Skip to content
Related Articles

Related Articles

Improve Article
Save Article
Like Article

Difference between Big Data and Machine Learning

  • Difficulty Level : Hard
  • Last Updated : 05 May, 2020

Big Data: It is huge, large or voluminous data, information, or the relevant statistics acquired by the large organizations and ventures. Many software and data storage created and prepared as it is difficult to compute the big data manually. It is used to discover patterns and trends and make decisions related to human behavior and interaction technology.

Machine Learning: Machine learning is a subset of artificial intelligence that helps to automatically learn and improve the system without being explicitly programmed. Machine learning is applied using Algorithms to process the data and get trained for delivering future predictions without human intervention. The inputs for Machine Learning is the set of instructions or data or observations.


Below is a table of differences between Big Data and Machine Learning:

Big DataMachine Learning
Big Data is more of extraction and analysis of information from huge volumes of data.Machine Learning is more of using input data and algorithms for estimating unknown future results.
Types of Big Data are Structured, Unstructured and Semi-Structured.Types of Machine Learning Algorithms are Supervised Learning and Unsupervised Learning, Reinforcement Learning.
Big data analysis is the unique way of handling bigger and unstructured data sets using tools like Apache Hadoop, MongoDB.Machine Learning is the way of analysing input datasets using various algorithms and tools like Numpy, Pandas, Scikit Learn, TensorFlow, Keras.
Big Data analytics pulls raw data and looks for patterns to help in stronger decision-making for the firmsMachine Learning can learn from training data and acts like a human for making effective predictions by teaching itself using Algorithms.
It’s very difficult to extract relevant features even with latest data handling tools because of high-dimensionality of data.Machine Learning models work with limited dimensional data hence making it easier for recognizing features
Big Data Analysis requires Human Validation because of large volume of multidimensional data.Perfectly built Machine Learning Algorithms does not require human intervention.
Big Data is helpful for handling different purposes including Stock Analysis, Market Analysis, etc.Machine Learning is helpful for providing virtual assistance, Product Recommendations, Email Spam filtering, etc.
The Scope of Big Data in the near future is not just limited to handling large volumes of data but also optimizing the data storage in a structured format which enables easier analysis.The Scope of Machine Learning is to improve quality of predictive analysis, faster decision making, more robust, cognitive analysis, rise of robots and improved medical services.

My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!