• Support Vector Machine

Clustering

Group similar entities.
E.g.: Group mall customers into categories.

• K-Means Clustering
• Hierarchical Clustering

Association Rule Learning

Identify relationships among variables.
E.g.: A customer who bought coffee is likely to buy sugar.

• Apriori
• Eclat

Dimensionality Reduction

Reduce the number of input variables while preserving essential information.

• Principal Component Analysis

Hyperparameter Tuning

Optimize the parameters of a model to improve its performance.

• RandomizedSearchCV

Deep Learning

Use neural networks to learn complex patterns and representations from data.

• Perceptron
• Multi-layer Perceptron
• Convolutional Neural Networks
• Auto-encoders
• Variational Auto-encoders
• Long Short-Term Memory
• Generative Adversial Networks
• Self-Organizing Maps

Natural Language Processing

The NLP folder lacks good resources at the moment. Interesting notebooks will be added later. As of now, it contains:

• Basics concepts: Tokenization, Stopwords, Stemming and Bag of words.
• Sentiment Analysis