Learn how to use Python's scikit-learn library to perform effective machine learning. Understand the basics of machine learning, set up Python for machine learning, and work with datasets like the iris dataset. Train machine learning models, compare them, and select the best one using cross-validation. Explore data science tools like pandas, seaborn, and scikit-learn. Learn to evaluate classifiers, encode categorical features, and work with text data in scikit-learn. Ideal for individuals interested in machine learning and data science using Python.
Overview
Syllabus
What is machine learning, and how does it work?.
Setting up Python for machine learning: scikit-learn and Jupyter Notebook.
Getting started in scikit-learn with the famous iris dataset.
Training a machine learning model with scikit-learn.
Comparing machine learning models in scikit-learn.
Data science in Python: pandas, seaborn, scikit-learn.
Selecting the best model in scikit-learn using cross-validation.
How to find the best model parameters in scikit-learn.
How to evaluate a classifier in scikit-learn.
How do I encode categorical features using scikit-learn?.
Machine Learning with Text in scikit-learn (PyCon 2016).
Taught by
Data School
