Content

Module 1: How Google Does Machine Learning

• Develop a data strategy around machine learning.
• Examine use cases that are then reimagined through an ML lens.
• Recognize biases that ML can amplify.
• Leverage Google Cloud Platform tools and environment to do ML.
• Learn from Google’s experience to avoid common pitfalls.
• Carry out data science tasks in online collaborative notebooks.
• Invoke pre-trained ML models from Cloud Datalab.

Module 2: Launching into Machine Learning

• 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.

Module 3:Intro to TensorFlow

• Create machine learning models in TensorFlow.
• Use the TensorFlow libraries to solve numerical problems.
• Troubleshoot and debug common TensorFlow code pitfalls.
• Use tf_estimator to create, train, and evaluate an ML model.
• Train, deploy, and productionalize ML models at scale with Cloud ML Engine.

Module 4: Feature Engineering

• Turn raw data into feature vectors.
• Preprocess and create new feature pipelines with Cloud Dataflow.
• Create and implement feature crosses and assess their impact.
• Write TensorFlow Transform code for feature engineering.

Module 5: The Art and Science of ML

• Optimize model performance with hyperparameter tuning.
• Experiment with neural networks and fine-tune performance.
• Enhance ML model features with embedding layers.
• Create reusable custom model code with the Custom Estimator.