Introduction to scikit-learn, Explained Simply (+ Live Python Code)

What is scikit-learn and why does almost every machine learning project in Python use it? In this first video of Machine Learning Explained Simply, we build it up from zero with simple pictures and a live coding demo. We map scikit-learn to the full ML workflow, learn its four design principles (consistency, inspection, composition, sensible defaults), and meet the three object types: estimators, transformers and predictors. Then we open a notebook and use fit, transform, predict and score on the iris dataset, check accuracy with the metrics module, and get an honest number with five fold cross validation. We finish with the inspection API, tips for reading the docs, and the most common beginner mistake. Chapters: 0:00 Intro 0:27 sklearn and the ML workflow 1:00 API design principles 1:42 Estimators, transformers, predictors 2:28 The model API 3:06 Live code: load and split data 4:55 Live code: scale, train, predict 6:44 Model evaluation API 7:19 Model selection API 7:51 Live code: evaluate and cross validate 9:11 Model inspection API 9:42 Tips for the docs 10:13 Common mistake 10:41 Quick check 11:11 Recap and outro Previous video: Machine Learning vs Deep Learning, Explained Simply Next video: Loading Data in scikit-learn If this helped, like, follow, and tell me in the comments what topic you want next. Stay curious! #machinelearning #scikitlearn #python