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.
What you'll learn
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.
Table of contents
- Introduction 1m
- Improve data quality 13m
- Pluralsight: Getting Started with GCP and Qwiklabs 4m
- Lab intro: Improve the quality of your data 1m
- Lab Demo: Improve the quality of your data 23m
- What is exploratory data analysis? 5m
- How is EDA used in machine learning? 4m
- Data analysis and visualization 4m
- Lab intro: Explore the data using Python and BigQuery 1m
- Resources: Get to Know Your Data: Improve Data through Exploratory Data Analysis 0m
- Introduction 1m
- Improve data quality 13m
- Lab intro: Improve the quality of your data 1m
- Lab Demo: Improve the quality of your data 23m
- Lab: Improving Data Quality 0m
- What is exploratory data analysis? 5m
- How is EDA used in machine learning? 4m
- Data analysis and visualization 4m
- Lab intro: Explore the data using Python and BigQuery 1m
- Lab: Exploratory Data Analysis Using Python and BigQuery 0m
- Resources: Get to Know Your Data: Improve Data through Exploratory Data Analysis 0m
- Introduction 1m
- Training an ML model using BigQuery ML 6m
- BigQuery Machine Learning supported models 2m
- Lab intro: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI) 0m
- Lab Demo: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI) 12m
- BigQuery ML hyperparameter tuning 3m
- How to build and deploy a recommendation system with BigQuery ML 6m
- Resources: BigQuery Machine Learning: Develop ML Models Where Your Data Lives 0m
- Introduction 1m
- Training an ML model using BigQuery ML 6m
- BigQuery Machine Learning supported models 2m
- Lab intro: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI) 0m
- Lab Demo: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI) 12m
- Lab: Using BigQuery ML to Predict Penguin Weight 0m
- BigQuery ML hyperparameter tuning 3m
- How to build and deploy a recommendation system with BigQuery ML 6m
- Resources: BigQuery Machine Learning: Develop ML Models Where Your Data Lives 0m
- Introduction 1m
- Defining ML models 4m
- Introducing the course dataset 7m
- Introduction to loss functions 7m
- Troubleshooting loss curves 3m
- ML model pitfalls 6m
- Lecture lab: Introducing the TensorFlow Playground 6m
- Lecture lab: TensorFlow Playground - Advanced 3m
- Lecture lab: Practicing with neural networks 7m
- Performance metrics 4m
- Confusion matrix 6m
- Resources: Optimization 0m
- Introduction 1m
- Defining ML models 4m
- Introducing the course dataset 7m
- Introduction to loss functions 7m
- Gradient descent 5m
- Troubleshooting loss curves 3m
- ML model pitfalls 6m
- Lecture lab: Introducing the TensorFlow Playground 6m
- Lecture lab: TensorFlow Playground - Advanced 3m
- Lecture lab: Practicing with neural networks 7m
- Performance metrics 4m
- Confusion matrix 6m
- Resources: Optimization 0m
