Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors

Machine learning algorithms for Big data

Photo of author

By Team EasyExamNotes

When working with “Big Data,” many different machine learning algorithms can be used to analyse the data and get useful information from it.

Here are some well-known machine learning algorithms and tools that are often used with Big Data processing systems like Hadoop, Spark, Hive, NoSQL databases, MapReduce, and Storm:

  1. Linear Regression: A basic regression algorithm used to model the relationship between a dependent variable and one or more independent variables. It can be used for prediction and forecasting tasks.
  2. Logistic Regression: Used for binary classification problems, where the goal is to predict a binary outcome (e.g., yes/no, true/false).
  3. Decision Trees: A versatile algorithm used for classification and regression tasks. It can handle both categorical and numerical data.
  4. Random Forest: An ensemble learning method that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting.
  5. Gradient Boosting Machines (GBM): Another ensemble technique that builds multiple weak learners (typically decision trees) sequentially, where each new tree corrects the errors of the previous one.
  6. K-Nearest Neighbors (KNN): A simple algorithm that classifies data points based on the majority class of their K-nearest neighbors.
  7. Support Vector Machines (SVM): Suitable for both classification and regression tasks, SVM aims to find the optimal hyperplane that best separates data points of different classes.
  8. Naive Bayes: A probabilistic classification algorithm based on Bayes’ theorem, often used for text classification and spam filtering.
  9. Clustering Algorithms (e.g., K-Means): Used to group similar data points into clusters, helpful for segmentation and pattern recognition tasks.
  10. Neural Networks and Deep Learning: Artificial neural networks, including deep learning architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are utilized for complex tasks such as image recognition, natural language processing, and speech recognition.

Regarding Big Data processing frameworks and technologies:

  1. Hadoop: An open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses the Hadoop Distributed File System (HDFS) and MapReduce for data processing.
  2. Apache Spark: Another popular distributed computing framework that supports in-memory processing and offers various APIs for batch processing, real-time streaming, machine learning (Spark MLlib), and graph processing.
  3. Apache Hive: A data warehousing and SQL-like query language built on top of Hadoop, allowing users to query and analyze data using SQL commands.
  4. NoSQL Databases: Various NoSQL databases, such as MongoDB, Cassandra, HBase, and Couchbase, are utilized to handle large volumes of unstructured and semi-structured data.
  5. MapReduce: A programming model for distributed computing, commonly used in Hadoop, where large datasets are processed in parallel across a cluster.
  6. Apache Storm: A real-time stream processing system, suitable for applications that require low-latency data processing and analysis.
Download as PDF

Related posts:

  1. What is Machine Learning ?
  2. Types of Machine Learning ?
  3. Applications of Machine Learning
  4. Data Preprocessing
  5. Data Cleaning
  6. Handling Missing Data
  7. Feature Scaling
  8. Labeled data in Machine learning
  9. Difference between Supervised vs Unsupervised vs Reinforcement learning
  10. Difference between Supervised vs Unsupervised vs Reinforcement learning
  11. What is training data in Machine learning
  12. What is Ordinary Least Squares (OLS) estimation
  13. Scalar in Machine Learning
  14. Scalars in Loss Functions | Machine Learning
  15. Linear Algebra for Machine Learning Practitioners
  16. Supervised Learning
  17. Top Interview Questions and Answers for Supervised Learning
  18. Define machine learning and explain its importance in real-world applications.
  19. Differences Between Machine Learning and Artificial Intelligence
  20. Machine Learning works on which type of data ?
  21. What is target variable and independent variable in machine learning
  22. Machine Learning Scope and Limitations
  23. What is Regression in Machine learning
  24. Statistics and linear algebra for machine learning
  25. Finding Machine Learning Datasets
  26. What is hypothesis function and testing
  27. Explain computer vision with an appropriate example
  28. Explain Reinformcement learning with an appropriate exaple
  29. Reinforcement Learning Framework
  30. Data augmentation
  31. Normalizing Data Sets in Machine Learning
  32. Machine learning models
  33. Unsupervised machine learning
  34. Neural Network in Machine Learning
  35. Recurrent neural network
  36. Support Vector Machines
  37. Long short-term memory (LSTM) networks
  38. Convolutional neural network
  39. How to implement Convolutional neural network in Python
  40. What is MNIST ?
  41. What does it mean to train a model on a dataset ?
  42. Can a textual dataset be used with an openCV?
  43. Name some popular machine learning libraries.
  44. Introduction to Machine Learning
  45. Some real time examples of machine learning