Data Mining and Machine Learning
Both data mining and machine learning are rooted in #datascience and generally fall under that category. They often intersect or are confused with each other, but there are a few key contrasts between the two. The major difference between machine learning and data mining is how they are used and applied in our everyday lives. Data mining can be used for a variety of purposes, including financial research, Investing, sales trends and marketing. Machine learning visible form of the principles of data mining, but can also make automatic correlations and learn from them to apply to new algorithms
- Machine learning and statistics
- Machine learning tools and techniques
- Fielded applications
- Generalization as search
- Bayesian networks
Related Conference of Data Mining and Machine Learning
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8th International Conference on Artificial Intelligence, Machine Learning and Robotics
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3rd World Summit on Big Data, Machine Learning and Artificial Intelligence
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11th World congress on Big Data, Computer science, Analytics and Data Mining
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11th International Conference and Exhibition on Mechanical & Aerospace Engineering
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24th International Conference on Big Data & Data Analytics
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7th Annual Summit on Satellite and Space Communication Technology
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Data Mining and Machine Learning Conference Speakers
Recommended Sessions
- Artificial Intelligence
- Big Data Analytics
- Big Data Technologies
- Business Analytics
- Cloud Computing
- Clustering
- Data Mining Methods and Algorithms
- Forecasting from Big Data
- Internet of Things (IOT)
- Nanoinformatics
- New Visualization Techniques
- Renewable Energy Forecasting with Big Data
- Social Network Analysis
- Big Data Algorithm
- Big Data Applications, Challenges and Opportunities
- Big Data in Nursing Research
- Big Data Optimization
- Complexity and Algorithms
- Data Mining and Machine Learning
- Data Mining Applications in Science, Engineering, Healthcare and Medicine
- Data Mining Tasks, Processes and Analysis
- Data Mining Tools and Software
- Data Privacy and Ethics
- Data Warehousing
- Frequent Pattern Mining
- Open Data
- Search and Data Mining
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