Launching into Machine Learning

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

• Introduction
• Course introduction
• Get to Know Your Data: Improve Data through Exploratory Data Analysis
• Introduction
• Improve data quality
• Lab intro: Improve the quality of your data
• Lab Demo: Improve the quality of your data
• Improving Data Quality
• What is exploratory data analysis?
• How is EDA used in machine learning?
• Data analysis and visualization
• Lab intro: Explore the data using Python and BigQuery
• Exploratory Data Analysis Using Python and BigQuery
• Quiz: Get to know your data: Improve data through Exploratory Data Analysis
• Machine Learning in Practice
• Introduction
• Supervised learning
• Linear regression
• Lab intro: Introduction to linear regression
• Lab Demo: Intro to Linear Regression
• Introduction to Linear Regression
• Logistic regression
• Quiz: Machine Learning in Practice
• Training AutoML Models Using Vertex AI
• Introduction
• Machine learning vs. deep learning
• What is automated machine learning?
• AutoML regression model
• (Optional) Lab intro: Training an AutoML classification model (Structured data)
• (Optional) Lab Demo: Training an AutoML classification model (Structured data)
• Training an AutoML Classification Model - Structured Data
• Evaluate AutoML models
• Quiz: Training AutoML Models Using Vertex AI
• BigQuery Machine Learning: Develop ML Models Where Your Data Lives
• Introduction
• Training an ML model using BigQuery ML
• BigQuery Machine Learning supported models
• Lab intro: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI)
• Lab Demo: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI)
• Using BigQuery ML to Predict Penguin Weight
• BigQuery ML hyperparameter tuning
• (Optional) Lab intro: Using the BigQuery ML hyperparameter tuning to improve model performance
• Using the BigQuery ML Hyperparameter Tuning to Improve Model Performance
• How to build and deploy a recommendation system with BigQuery ML
• Quiz: BigQuery Machine Learning: Develop ML Models Where Your Data Lives
• Optimization
• Introduction
• Defining ML models
• Introducing the course dataset
• Introduction to loss functions
• Gradient descent
• Troubleshooting loss curves
• ML model pitfalls
• Lecture lab: Introducing the TensorFlow Playground
• Lecture lab: TensorFlow Playground - Advanced
• Lecture lab: Practicing with neural networks
• Performance metrics
• Confusion matrix
• Quiz: Optimization
• Generalization and Sampling
• Introduction
• Generalization and ML models
• When to stop model training
• Creating repeatable samples in BigQuery
• Demo: Splitting datasets in BigQuery
• Quiz: Generalization and Sampling
• Summary
• Summary
• Resource: All quiz questions
• Resource: All readings
• Resource: All slides