Machine Learning (ML) is a new field and there is a competitive advantage to any first adopters. Yet, many companies are buying ML technologies and developing strategies before they have taken the appropriate step to evaluate whether their data is ready or not.
Ted Sfikas, Director for Solutions Consulting, North America and LATAM, recently did a webinar on “Is Your Company’s Data Ready for Machine Learning.”
In this webinar, Ted takes us through the history and background of data science, what ML is, what a data supply chain’s role in an ML environment is and ML use cases. The webinar includes key takeaways such as:
What is Data Science?
- A concept to unify statistics, data analysis, ML and their related methods in order to understand and analyze actual phenomena with data
- It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science and computer science
- Data science brings these things together to produce new insights and assist in decision making – resulting in a product or service
Key Common Terms
- Data Mining and Knowledge in Databases (KDD) is the unifying practice in data science, it essentially explains patterns
- Statistics – quantifies numbers
- Machine Learning – predicts with models
- Artificial Intelligence – behaves and reasons
Statistics quantifies numbers, Machine Learning predicts with models and Artificial Intelligence behaves and reasons.
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What is Machine Learning (ML)?
- Mathematical computations that “learn” from input datasets without relying on a predetermined equation input by humans
- The model behind ML is provided by a human, and it defines the relationship between the input data (features) and the thing to predict (label)
ML models are cyclically trained with labelled data, then applied for inference to unlabelled data to improve the model. So predictions can improve throughout as the cycle continues.
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2 Types of Machine Learning
- Unsupervised Learning – group and interpret data based only on input data
- Supervised Learning – develop predictive models based on both input and output data
To get more key takeaways and knowledge around all things ML watch the on-demand webinar and learn:
- Why the fundamental need for data readiness is so crucial when it comes to ML
- Key steps to evaluate the readiness of your data
- How a governed data supply chain using automated data orchestration can deliver a reliable engine for ML platforms
- And so much more!
Watch the on-demand webinar and determine if your company’s data is ready for Machine Learning today!
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