Machine Learning with Python: Foundations

Learn the basics of machine learning and how you can create a machine learning model with Python.

Introduction

• Machine learning in our world
• What you should know
• The tools you need
• Using the exercise files

1. Machine Learning

• What is machine learning?
• What is not machine learning?
• What is unsupervised learning?
• What is supervised learning?
• What is reinforcement learning?
• What are the steps to machine learning?

2. Collecting Data for Machine Learning

• Things to consider when collecting data
• How to import data in Python

3. Understanding Data for Machine Learning

• Describe your data
• How to summarize data in Python
• Visualize your data
• How to visualize data in Python

4. Preparing Data for Machine Learning

• Common data quality issues
• How to resolve missing data in Python
• Normalizing your data
• How to normalize data in Python
• Sampling your data
• How to sample data in Python
• Reducing the dimensionality of your data

5. Types of Machine Learning Models

• Classification vs. regression problems
• How to build a machine learning model in Python
• Common machine learning algorithms

Conclusion

• Next steps with applied machine learning