Launching into Machine Learning

Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

Course Objectives:
Identify why deep learning is currently popular
Optimize and evaluate models using loss functions and performance metrics
Mitigate common problems that arise in machine learning
Create repeatable and scalable training, evaluation, and test datasets

What skills will you gain?

Machine Learning

Human Learning

Bigquery

Tensorflow

Extract, Transform, Load

Google Cloud Platform

Cloud Computing

Modeling

Mathematical Optimization

Regression