Data Annotation at Scale: Active and Semi-Supervised Learning in Python
This course is only available as a part of subscription plans.
Training duration : 4 hours
DIFFICULTY LEVEL: ADVANCED
Recap of Supervised Learning model and techniques
Understand Active Learning and explore human-in-the-loop to scale data annotation
Understand Semi-Supervised Learning to attach the data annotation problem
Putting Everything Together - A Complete Data Annotation Pipeline
Gokhan Ciflikli, PhD
INTERESTED IN MORE HANDS-ON TRAINING SESSIONS?
Module 1: Recap of Supervised Learning
- Brief recap of the supervised learning paradigm, train-test split procedure; cross-validation; in-sample vs. out-of-sample forecasts; accuracy vs. precision; the bias-variance trade-off
Module 2: Active Learning
- Using Active Learning to leverage the least confident predictions of an estimator
- Expedite its learning by querying their labels from a human annotator
- Explore how the human-in-the-loop can help scale up the data annotation process.
Module 3: Semi-Supervised Learning
- How Semi-Supervised Learning attacks the problem of data annotation from the opposite angle
- Explore the underpinnings of the so-called ML/AI-assisted data annotation
- How to leverage the most confident predictions of estimator to label data at scale
Module 4: Putting Everything Together
- A Complete Data Annotation Pipeline
- Walk-through of an interactive Jupyter notebook
- Demonstration of how two aforementioned frameworks can be combined to create bespoke data labeling jobs.
- Explore a multitude of scenarios by utilizing the individual components in various configurations
- Assess their pros and cons.
This course is for current and aspiring Data Scientists, Data Analysts and AI Product Managers
Knowledge of following tools and concepts is useful:
Familiarity with Python and Jupyter notebooks (R users should be able to follow the material)
Specifically for Python, prior working experience using numpy, pandas, scikit-learn, and modAL libraries
General grasp of supervised learning concepts
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