Scikit-learn to "Learn Them All"

Explore the power of Scikit-learn for machine learning in this comprehensive EuroPython conference talk. Dive into the library's capabilities, from supervised and unsupervised learning techniques to scaling up for big data applications. Learn how to implement text classification using SVM and kernel methods, as well as partitional and model-based clustering algorithms. Gain insights into Scikit-learn's design philosophy, data representation, and model validation techniques. Compare Scikit-learn with other popular machine learning libraries in Python, and discover how to leverage its features for efficient and effective machine learning solutions. Suitable for intermediate-level Python developers with basic math skills and familiarity with NumPy and SciPy packages.

Intro
THREE QUESTIONS
WHAT IS MACHINE LEARNING ?
ML & DATA ANALYSIS
DATA DATA SCIENCE
MACHINE LEARNING & DATA ANALYSIS
THE ESSENCE OF MACHINE LEARNING
ML PYTHON POWERED
SCIKIT DESIGN PHILOSOPHY
DATA REPRESENTATION
IRIS DATASET
MODEL VALIDATION
CROSS VALIDATION
SCIKIT meets Natural Language Toolkit