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Machine Learning for Business

Beginner

4.3+

Updated 12/2024

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

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TheoryMachine Learning2 hours15 videos48 exercises3,200 XP34,891Statement of Accomplishment

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Course Description

Learn the Basics of Machine Learning

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.

Dive into the Model Specifics

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.

Prerequisites

There are no prerequisites for this course

1

Machine learning and data use cases

Machine learning and data pyramid

Terminology clarification

Order data pyramid needs

Match tasks in data pyramid

Machine learning principles

Modeling types

Find supervised and unsupervised cases

Job roles, tools and technologies

Job role responsibilities

Match data projects with job roles

Team structure types

2

Machine learning types

Prediction vs. inference dilemma

Inference and prediction differences

Identify inference vs. prediction use cases

Inference (causal) models

Experiments and causal models

Identify non actionable variables

Prediction models (supervised learning)

Supervised modeling principles

Identify classification and regression models

Prediction models (unsupervised learning)

Unsupervised modeling use cases

Classification, regression or unsupervised models

3

Business requirements and model design

Business requirements

Identify situation, opportunity and action

Identify successful experiments

Model training

Model training process

Training, validation and test

Model performance measurement

Poor performance examples

Identify performance metrics

Machine learning risks

Fixing non performing models

Non-actionable models

Identify actionable recommendations

4

Managing machine learning projects

Machine learning mistakes

Identify machine learning mistakes

Data needs pyramid

Match ML mistakes by their types

Communication management

Business communication focus

Market testing

Machine learning in production

Production systems

Production systems ML use cases

ML in production launch

Machine Learning for Business

Course
Complete

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*4.3

from 25 reviews

64%

24%

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8%

4%

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Most recent
Top reviews

Sue D.

5 months

Fabulous course and awesome instructor!

Pedro D.

11 months

Very useful

Maja L.

about 1 year

Really liked the course!

David C.

about 1 year

Very interesting subject! Loved it!

Muhammad A.

over 1 year

This is a very good course which offers a wide analytical perspective from a busniess point of view. This is a good step, an amalgamation of the (desired) field domain knowledge courses and coding excercises makes a perfect match.

"Fabulous course and awesome instructor!"

Sue D.

"Very useful"

Pedro D.

"Really liked the course!"

Maja L.

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