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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 3번째 강좌:
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일정에 따라 마감일을 재설정합니다.
초급 단계

Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML – whether you're an enterprise leader or a quant.

완료하는 데 약 16시간 필요
영어

배울 내용

  • Participate in the application of machine learning, helping select between and evaluate technical approaches

  • Interpret a predictive model for a manager or executive, explaining how it works and how well it predicts

  • Circumvent the most common technical pitfalls of machine learning

  • Screen a predictive model for bias against protected classes

귀하가 습득할 기술

Data ScienceArtificial Intelligence (AI)Machine LearningPredictive AnalyticsMachine Learning (ML) Algorithms
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계

Accessible to business-side learners yet also vital to techies. Engage in the commercial use of ML – whether you're an enterprise leader or a quant.

완료하는 데 약 16시간 필요
영어

제공자:

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강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 5시간 필요

MODULE 1 - The Foundational Underpinnings of Machine Learning

완료하는 데 5시간 필요
10개 동영상 (총 83분), 5 개의 읽기 자료, 12 개의 테스트
10개의 동영상
P-hacking: a treacherous pitfall8m
P-hacking: your predictive insights may be bogus8m
P-hacking: how to ensure sound discoveries9m
Avoiding overfitting: the train/test split8m
Why ice cream is linked to shark attacks7m
Causation is just a hobby -- prediction is your job6m
The art of induction: why generalizing from data is hard6m
Learning from mistakes: why negative cases matter5m
Intro to the hands-on assessment (Excel or Google Sheets)11m
5개의 읽기 자료
Why this course isn't hands-on & why it's essential for techies anyway19m
One-question survey1m
Complementary materials on p-hacking (optional)10m
Correlation does not imply causation (optional)10m
Data access for auditors (optional)10m
11개 연습문제
Course overview: Machine Learning Under the Hood4m
P-hacking: a treacherous pitfall2m
P-hacking: your predictive insights may be bogus2m
P-hacking: how to ensure sound discoveries4m
Avoiding overfitting: the train/test split4m
Why ice cream is linked to shark attacks4m
Causation is just a hobby -- prediction is your job4m
The art of induction: why generalizing from data is hard4m
Learning from mistakes: why negative cases matter2m
Intro to the hands-on assessment (Excel or Google Sheets)2m
Module 1 Review30m
2

2

완료하는 데 3시간 필요

MODULE 2 - Standard, Go-To Machine Learning Methods

완료하는 데 3시간 필요
12개 동영상 (총 107분), 1 개의 읽기 자료, 11 개의 테스트
12개의 동영상
Business rules rock and decision trees rule13m
Pruning decision trees to avoid overfitting12m
DEMO - Comparing decision tree models (optional)13m
Drawing the gains curve for a decision tree6m
Drawing the profit curve for a decision tree6m
Naïve Bayes11m
Linear models and perceptrons6m
Linear part II: a perceptron in two dimensions8m
Why probabilities drive better decisions than yes/no outputs7m
Logistic regression6m
DEMO - Training a logistic regression model (optional)4m
1개의 읽기 자료
A powerful, helpful visualization of how decision trees work (optional)10m
11개 연습문제
A refresher on decision trees2m
Business rules rock and decision trees rule4m
Pruning decision trees to avoid overfitting2m
Drawing the gains curve for a decision tree2m
Drawing the profit curve for a decision tree2m
Naïve Bayes2m
Linear models and perceptrons2m
Linear part II: a perceptron in two dimensions4m
Why probabilities drive better decisions than yes/no outputs4m
Logistic regression4m
Module 2 Review30m
3

3

완료하는 데 4시간 필요

MODULE 3 - Advanced Methods, Comparing Methods, & Modeling Software

완료하는 데 4시간 필요
16개 동영상 (총 154분), 2 개의 읽기 자료, 14 개의 테스트
16개의 동영상
Neural nets: decision boundaries & a comparison to logistic regression8m
DEMO - Training a neural network model (optional)2m
Deep learning9m
Ensemble models and the Netflix Prize8m
Supercharging prediction: ensembles & the generalization paradox12m
DEMO - Training an ensemble model (optional)3m
DEMO - Autotuning a machine learning model (optional)3m
Compare and contrast: summary of ML methods8m
Machine learning software: dos and don'ts for choosing a tool11m
Machine learning software: how tools vary and how to choose one11m
Model deployment: out of the software tool and into the field9m
Uplift modeling I: optimize for influence and persuade by the numbers12m
Uplift modeling II: modeling over treatment and control groups12m
Uplift modeling III: how it works – for banks and for Obama15m
Uplift modeling IV: improving churn modeling, plus other applications13m
2개의 읽기 자료
The generalization paradox of ensembles (optional) 10m
Complementary readings on uplift modeling (optional) 10m
14개 연습문제
How neural networks work5m
Neural nets: decision boundaries & a comparison to logistic regression2m
Deep learning2m
Ensemble models and the Netflix Prize2m
Supercharging prediction: ensembles & the generalization paradox4m
Compare and contrast: summary of ML methods4m
Machine learning software: dos and don'ts for choosing a tool2m
Machine learning software: how tools vary and how to choose one2m
Model deployment: out of the software tool and into the field2m
Uplift modeling I: optimize for influence and persuade by the numbers2m
Uplift modeling II: modeling over treatment and control groups2m
Uplift modeling III: how it works – for banks and for Obama4m
Uplift modeling IV: improving churn modeling, plus other applications4m
Module 3 Review30m
4

4

완료하는 데 4시간 필요

MODULE 4 – Pitfalls, Bias, and Conclusions

완료하는 데 4시간 필요
7개 동영상 (총 76분), 8 개의 읽기 자료, 8 개의 테스트
7개의 동영상
Machine bias II: visualizing why models are inequitable8m
Machine bias III: justice can't be colorblind13m
Explainable ML, model transparency, and the right to explanation15m
Conclusions on ML ethics: establishing standards as a form of social activism8m
Pitfalls: the seven deadly sins of machine learning11m
Conclusions and what's next – continuing your learning10m
8개의 읽기 자료
The original ProPublica article on machine bias10m
Interactive MIT Technology Review article on disparate false positive rates10m
Another interactive demo of machine bias (optional)10m
Complementary reading on machine bias (optional)10m
More on explainable ML and model transparency (optional)10m
Tallying the positive and negative impacts of AI (optional)10m
John Elder's top ten data science mistakes (optional)10m
Further resources and readings to continue your learning (optional)10m
8개 연습문제
Machine bias I: the conundrum of inequitable models4m
Machine bias II: visualizing why models are inequitable2m
Machine bias III: justice can't be colorblind4m
Explainable ML, model transparency, and the right to explanation4m
Conclusions on ML ethics: establishing standards as a form of social activism2m
Pitfalls: the seven deadly sins of machine learning4m
Conclusions and what's next - continuing your learning2m
Module 4 Review30m

검토

MACHINE LEARNING UNDER THE HOOD: THE TECHNICAL TIPS, TRICKS, AND PITFALLS의 최상위 리뷰

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Machine Learning for Everyone with Eric Siegel

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