Content

The course includes presentations, demonstrations, and hands-on labs.

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 AI Platform.

Module 2: Launching into Machine Learning

• Describe how to improve data quality.
• Perform exploratory data analysis.
• Build and train supervised learning models.
• 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:Introduction to TensorFlow 2.x

• Create TensorFlow 2.x and Keras machine learning models.
• Describe Tensorflow 2.x key components.
• Use the tf.data library to manipulate data and large datasets.
• Use the Keras Sequential and Functional APIs for simple and advanced model creation.
• Train, deploy, and productionalize ML models at scale with Cloud AI Platform.

Module 4: Feature Engineering

• Compare the key required aspects of a good feature.
• Combine and create new feature combinations through feature crosses.
• Perform feature engineering using BQML, Keras, and TensorFlow 2.x.
• Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep.
• Understand and apply how TensorFlow transforms features.

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.