In this course, you will :
- introduces machine learning in an approachable manner and provides step-by-step guidance on how to get started with machine learning using Python, today's most popular programming language.
- begins with a definition of machine learning and the various ways machines learn, then moves on to how to collect, understand, and prepare data for machine learning.
- It also includes guided examples of how to complete each step in Python.
- Finally, he uses Python to build, evaluate, and interpret the results of a machine learning model.
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