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Data scientists, machine learning engineers, researchers, and developers building AI-powered solutions.
Users input their datasets, select ML algorithms, and train models using the software. The software evaluates model accuracy and lets users fine-tune parameters. After training, models can be exported or deployed for real-world use.
Generally, yes. Most ML software requires coding knowledge, especially in Python or R.
Many ML tools are designed to work with large datasets, but some may need additional infrastructure.
Both have pros and cons; open-source offers flexibility, while commercial software may offer better support.
ML software provides metrics like accuracy, precision, recall, and F1-score to assess models.
Yes, most ML software supports model deployment via APIs or embedded solutions.
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