CPB102: GOOGLE MACHINE LEARNING WITH CLOUD ML
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).
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.
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
1 day (8 hours)
Instructor-led or virtual class
Available in English and Spanish
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
Module 0: Welcome [⅓ hr]
We assume that attendees may attended CPB100.
Module 1: Getting started with Machine Learning [1½ hr]
What is machine learning (ML)?
Effective ML: concepts, types
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
Lab: Run a ML model locally and on cloud
Module 4: Feature Engineering [1.5 hr]
Creating good features
Preprocessing with Cloud ML
Lab: Feature engineering
Module 5: ML architectures [optional]
Wide and deep
Embeddings and sequences