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CPB102: Machine Learning with CloudML Training Course

Course

Online

£ 770 VAT inc.

Description

  • Type

    Course

  • 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

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Course programme

Module 0: Welcome [⅓ hr]

We assume that attendees 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

Additional information

8 hours (usually 1 day including breaks)

CPB102: Machine Learning with CloudML Training Course

£ 770 VAT inc.