Machine Learning with Python: Foundations
1h 44mIntermediate2021-10-06
Authors
Frederick Nwanganga
Information Technology Professional and Teacher
Course details
You’ve probably heard about machine learning before, but have you ever wondered what that term really means? How does a machine learn? Have you thought about building a machine learning model, but didn’t know where to start? In this course, Frederick Nwanganga introduces machine learning in an approachable way and provides step-by-step guidance on how to get started with machine learning via the most in-demand language in use today, Python. Frederick starts with exactly what it means for machines to learn and the different ways they learn, then gets into how to collect, understand, and prepare data for machine learning. He also provides guided examples of how to accomplish each step using Python. Finally, he brings it all together to build, evaluate, and interpret the results of a machine learning model in Python.
Skills covered
Introduction toMachine LearningPythonArtificial Intelligence (AI)Programming LanguagesOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - Machine learning in our world
- 02 - What you should know
- 03 - The tools you need
- 04 - Using the exercise files
1. Machine Learning
- 05 - What is machine learning
- 06 - What is not machine learning
- 07 - What is unsupervised learning
- 08 - What is supervised learning
- 09 - What is reinforcement learning
- 10 - What are the steps to machine learning
2. Collecting Data for Machine Learning
- 11 - Things to consider when collecting data
- 12 - How to import data in Python
3. Understanding Data for Machine Learning
- 13 - Describe your data
- 14 - How to summarize data in Python
- 15 - Visualize your data
- 16 - How to visualize data in Python
4. Preparing Data for Machine Learning
- 17 - Common data quality issues
- 18 - How to resolve missing data in Python
- 19 - Normalizing your data
- 20 - How to normalize data in Python
- 21 - Sampling your data
- 22 - How to sample data in Python
- 23 - Reducing the dimensionality of your data
5. Types of Machine Learning Models
- 24 - Classification vs. regression problems
- 25 - How to build a machine learning model in Python
Conclusion
- 26 - Next steps with applied machine learning