Course Summary
The Machine Learning, Say What? training course is designed to demonstrate the possibilities and challenges around machine learning.
The course begins by examining the product life cycle for machine learning systems, with some definitions around machine learning. Next, it describes the strengths and weaknesses of machine learning and an examination of what goes into making a machine learning product. The course concludes with discussions of good and bad machine learning in practice.
Purpose
Promote an in-depth understanding of the challenges and opportunities of machine learning.
Audience
Executives and leaders looking to better understand the potentials and challenges of machine learning.
Role
Skill Level
Style
Duration
1 Day
Related Technologies
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- Productivity Objectives:
- Explain what machine learning is
- List the 3 major types of machine learning
- Describe what goes into making a machine learning model
- Discuss some strengths and weaknesses of machine learning
- Understand the risks and challenges associated with machine learning
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about our training
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Real-World Content
Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
-
Expert Practitioners
Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
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Experiential Learning
More coding than lecture, coupled with architectural and design discussions.
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Fully Customized
One-size-fits-all doesn't apply to training teams. That's where we come in!
What You'll Learn
In the Machine Learning, Say What? training course, you'll learn:
- High level introduction
- What is Machine Learning?
- What are the three major types of Machine Learning?
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- How are companies using these technologies?
- How is this software being used?
- Strengths and weaknesses of Machine Learning
- How to evaluate if machine learning is a good fit for a particular problem
- What are the major sources of risk in machine learning projects?
- “Black box” versus interpretable models
- Data quality and bias
- What goes into making a Machine Learning product?
- Describe the parts of the Machine Learning life-cycle
- Define the objective
- Acquire & explore the data
- Execute modeling
- Interpret and communicate the results
- Implement, document, deploy, monitor and maintain the system
- Describe the parts of the Machine Learning life-cycle
- What went wrong? (Case Study)
- Amazon attempted to use machine learning as a tool for resume screening, but ultimately shuttered the program before deployment after determining it was biased against women
- Discuss the following:
- What are some few hypotheses about why their system ended up with this bias?
- What measurements could be used to quantify the model’s bias?
- What corrections could be made to correct this bias in the model?
- What are the possible classifications of this tasks as a good fit for machine learning?
- How to simulate the product life-cycle?
- Discuss what the machine learning product life-cycle
- What data is needed and how will it be retrieved?
- What metrics should be tracked to measure the success of this system?
- How to define “success” and “failure” with respect to these metrics
- What are the risks in a system like this?
- What are possible failsafes to design to mitigate these risks?
- What might the infrastructural requirements be?
- What systems generate the input on the live system?
- How will it be monitored? How will it alert if it’s no longer working?
- What controls does the algorithm need to be successful?
- (e.g. it probably needs to block a transaction… what else?)
- What needs to be done if the system starts failing?
- Is this a good fit for machine learning?
- Is machine learning the best way to do this?
- Is there something simpler that works just as well?
- Discuss what the machine learning product life-cycle
- Group discussion and recap
Real-world content
Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners
Industry experts that bring their battle scars into the classroom.
Experiential Learning
More coding than lecture, coupled with architectural and design discussions.
Fully Customized
One-size-fits-all doesn't apply to training teams. That's where we come in!
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