Difference between Big Data and Machine Learning
Big Data: It is huge, large, or voluminous data, information, or relevant statistics acquired by large organizations and ventures. Many software and data storage created and prepared as it is difficult to compute 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 Primarily used in applications where prediction or decision-making is important, such as finance, manufacturing, and cybersecurity
Difference between Big Data and Machine Learning are as follows:
| Big Data | Machine 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 firms | Machine 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. |
| Big data analytics look for emerging patterns by extracting existing information which helps in the decision making process. | It teaches the machine by learning from existing data. |
| Problem: Dealing with large volumes of data. | Problem: Overfitting. |
| It stores large volumes of data and finds out patterns from data. | It learns from trained data and predicts future results. |
| It processes and transforms data to extract useful information. | Machine Learning uses data for predicting output. |
| It deals with High-Performance Computing. | It is a part of Data Science. |
| Volume, velocity, and variety of data | Building predictive models from data |
| Managing and analyzing large amounts of data | Making accurate predictions or decisions based on data |
| Descriptive and diagnostic | Predictive and prescriptive |
| Large volumes of structured and unstructured data | Historical and real-time data |
| Reports, dashboards, and visualizations | Predictions, classifications, and recommendations |
| Data storage, processing, and analysis | Regression, classification, clustering, deep learning |
| Data cleaning, transformation, and integration | Data cleaning, transformation, and feature engineering |
| Strong domain knowledge is often required | Domain knowledge is helpful, but not always necessary |
| Can be used in a wide range of applications, including business, healthcare, and social science | Primarily used in applications where prediction or decision-making is important, such as finance, manufacturing, and cybersecurity |
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