Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly “futuristic.” One of the central foundations of deep learning is linear regression; using probability theory to gain deeper insight into the “line of best fit.” This is the first step to building machines that, in effect, act like neurons in a neural network as they learn while they’re fed more information. In this course, you’ll start with the basics of building a linear regression module in Python, and progress into practical machine learning issues that will provide the foundations for an exploration of Deep Learning.
- Access 20 lectures & 2 hours of content 24/7
- Use a 1-D linear regression to prove Moore’s Law
- Learn how to create a machine learning model that can learn from multiple inputs
- Apply multi-dimensional linear regression to predict a patient’s systolic blood pressure given their age & weight
- Discuss generalization, overfitting, train-test splits, & other issues that may arise while performing data analysis
For more see: Deep Learning and AI Intro Bundle