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Data Mining vs Machine Learning– Key Differences Explained

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Introduction to Data Mining

Data mining extracts patterns from large data using stats, ML & databases, providing insights for business optimization, marketing & trend prediction.

How Does Data Mining Work?

– Data Collection: Gather large datasets from sources. – Data Cleaning: Remove noise & fix errors. – Pattern Recognition: Identify trends & correlations. – Evaluation & Interpretation: Analyze & visualize insights.

Data Mining in Marketing

Data Mining in Crime and Intelligence

Data Mining in Finance

Use Cases of Data Mining

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Introduction to Machine Learning

Machine learning, a subset of AI, enables systems to learn from data, improving predictions over time, shaping AI’s future, and driving industry advancements.

How Does Machine Learning Work?

– Data Collection: Gather labeled/unlabeled data. – Feature Selection: Choose key data attributes. – Model Training: Apply ML algorithms. – Model Evaluation: Test accuracy & performance. – Prediction & Optimization: Improve with new data.

Image Recognition

Stock Market Predictions

Natural Language Processing (NLP)

Use Cases of Machine Learning

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Data Mining vs Machine Learning – Key Differences

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Definition:

– Data Mining: Finds hidden patterns in data. – Machine Learning: Learns from data to predict outcomes.

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Goal:

– Data Mining: Extracts useful insights from data. – Machine Learning: Builds models to make decisions.

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Process:

– Data Mining: Uses statistical techniques to analyze data. – Machine Learning: Trains models using algorithms.

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Dependency:

– Data Mining: Relies on statistics and databases. – Machine Learning: Uses algorithms and training data.

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Output:

– Data Mining: Provides insights and patterns. – Machine Learning: Automates predictions and decisions.

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Adaptability:

Data Mining: Static process; doesn’t improve over time. Machine Learning: Continuously learns and adapts with data.

Which One is Better – Data Mining vs Machine Learning?

Neither is inherently better—data mining finds patterns in data, while machine learning predicts and automates. The best choice depends on your specific needs.

Conclusion

Data mining finds patterns, ML predicts. Together, they enhance insights, automation & decisions, boosting marketing, stock forecasting & customer experiences.