Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. It is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions. The term “machine learning” is an umbrella term and can be used to describe one of the following topics:
- Classification: Identifying categories an object belongs to.
- Regression: Predict values associated with objects.
- Clustering: Automatically group similar objects into sets.
- Dimension reduction: Make large-scale problems more manageable.
- Model selection: Find and calibrate the best models.
- Preprocessing: Normalizing of raw data preparing it for learning algorithms.
In this course we will focus on the application of tools to perform various aspects of machine learning, using the Scikit-learn toolkit. We will touch base on how the various algorithms work.
The course requires familiarity with the Numpy, Scipy and Matplotlib libraries, introduced here.
Duration: 4-6 hours
- Scikit-learn is an open source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries Numpy and Scipy.