Hands-on Machine Learning with Python: Classification and Regression
It's common knowledge that when undertaking a machine learning project, most of your time is spent preparing and tweaking your data so that the 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 training course 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
- Programming experience in any language
- Familiarity with the Python programming language (useful but not required)
Materials or Downloads Needed in Advance:
- A machine with Anaconda and Jupyter installed and set up. (Please try them out to get comfortable before the course.)
Learning the Pandas Library (book)
About your instructor
Matt runs MetaSnake, a Python and Data Science training and consulting company. He has over 15 years of experience using Python across a breadth of domains: Data Science, BI, Storage, Testing and Automation, Open Source Stack Management, and Search.
The timeframes are only estimates and may vary according to how the class is progressing
Introduction to Jupyter - 20 min
Common Data Cleaning Operations - 20 min
Regression - Predicting a continuous value - 40 min
Break - 10 min
Regression Evaluation - 30 min
Classification - Assigning a category - 30 min
Classification Evaluation - 30 min