Implementing Enterprise AI course – new batch – April 2017

 

The January batch of the Implementing AI course is completely sold out!

I am pleased to announce a new cohort for Implementing Enterprise AI course – starting April 24 2017. We are accepting places now. As usual, numbers are limited and we have an early bird discount

Implementing Enterprise AI is a unique and limited edition course that is focussed on AI Engineering / AI for the Enterprise.

Created in partnership with H2O.ai , the course uses Open Source technology to work with AI use cases. Successful participants will receive a certificate of completion and also validation of their project from H2O.ai.

 To sign up or learn more, email info@futuretext.com

The course covers

  • Design of Enterprise AI
  • Technology foundations of Enterprise AI systems
  • Specific AI use cases
  • Development of AI services
  • Deployment and Business models

 The course targets developers and Architects who want to transition their career to Enterprise AI. The course correlates the new AI ideas with familiar concepts like ERP, Data warehousing etc and helps to make the transition easier.

The implementation / development for the course is done using the H2O APIs for R, Python & Spark. 

 

Duration:
Starting April 2017 approximately six months (3 months for the content and up to three months for the Project)
Course includes a certificate of completion and also validation of the project from H2O.ai. (Projects will be created in a team)

 Course Logistics:

Offered Online  and Offline ( London and Berlin)

When:    April 2017
Duration: Approximately six months (including project)
Fees:      contact us

To sign up or learn more, email info@futuretext.com

 Outline

April – May 2017

 

  • Understanding the Enterprise AI layer
  • Introduction to Machine Learning
  • Unsupervised Learning
  • Supervised Learning
  • Generalized Linear Modeling
  • Gradient Boosting Machine
  • Ensembles
  • Random Forest
  • Programming foundations(see notes below)

 

June

  • Introduction to Deep Learning
  • Multilayer Perception
  • Auto encoders
  • Deep Convolutional Networks
  • Recurrent Neural Networks
  • Reinforcement learning
  • Programming foundations(see notes below)

 

July 2017

 

  • Natural language processing
  • Basics of Text Analytics
  • POS Tagging
  • Sentiment Analysis
  • Text Classification
  • Intelligent bots
  • Programming foundations(see notes below)

 

Aug – Oct 2017 – Projects and deployment

 

  • Deploying Enterprise AI
  • Acquiring Data and Training the Algorithm
  • Processing and hardware considerations
  • Business Models – High Performance Computing – Scaling and AI system
  • Costing an AI system
  • Creating a competitive advantage from AI
  • Industry Barriers for AI

 

Implementation of Enterprise AI use cases (in groups)

 

  • Healthcare
  • Insurance
  • Adtech
  • Fraud detection
  • Anomaly detection
  • Churn, classification
  • Customer analytics
  • Natural Language Processing, Bots and Virtual Assistants

 

Notes

  • The course covers Design of Enterprise AI, Technology foundations of Enterprise AI systems, Specific AI use cases, Development of AI services and Deployment and Business models
  • The implementation / development for the course is done using R, Python and Spark using the H2O APIs
  • For Deep learning, we also work with GPUs, tensoflow, Mxnet and Caffe
  • We focus on large scale problems
  • Notes on Programming foundations: We assume that you have significant Programming knowledge. However, we do not assume that you are familiar with Python, R or Spark.
  • The course provides you background in these languages over the first three months. You will then use this knowledge to work on the use cases in the Project phase Certification of completion is based on completion of quiz related to modules.
  • Project certification (validated by H2O.ai) is based on Projects working in groups
  • Note that the syllabus is subject to change

Project certification by h2o.ai

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