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Machine Learning for Business

基础技能水平

更新时间 2024年11月

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

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TheoryMachine Learning2 小时15 视频48 练习3,200 经验值45,519成就声明

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课程描述

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.

先决条件

本课程无先修要求

1

Machine learning and data use cases

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

50 经验值

Terminology clarification

50 经验值

Order data pyramid needs

100 经验值

Match tasks in data pyramid

100 经验值

Machine learning principles

50 经验值

Modeling types

50 经验值

Find supervised and unsupervised cases

100 经验值

Job roles, tools and technologies

50 经验值

Job role responsibilities

50 经验值

Match data projects with job roles

100 经验值

Team structure types

100 经验值

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.

Prediction vs. inference dilemma

50 经验值

Inference and prediction differences

50 经验值

Identify inference vs. prediction use cases

100 经验值

Inference (causal) models

50 经验值

Experiments and causal models

50 经验值

Identify non actionable variables

50 经验值

Prediction models (supervised learning)

50 经验值

Supervised modeling principles

50 经验值

Identify classification and regression models

100 经验值

Prediction models (unsupervised learning)

50 经验值

Unsupervised modeling use cases

50 经验值

Classification, regression or unsupervised models

100 经验值

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.

Business requirements

50 经验值

Identify situation, opportunity and action

100 经验值

Identify successful experiments

50 经验值

Model training

50 经验值

Model training process

50 经验值

Training, validation and test

100 经验值

Model performance measurement

50 经验值

Poor performance examples

50 经验值

Identify performance metrics

100 经验值

Machine learning risks

50 经验值

Fixing non performing models

50 经验值

Non-actionable models

50 经验值

Identify actionable recommendations

50 经验值

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.

Machine learning mistakes

50 经验值

Identify machine learning mistakes

50 经验值

Data needs pyramid

100 经验值

Match ML mistakes by their types

100 经验值

Communication management

50 经验值

Business communication focus

50 经验值

Market testing

100 经验值

Machine learning in production

50 经验值

Production systems

50 经验值

Production systems ML use cases

100 经验值

ML in production launch

100 经验值

50 经验值

Machine Learning for Business

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