CPB102: GOOGLE MACHINE LEARNING WITH CLOUD ML

Course Description

Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn machine learning and Tensorflow concepts and develop hands-on skills in developing, evaluating, and productionizing machine learning models.

This 1 day instructor led course builds upon CPB100 and CPB101 (which are prerequisites).

Audience

This class is intended for programmers and data scientists responsible for developing predictive analytics using machine learning. The typical audience member has experience analyzing and visualizing big data, implementing cloud-based big data solutions, and transforming/processing datasets.

Prerequisites

Knowledge of Google Cloud Platform Big Data & Machine Learning Fundamentals to the level of CPB100

Knowledge of BigQuery and Dataflow to the level of CPB101

Knowledge of Python and familiarity with the numpy package

Knowledge of undergraduate-level statistics to the level of Udacity ST101

Duration

1 day (8 hours)

Delivery Method

Instructor-led or virtual class

Language

Available in English and Spanish

Objectives

Understand what kinds of problems machine learning can address

Build a machine learning model using TensorFlow

Build scalable, deployable ML models using Cloud ML

Know the importance of preprocessing and combining features

Incorporate advanced ML concepts into their models

Employ ML APIs

Productionize trained ML model

Modules

Module 0: Welcome [⅓ hr]

We assume that attendees may attended CPB100.

Logistics

Introductions

Module 1: Getting started with Machine Learning [1½ hr]

What is machine learning (ML)?

Effective ML: concepts, types

Evaluating ML

ML datasets: generalization

Lab: Explore and create ML datasets

Module 2: Building ML models with Tensorflow [2 hr]

Getting started with TensorFlow

Lab: Using tf.learn

TensorFlow graphs and loops + lab

Lab: Using low-level TensorFlow + early stopping

Monitoring ML training

Lab: Charts and graphs of TensorFlow training

Module 3: Scaling ML models with CloudML [1 hr]

Why Cloud ML?

Packaging up a TensorFlow model

End-to-end training

Lab: Run a ML model locally and on cloud

Module 4: Feature Engineering [1.5 hr]

Creating good features

Transforming inputs

Synthetic features

Preprocessing with Cloud ML

Lab: Feature engineering

Module 5: ML architectures [optional]

Wide and deep

Image analysis

Embeddings and sequences

Recommendation systems

Summary