Description:

Scikit-Learn is one of the major libraries for machine learning in Python. This series comprises four workshops designed to give you a map of Scikit-Learn’s different functionalities and place you on firm ground to start using it for your machine-learning projects. 

Part 1 - The Data Science Pipeline
This workshop introduces the fundamentals of machine learning (ML): How does ML differ from statistics? Which types of problems are best suited for ML? We will explore different real-world problems and learn to distinguish between supervised and unsupervised learning. You will become familiar with the main stages of the data science pipeline: data wrangling, cleaning and pre-processing, modeling, optimization and model validation, and post-processing and visualization. 

Prerequisites: Basic familiarity with Python is required. Familiarity with NumPy is highly recommended. No previous machine learning or statistics experience is necessary, but it will be helpful.