Vatsal Ghiya a serial entrepreneur with more than 20 years of experience in AI software has shared some keynote on how to automate data labeling in Machine Learning(ML) in this latest guest feature.
Key Takeaways from Article are-
- No matter the kind of AI system you need, data is the first priority and it must be quality data so that you can get accurate results. As we have seen data is massive and quality should be maintained, processing both these accurately is a mammoth task. You can get data from internal resources, CRM, analytics, sheets, landing pages, and others.
- Also, data can be downloaded as per niche, demographics, and market segment. There are government websites, Kaggle datasets, archives, and more. Moreover, to maintain the quality of data, it needs to be cleaned and labeled with appropriate details and that’s where machine learning came into existence.
- Three methods that can automate data modeling in machine learning are reinforcement learning, supervised learning, and unsupervised learning. Using this learning, data labeling can be automated efficiently in machine learning with accurate meta details and critical factors.
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