AI Skills

 ### AI Skills \


#### Binary Classification Algorithms

- **Naive Bayes**: This algorithm guesses the class of data based on probabilities.

- **Logistic Regression**: It predicts if something belongs to a category or not, like yes or no.

- **K-Nearest Neighbors (KNN)**: It classifies data by looking at the closest examples in the dataset.

- **Support Vector Machine (SVM)**: It finds the best line that separates different classes of data.

- **Decision Tree**: It makes decisions by splitting data into branches based on features.

- **Random Forest**: It uses many decision trees to make more accurate predictions.

- **Neural Network (Deep Learning)**: It mimics the human brain to learn from data and make predictions.


#### Unsupervised Machine Learning Algorithms

- **K-means Clustering**: It groups similar data points into clusters.

- **PCA (Principal Component Analysis)**: It reduces the number of features in data while keeping important information.

- **CNN (Convolutional Neural Network)**: It is used for image recognition by processing data in layers.

- **SVMs (Support Vector Machines)**: They also classify data without labeled examples.

- **K-modes**: It is like K-means but used for categorical data clustering.


#### Dimensionality Reduction Algorithms

- **Linear Methods (PCA, LDA)**: These methods simplify data by reducing features while keeping the most important parts.

- **Non-Linear Methods (Kernel PCA)**: They reduce features by transforming data into higher dimensions for better separation.


#### Supervised Machine Learning Models

- **RAG (Retrieval-Augmented Generation)**: It enhances models by adding information retrieval to generate better results.

- **Logistic/Linear Regression**: They predict outcomes based on relationships between features.

- **Multiple Linear Regression (MLR)**: It predicts a target value using multiple features.

- **Decision Tree**: Splits data into branches to make predictions.

- **Random Forest**: Uses many decision trees for better accuracy.

- **Gradient Boosting**: Combines weak models to create a strong model.

- **XGBoost**: An advanced version of gradient boosting for better performance.

- **Likelihood of Model**: Measures how well the model fits the data.

- **Support Vector Machine (SVM)**: Finds the best boundary to separate different classes.


#### Other Techniques

- **Genetic Algorithm**: It uses the process of natural selection to find optimal solutions.

- **Simulated Annealing**: It is a method to find good solutions by slowly cooling down the search process to avoid getting stuck in bad solutions.

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