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CPB102: Machine Learning with CloudML Training Course
Course
Online
Description
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Type
Course
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Methodology
Online
This 8-hour instructor led course builds upon CPB100 and CPB101 (which are prerequisites). 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 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.
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
Invoke and customize ML APIs
Productionize trained ML model
About this course
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 some familiarity with data analysis libraries (numpy, Pandas, matplotlib, etc.)
Knowledge of undergraduate-level statistics to the level of Udacity ST101
Reviews
Course programme
We assume that attendees attended CPB100.
- Logistics
- Introductions
- 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
Additional information
CPB102: Machine Learning with CloudML Training Course
