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Machine learning for the enterprise

Supercharge your company with data

Matt Kirk

You’ll take a fascinating deep dive into the power and applications of machine learning in the enterprise. Beginning with the fundamentals of machine learning, how it works, and how enterprises are taking advantage of the benefits of working with machine learning applications, you’ll get a thorough introduction to three fascinating, business-ready use cases where machine learning leads to greater actionable insights, reduced costs, and increased revenue and profitability. You’ll be able to apply what you’ve learned with a tour of IBM Watson machine learning solutions, and you’ll walk away with concrete examples of how ML in action can benefit your large organization

What you'll learn-and how you can apply it

By the end of this live, hands-on, online course, you’ll understand:

  • Just enough machine learning to get started
  • How to go about deploying machine learning into the real world
  • The benefits of machine learning as it relates to the enterprise
  • How to use ML for good not harm
  • The tacit knowledge of ML not taught at universities

And you’ll be able to:

  • Visualize data with IBM watson
  • Predict Churn with IBM AutoAI
  • Bring machine learning to your enterprise
  • Understand how to approach machine learning problems
  • Get a basic introduction of what machine learning is and how to approach these problems

This training course is for you because...

  • You're a business decision maker looking to place bets on machine learning.
  • You're a technical leader within an organization looking for the right machine learning framework.
  • You're a CTO, CIO, or other IT executive looking to understand machine learning’s impact on the enterprise.
  • You're a data scientist, machine learning engineer, or AI developer.

Prerequisites

  • Basic understanding of machine learning helpful, but not required.

Recommended preparation:

Recommended follow-up:

About your instructor

  • Matt Kirk is a data architect, software engineer, and entrepreneur based out of Seattle, WA.

    For years, he struggled to piece together his quantitative finance background with his passion for building software.

    Then he discovered his affinity for solving problems with data.

    Now, he helps multi-million dollar companies with their data projects. From diamond recommendation engines to marketing automation tools, he loves educating engineering teams about methods to start their big data projects.

    To learn more about how you can get started with your big data project (beyond taking this class), check out matthewkirk.com for tips.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Introduction (10 minutes)

  • Poll: What is your motivation for using ML at your company now?
  • Presentation:
    • Why machine learning for the enterprise?
    • Why aren’t you using ML already?
    • About Matt
    • About IBM

What is machine learning and how does it apply to businesses? (50 minutes)

  • Presentation:
  • What is the goal of machine learning?
  • Deductive reasoning
  • Inductive reasoning
  • What are the applications of machine learning to enterprises?
    • Social Media
    • Healthcare
    • Retail
    • Finance
    • Marketing
    • Security
  • 3 classes of machine learning + Deep Learning
  • Quiz
  • Discussion and Q&A
  • Break (5 minutes)

Visualizing data with Watson (60 minutes)

  • Presentation:
  • What is the purpose of visualizing data in the enterprise?
  • Types of visualizations and a basic introduction to seeing with data.
  • Strategies with visualization
  • Tactics with visualization
  • Quiz
  • Discussion
  • Demonstration of refining data with IBM watson and setup of the exercise
  • Exercise: visualize data with IBM watson
  • Break (5 minutes)

Predicting churn with IBM Watson (55 minutes)

  • Presentation:
  • How to apply an ML model to a real problem
  • Methodologies
  • TACT:
    • Targeting: how to steer the ship and focus on what’s wildly important
    • Arranging data: going into depth about how data is pipelined
    • Composition of models: how to go about building the best model.
    • Transmission: how to deploy, evaluate, and present results
  • Quiz
  • Discussion and Q&A
  • Demonstration of predicting churn with IBM Watson
  • Exercise: Using IBM Watson to predict customer churn

Wrapup and conclusion (5 minutes)

  • Presentation:
  • What machine learning is and is not.
  • Visualizing data: what did we learn?
  • Predicting churn: what did we learn?
    • Discussion: What was your number? And how are you going to bring this back to your job?