Machine Learning for Business
Understand the fundamentals of Machine Learning and how it's applied in the business world.Start Course for Free
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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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.
Machine learning and data use casesFree
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 pyramid50 xpTerminology clarification50 xpOrder data pyramid needs100 xpMatch tasks in data pyramid100 xpMachine learning principles50 xpModeling types50 xpFind supervised and unsupervised cases100 xpJob roles, tools and technologies50 xpJob role responsibilities50 xpMatch data projects with job roles100 xpTeam structure types100 xp
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 dilemma50 xpInference and prediction differences50 xpIdentify inference vs. prediction use cases100 xpInference (causal) models50 xpExperiments and causal models50 xpIdentify non actionable variables50 xpPrediction models (supervised learning)50 xpSupervised modeling principles50 xpIdentify classification and regression models100 xpPrediction models (unsupervised learning)50 xpUnsupervised modeling use cases50 xpClassification, regression or unsupervised models100 xp
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 requirements50 xpIdentify situation, opportunity and action100 xpIdentify successful experiments50 xpModel training50 xpModel training process50 xpTraining, validation and test100 xpModel performance measurement50 xpPoor performance examples50 xpIdentify performance metrics100 xpMachine learning risks50 xpFixing non performing models50 xpNon-actionable models50 xpIdentify actionable recommendations50 xp
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 mistakes50 xpIdentify machine learning mistakes50 xpData needs pyramid100 xpMatch ML mistakes by their types100 xpCommunication management50 xpBusiness communication focus50 xpMarket testing100 xpMachine learning in production50 xpProduction systems50 xpProduction systems ML use cases100 xpML in production launch100 xpWrap-up50 xp
In the following tracksData Scientist Professional with PythonData Scientist Professional with RData Skills for Business
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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