This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
Python Data Products for Predictive Analytics 특화 과정캘리포니아 샌디에고 대학교이 강좌에 대하여
최근 조회 3,128회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 9시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 중국어 (간체자), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 터키어, 영어, 스페인어
배울 내용
Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).
Evaluate the performance of regressors / classifiers using the above measures.
Understand the difference between training/testing performance, and generalizability.
Understand techniques to avoid overfitting and achieve good generalization performance.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 9시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 중국어 (간체자), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 터키어, 영어, 스페인어
제공자:
캘리포니아 샌디에고 대학교
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 3시간 필요
Week 1: Diagnostics for Data
For this first week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of diagnostics for the results of supervised learning.
완료하는 데 3시간 필요
6개 동영상 (총 49분), 4 개의 읽기 자료, 3 개의 테스트
6개의 동영상
Motivation Behind the MSE8m
Regression Diagnostics: MSE and R²6m
Over- and Under-Fitting6m
Classification Diagnostics: Accuracy and Error11m
Classification Diagnostics: Precision and Recall12m
4개의 읽기 자료
Syllabus10m
Setting Up Your System10m
(Optional) Additional Resources and Recommended Readings10m
Course Materials10m
3개 연습문제
Review: Regression Diagnostics30m
Review: Classification Diagnostics30m
Diagnostics for Data30m
완료하는 데 2시간 필요
Week 2: Codebases, Regularization, and Evaluating a Model
This week, we will learn how to create a simple bag of words for analysis. We will also cover regularization and why it matters when building a model. Lastly, we will evaluate a model with regularization, focusing on classifiers.
완료하는 데 2시간 필요
4개 동영상 (총 35분)
4개의 동영상
Model Complexity and Regularization10m
Adding a Regularizer to our Model, and Evaluating the Regularized Model8m
Evaluating Classifiers for Ranking4m
4개 연습문제
Review: Setting Up a Codebase30m
Review: Regularization5m
Review: Evaluating a Model5m
Codebases, Regularization, and Evaluating a Model45m
완료하는 데 2시간 필요
Week 3: Validation and Pipelines
This week, we will learn about validation and how to implement it in tandem with training and testing. We will also cover how to implement a regularization pipeline in Python and introduce a few guidelines for best practices.
완료하는 데 2시간 필요
4개 동영상 (총 24분)
4개의 동영상
“Theorems” About Training, Testing, and Validation8m
Implementing a Regularization Pipeline in Python5m
Guidelines on the Implementation of Predictive Pipelines5m
3개 연습문제
Review: Validation30m
Review: Predictive Pipelines30m
Predictive Pipelines20m
완료하는 데 2시간 필요
Final Project
In the final week of this course, you will continue building on the project from the first and second courses of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data. Evaluate your model, validate your analyses, and make sure you aren't overfitting the data.
완료하는 데 2시간 필요
2 개의 읽기 자료
2개의 읽기 자료
Project Description10m
Where to Find Datasets10m
Python Data Products for Predictive Analytics 특화 과정 정보
Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego.
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