Dive into machine learning using Python's scikit-learn library through this comprehensive 7-hour tutorial. Explore fundamental concepts, set up your environment with Jupyter Notebook, and work with the iconic iris dataset. Master techniques for training, comparing, and selecting optimal models using cross-validation. Learn to evaluate classifiers, encode categorical features, and apply machine learning to text data. Gain practical skills in data science by integrating pandas and seaborn with scikit-learn, equipping you to tackle real-world machine learning challenges effectively.
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
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