ML Algorithms
- [Vienna, Austria - Jacek Dylag]
- Overview
There are many different types of ML algorithms, depending on the goals of your ML project, how data is fed into the algorithm, and how you want the algorithm to "learn." Here are some ML algorithms:
- Linear Regression: Predicts a continuous value by fitting a linear relationship between input features and the target variable.
- Logistic Regression: Used for binary classification by modeling the probability of a class using a logistic function.
- Decision Trees: Classifies or regresses by splitting data into subsets based on feature values, creating a tree-like structure.
- Support Vector Machines (SVM): Finds a hyperplane that best separates data into different classes in a high-dimensional space.
- K-Nearest Neighbors (KNN): Classifies a data point based on the majority class of its nearest neighbors.
- K-Means Clustering: Partitions data into K clusters by minimizing intra-cluster variance.
- Hierarchical Clustering: Builds a tree of clusters by iteratively merging or splitting clusters based on feature similarity.
- Principal Component Analysis (PCA): Reduces the dimensionality of data while retaining the most important features.
- Q-Learning: A reinforcement learning algorithm where an agent learns to take actions in an environment to maximize cumulative rewards.
- Gradient Boosting (XGBoost, LightGBM): Ensemble methods that combine weak learners (typically decision trees) to improve predictive accuracy.
- Random Forests: Uses multiple decision trees for classification or regression, with each tree trained on a random subset of data.
- Convolutional Neural Networks (CNN): Used for image classification by applying filters to extract features from images.
- Recurrent Neural Networks (RNN): A neural network designed for sequential data (e.g., time series, text) by maintaining internal state across time steps.
- Transformers: Deep learning architecture used for Natural Language Processing tasks, relying on self-attention mechanisms to process sequential data.
- [Machine Learning Cheat Sheet]
- Some ML Techniques and Algorithms
Some key machine learning techniques and algorithms include: linear regression, logistic regression, support vector machines (SVM), Naive Bayes, decision trees, random forests, K-Nearest Neighbors (KNN), clustering, dimensionality reduction, gradient boosting, and AdaBoost.
The choice of which algorithm to use depends on the specific data and problem you're trying to solve, factors like data size, quality, and desired outcome playing a significant role.
Here are some ML algorithms:
- Logistic regression: A ML technique that is good for binary classification problems. It uses a logistic function at its core.
- Decision tree: A ML technique that uses rules and conditions to solve classification problems. It involves dividing input data into two or more homogeneous data sets based on defining attributes.
- Support vector machine: A ML method that uses statistical theory to solve fitting accuracy and generalization problems. It is used in pattern recognition, information security, and data fitting.
- Naive Bayes: A ML algorithm that is used when the output variable is discrete. It is driven by the Bayes Theorem.
- Random forest: A ML process that consists of many decision trees. A decision tree is a tree-like structure where each internal node represents a test on the input attribute.
- Clustering: A ML technique that involves grouping data points. It is an unsupervised learning method and a famous technique for statistical data analysis.
- Hyperparameters: An integral part of machine learning code that lets you control the code without directly modifying it.
- Gradient descent: A famous optimization technique that is used in machine learning and in deep learning. Its main purpose is to minimize the cost function.
[More to come ...]
