Course
Skills
Serverless Machine Learning with Tensorflow on Google Cloud Platform
This course covers how to build, scale and operationalize machine learning models on Google Cloud Platform. You build ML models with TensorFlow, an open-source ML package and you can train and deploy them in a serverless way using Cloud ML Engine.
What you'll learn
This course covers how to build, scale and operationalize machine learning models on Google Cloud Platform. On GCP, you build ML models with TensorFlow, an open-source ML package and you can train and deploy them in a serverless way using Cloud ML Engine.
Table of contents
Welcome to Serverless Machine Learning on Google Cloud Platform
4mins
Getting Started with Machine Learning
113mins
- What is Machine Learning (ML)? 7m
- Types of ML 3m
- The ML Pipeline 2m
- Variants of ML model 7m
- Framing a ML problem 3m
- Playing with Machine Learning (ML) 8m
- Optimization 10m
- A Neural Network Playground 19m
- Combining Features 3m
- Feature Engineering 3m
- Image Models 5m
- Effective ML 2m
- What makes a good dataset? 5m
- Error Metrics 4m
- Accuracy 3m
- Precision and Recall 6m
- Creating Machine Learning Datasets 3m
- Splitting Dataset 7m
- Python Notebooks 2m
- Getting Started With GCP And Qwiklabs 4m
- Create ML Datasets Lab Overview 3m
- Serverless Machine Learning - Lab 1 : Create ML datasets v1.3 0m
- Create ML Datasets Lab Review 3m
Building ML models with Tensorflow
65mins
- Overview 1m
- What is TensorFlow? 5m
- Core TensorFlow 6m
- Getting Started with TensorFlow Lab Overview 0m
- Serverless Machine Learning - Lab 2 : Getting Started with TensorFlow v1.3 0m
- TensorFlow Lab Review 11m
- Estimator API 9m
- Machine Learning with tf.estimator 0m
- Serverless Machine Learning - Lab 3 : Machine Learning using tf.estimator v1.3 0m
- Estimator Lab Review 7m
- Building Effective ML 7m
- Lab Intro:Refactoring to add batching and feature creation 1m
- Serverless Machine Learning - Lab 4 : Refactoring to add batching and feature-creation v1.3 0m
- Refactoring Lab Review 5m
- Train and Evaluate 4m
- Monitoring 1m
- Lab Intro:Distributed Training and Monitoring 2m
- Serverless Machine Learning - Lab 5 : Distributed training and monitoring v1.3 0m
- Lab Review:Distributed Training and Monitoring 7m
Scaling ML models with Cloud ML Engine
28mins
Feature Engineering
100mins
- Overview 2m
- Good Features 7m
- Casuality 9m
- Numeric 6m
- Enough Examples 16m
- Raw data to features 2m
- Categorical features 8m
- Feature crosses 4m
- Bucketizing 3m
- Wide and Deep 6m
- Where to do feature engineering 3m
- Feature Engineering Lab Overview 3m
- Serverless Machine Learning - Lab 7 : Feature Engineering v1.3 0m
- Feature Engineering Lab Review 10m
- Hyperparameter Tuning + Demo 16m
- ML Abstraction Levels 4m
- Summary 2m
