- Number of videos:
This tutorial session is an hands-on workshop on applied Machine Learning with the scikit-learn library. We will dive deeper into scikit-learn model evaluation and automated parameter tuning. We will also study how to scale text classification models for sentiment analysis or spam detection and use IPython.parallel to leverage multi-CPU or ad-hoc cloud clusters.
This tutorial will offer an introduction to the core concepts of machine learning, and how they can be easily applied in Python using Scikit-learn. We will use the scikit-learn API to introduce and explore the basic categories of machine learning problems, related topics such as feature selection and model validation, and the application of these tools to real-world data sets.
This tutorial will offer an introduction to the scikit-learn package and to the central concepts of Machine Learning. We will introduce the basic categories of learning problems, and explore practical examples based on real-world data, from handwriting analysis to facial recognition to automated classification of astronomical images.