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.

Machine learning and data pyramid

Terminology clarification

Data needs pyramid

Order data pyramid needs

Options

Machine Learning in production

Prototyping & Testing ML

Analysis

Match tasks in data pyramid

Machine learning principles

Modeling types

Machine Learning use cases

Supervised machine learning

Unsupervised machine learning

Find supervised and unsupervised cases

Job roles, tools and technologies

Job role responsibilities

Analytics roadmap items

Data Analyst

Data Scientist

Machine Learning Engineer

Match data projects with job roles

Data organization structures

Centralized

Decentralized

Hybrid

Team structure types

Machine learning and data use cases

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. 

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

Machine learning types

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.

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

Business requirements and model design

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.

Machine learning mistakes

Identify machine learning mistakes

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

Wrap-up

Managing machine learning projects

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.

<h2> Learn the Basics of Machine Learning </h2>
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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. 
<br><br>
<h2> Dive into the Model Specifics </h2>
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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.

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

Machine Learning for Business

Datenwissenschaftler/in in Python

Datenwissenschaftler/in in R

Create an ad-hoc analysis comparing year over year performance of different business units.

Prototype a machine learning model on a laptop and present results to the business.

Design an experiment and run A/B test with the fraud prevention team in a bank.

Re-write prototype machine learning model into production code and deploy into company's customer facing website.

Design a customer risk scoring algorithm from scratch and deploy it on a banking app in real time.

Match data projects with job roles

Machine Learning for Business

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