Who this course is for?

This class is primarily intended for the following participants:

• Aspiring machine learning data scientists and engineers
• Machine learning scientists, data scientists, and data analysts
• Data engineers

 

Prerequisite

To get the most out of this course, participants should have:

• Some familiarity with basic machine learning concepts
• Basic proficiency with a scripting language: Python preferred

 

Course Outline

• Module 1: Introduction to Course
  • Summarize course scope
  • Get started with Google Cloud and Qwiklabs
• Module 2: Improve Data Quality and Exploratory Data Analysis
  • Understand best practices for improving data quality
  • Perform exploratory data analysis
• Module 3: Practical Machine Learning
  • Differentiate between the major categories of ML problems
  • Place major ML methods in the context of their historical development
  • Build and train supervised learning models
• Module 4: Optimization
  • Quantify model performance using loss functions
  • Use loss functions as the basis for an algorithm called gradient descent
  • Optimize gradient descent to be as efficient as possible
  • Use performance metrics to make business decisions
• Module 5: Generalization and Sampling
  • Assess whether your model is overfitting
  • Gauge when to stop model training
  • Create repeatable training, evaluation, and test datasets
  • Establish performance benchmarks

• Module 6: Course Summary
  • Summarize Course