7. Modelling
Modelling in AI is essential for creating effective machine learning systems that can understand and predict outcomes based on data. It involves processes such as data collection, analysis, and training models, which can be either descriptive or predictive. Successful AI applications utilize various models and algorithms to handle real-world challenges efficiently.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Modelling is vital for training machines to understand data and make predictions.
- There are two major types of modelling: Descriptive (exploring data) and Predictive (forecasting outcomes).
- Effective modelling necessitates high-quality data, suitable algorithms, and thorough evaluation.
Key Concepts
- -- Modelling
- The process of creating mathematical or logical representations of real-world scenarios to help machines learn.
- -- Descriptive Modelling
- A type of modelling that focuses on understanding patterns and structures in past data.
- -- Predictive Modelling
- A type of modelling aiming at predicting future outcomes based on historical data.
- -- Supervised Learning
- A learning paradigm where the model is trained on labeled data.
- -- Unsupervised Learning
- A learning paradigm involving data without labeled outcomes, focusing on clustering or grouping.
- -- Algorithm
- A mathematical method used to train a model on data.
Additional Learning Materials
Supplementary resources to enhance your learning experience.