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Machine Learning for Business

Understand the fundamentals of Machine Learning and how it's applied in the business world.

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2 Hours15 Videos48 Exercises20,310 Learners3200 XPData Scientist Professional with Python TrackData Scientist Professional with R TrackData Skills for Business Track

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Course Description

This course will introduce the key elements of machine learning to the business leaders. We will focus on the key insights and base practices how to structure business questions as modeling projects with the machine learning teams. You will understand the different types of models, what kind of business questions they help answer, or what kind of opportunities they can uncover, also learn to identify situations where machine learning should NOT be applied, which is equally important. You will understand the difference between inference and prediction, predicting probability and amounts, and how using unsupervised learning can help build meaningful customer segmentation strategy.
  1. 1

    Machine learning and data use cases

    Free

    Machine learning is used in many different industries and fields. It can fundamentally improve the business if applied correctly. This chapter outlines machine learning use cases, job roles and how they fit in the data needs pyramid.

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    Machine learning and data pyramid
    50 xp
    Terminology clarification
    50 xp
    Order data pyramid needs
    100 xp
    Match tasks in data pyramid
    100 xp
    Machine learning principles
    50 xp
    Modeling types
    50 xp
    Find supervised and unsupervised cases
    100 xp
    Job roles, tools and technologies
    50 xp
    Job role responsibilities
    50 xp
    Match data projects with job roles
    100 xp
    Team structure types
    100 xp
  2. 2

    Machine learning types

    This chapter overviews different machine learning types. We will look into differences between causal and prediction models, explore supervised and unsupervised learning, and finally understand the sub-types of supervised learning: classification and regression.

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    Prediction vs. inference dilemma
    50 xp
    Inference and prediction differences
    50 xp
    Identify inference vs. prediction use cases
    100 xp
    Inference (causal) models
    50 xp
    Experiments and causal models
    50 xp
    Identify non actionable variables
    50 xp
    Prediction models (supervised learning)
    50 xp
    Supervised modeling principles
    50 xp
    Identify classification and regression models
    100 xp
    Prediction models (unsupervised learning)
    50 xp
    Unsupervised modeling use cases
    50 xp
    Classification, regression or unsupervised models
    100 xp
  3. 3

    Business requirements and model design

    This chapter reviews key steps in scoping out business requirements, identifying and sizing machine learning opportunities, assessing the model performance, and identifying any performance risks in the process.

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    Business requirements
    50 xp
    Identify situation, opportunity and action
    100 xp
    Identify successful experiments
    50 xp
    Model training
    50 xp
    Model training process
    50 xp
    Training, validation and test
    100 xp
    Model performance measurement
    50 xp
    Poor performance examples
    50 xp
    Identify performance metrics
    100 xp
    Machine learning risks
    50 xp
    Fixing non performing models
    50 xp
    Non-actionable models
    50 xp
    Identify actionable recommendations
    50 xp
  4. 4

    Managing machine learning projects

    This chapter will look into the best and worst practices of managing machine learning projects. We will identify most common machine learning mistakes, learn how to manage communication between the business and ML teams and finally address the challenges when deploying machine learning models to production.

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    Machine learning mistakes
    50 xp
    Identify machine learning mistakes
    50 xp
    Data needs pyramid
    100 xp
    Match ML mistakes by their types
    100 xp
    Communication management
    50 xp
    Business communication focus
    50 xp
    Market testing
    100 xp
    Machine learning in production
    50 xp
    Production systems
    50 xp
    Production systems ML use cases
    100 xp
    ML in production launch
    100 xp
    Wrap-up
    50 xp

In the following tracks

Data Scientist Professional with PythonData Scientist Professional with RData Skills for Business

Collaborators

Hadrien Lacroix
Sara Billen
Karolis Urbonas Headshot

Karolis Urbonas

Head of Machine Learning and Science

Karolis is currently leading a Machine Learning and Science team at Amazon Web Services. He's a data science enthusiast obsessed with machine learning, analytics, neural networks, data cleaning, feature engineering, and every engineering puzzle he can get his hands on.
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