Hands-on Machine Learning with Python: Classification and Regression
Published by O'Reilly Media, Inc.
It's common knowledge that when undertaking a machine learning project, most of your time is spent preparing and tweaking your data so that your libraries and algorithms will work on it. But many don't know that you can take advantage of Python's optimized libraries to run your algorithms more quickly.
Join Matt Harrison for an overview of classification and regression tasks with Python using Jupyter and pandas—the same tools used throughout industry to prepare data for analysis. Matt walks you through common machine learning actions, including regression analysis and how to predict continuous variables, how to label data given a labeled training set, and model evaluation and tuning, providing you the valuable hands-on experience you need to get started using them in your own work.
What you’ll learn and how you can apply it
By the end of this live online course, you’ll understand:
- Basic machine learning tasks
- How to use Python and Jupyter to perform machine learning
And you'll be able to:
- Use pandas to load and preprocess data
- Run regressions, classifications, and other common machine learning tasks
This live event is for you because...
- You are a programmer and would like to see how to use Python for machine learning tasks for classification or regression.
- You are a data scientist with experience in SAS or R and would like an introduction to the Python ecosystem
Prerequisites
- Programming experience in any language
- Familiarity with the Python programming language (useful but not required)
Recommended follow-up:
- Take Hands-on Machine Learning with Python: Clustering, Dimension Reduction, and Time Series Analysis (live online training course with Matt Harrison)
- Take Getting Started with pandas (live online training course with Matt Harrison)
- Take Mastering pandas (live online training course with Matt Harrison)
- Watch Introduction to Pandas for Developers (video, 5h 34m)
- Read Learning the Pandas Library (book)
- Read chapters 2 and 5–10 in Python for Data Analysis (book)
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Introduction to Jupyter (20 minutes)
- Lecture: Explore the functionality you will need to be successful with Jupyter
- Q&A
Common data cleaning operations (20 minutes)
- Lecture: Preparing your data
- Q&A
Break (5 minutes)
Regression: Predicting a continuous value (40 minutes)
- Lecture: Leveraging the scikit-learn library to create regression models
- Hands-on exercise: Regression
- Q&A
Regression evaluation (30 minutes)
- Lecture: Visualization; using the Yellowbrick library to see how a regression model performs
- Hands-on exercise: Regression evaluation
- Q&A
Break (5 minutes)
Classification: Assigning a category (25 minutes)
- Lecture: Creating classification models with scikit-learn
- Hands-on exercise: Classification
- Q&A
Classification evaluation (25 minutes)
- Lecture: Visualizing classification models with Yellowbrick; using it to understand model performance
- Hands-on exercise: Classification evaluation
- Q&A
Wrap-up and Q&A (10 minutes)
Your Instructor
Matt Harrison
Matt Harrison has a CS degree from Stanford University. He is a best-selling author on Python and data subjects. His books—Effective Pandas, Illustrated Guide to Learning Python 3, Intermediate Python, Learning the Pandas Library, and Effective PyCharm—have all been best-selling books on Amazon. Matt's newest publications are Machine Learning Pocket Reference and Pandas Cookbook, Second Edition. He has taught courses at large companies (Netflix, NASA, Verizon, Adobe, HP, Exxon, and more), universities (Stanford, University of Utah, BYU), as well as small companies. Matt has been using Python since 2000 and has taught thousands through live training, both online and in person.