Open Source Machine Learning

Improve Your Machine Learning Accuracy

When you have large data sets – face recognition, spam filtering, recommendation engines – machine learning is the best option for pattern recognition or predictive analysis. Cheaper and more powerful open source tools make it easier to implement, but machine learning continues to struggle with accuracy. An 80% accuracy rate is not competitive in today’s business climate. The only way to increase accuracy is to use better data.

With CrowdFlower, you get the best of human intelligence combined with machine learning to give you useful results. Training data is now easily collected and labeled for you to connect to your machine learning algorithms. You can also retrain struggling algorithms with our humans-in-the loop active learning process. At CrowdFlower, we also believe that open data is the new open source. Our open source library, Data for Everyone, can provide more free data for your machine learning algorithms to get better results.

Using CrowdFlower, you will:

  • Upload your data. Your data can be video, audio, text, or your mission-critical system of record
  • Design and launch your job. Customize your design settings and our human-in-the-loop platform will route the tasks based on your requirements.
  • Use your results. Download your enriched data, or use machine learning and humans-in-the-loop to make active learning work for you.

Download our whitepaper today and see how we can help you improve your machine learning accuracy.