About This Course
Machine learning is behind the biggest innovations in artificial intelligence — so much so that AI and machine learning have become nearly synonymous. In this course, we’ll focus on machine learning techniques for supervised and unsupervised learning problems, including deep learning. You’ll learn how to select and tune machine learning algorithms based on use cases. You'll also explore the pros and cons of different algorithms and how to open up “black box” models for interpretation. We’ll finish by looking at how models are deployed in production.
What You’ll Learn
- How to train and evaluate machine learning models for classification and regression
- The pros and cons of common machine learning algorithms
- Deep learning and how it differs from traditional machine learning
- Common techniques for explaining complex machine learning models
Get Hands-On Experience
- Use the scikit-learn library to train, tune and evaluate machine learning models
- Use TensorFlow/Keras to train and evaluate deep learning models
