Big Data vs Machine Learning
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 |
Conclusion
Big data and machine learning are both powerful tools with their own strengths and weaknesses. Big data is better for storing and analyzing large datasets, while machine learning is better for making predictions and insights from data. They are complementary technologies, and each one enhances the capabilities of the other.
Difference between Big Data and Machine Learning
In today’s world where information is abundant, big data and machine learning have emerged as transformative forces that have revolutionized various industries and shaped the digital landscape. Although they are sometimes used interchangeably, they are distinct yet interconnected domains that have profound implications. Big data and Machine learning share a symbiotic relationship despite their distinct natures. As we move forward, the interplay between these two technologies will continue to transform our world. The ability to harness the power of Big data and machine learning will be crucial for addressing complex challenges, optimizing decision-making, and unlocking new frontiers of innovation.
Big Data vs Machine Learning
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