Big Data is more about information extraction and analysis from enormous amounts of data. Input data and algorithms are used increasingly often in machine learning to estimate unknowable future results.
What is Big Data?
Big data refers to immense, huge, or voluminous data, which includes valuable insights obtained by large organizations and is challenging to handle using traditional devices. Big data can consist of semi-structured, unstructured, or structured data. Data plays a crucial role in maintaining any business and is continuously expanding in size. A decade ago, organizations were capable of managing only gigabytes of data and faced storage issues. However, with the emergence of big data, organizations are now capable of handling petabytes and exabytes of data. They can store large volumes of data using cloud and big data frameworks like Hadoop.
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What is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI) that enables machines/systems to learn from previous experiences or patterns and accurately predict future events. It allows systems to learn from training data and make predictions or take actions using various algorithms. An ideal ML model does not require human intervention, although such models are not yet prevalent. Machine learning is a science that focuses on creating algorithms and programs that predict outcomes or optimize a system based on continuously generated data.
Difference between Big Data and Machine Learning
Machine Learning
- Definition: It uses data and algorithms to predict future outcomes based on patterns.
- Types: It can be categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
- Scope: Machine learning aims to create automated learning machines that make faster decisions, predict future events, and analyze data more effectively.
- Human Intervention: It does not need human involvement because machines learn on their own.
- Examples: Machine learning is used for better customer service, product recommendations, virtual assistance, email filtering, automation, and speech/text recognition.
Big Data
- Definition: It involves extracting and analyzing information from a large amount of data from different sources.
- Types: Big data can be structured, unstructured, or semi-structured.
- Scope: Big data has a vast scope beyond just handling large volumes of data. It focuses on improving data storage and analysis for better insights.
- Human Intervention: Human intervention is required because big data deals with a massive amount of complex data.
- Examples: Big data is useful in stock market analysis, healthcare, agriculture, gambling, environmental protection, and more.