About this Course
최근 조회 1,879

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 40시간 필요

권장: 6 hours/week...

영어

자막: 영어

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 40시간 필요

권장: 6 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 1시간 필요

Course Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course....
2 videos (Total 9 min), 4 readings, 1 quiz
2개의 동영상
Meet Professor Brunner4m
4개의 읽기 자료
Syllabus10m
About the Discussion Forums10m
Updating Your Profile10m
Social Media10m
1개 연습문제
Orientation Quiz10m
완료하는 데 9시간 필요

Module 1: Introduction to Machine Learning

This module provides the basis for the rest of the course by introducing the basic concepts behind machine learning, and, specifically, how to perform machine learning by using Python and the scikit learn machine learning module. First, you will learn how machine learning and artificial intelligence are disrupting businesses. Next, you will learn about the basic types of machine learning and how to leverage these algorithms in a Python script. Third, you will learn how linear regression can be considered a machine learning problem with parameters that must be determined computationally by minimizing a cost function. Finally, you will learn about neighbor-based algorithms, including the k-nearest neighbor algorithm, which can be used for both classification and regression tasks....
4 videos (Total 47 min), 3 readings, 2 quizzes
4개의 동영상
Introduction to Machine Learning14m
Introduction to Linear Regression14m
Introduction to k-nn12m
3개의 읽기 자료
Module 1 Overview10m
Lesson 1-1 Readings10m
Lesson 1-2 Readings10m
1개 연습문제
Module 1 Graded Quiz20m
2
완료하는 데 9시간 필요

Module 2: Fundamental Algorithms

This module introduces several of the most important machine learning algorithms: logistic regression, decision trees, and support vector machine. Of these three algorithms, the first, logistic regression, is a classification algorithm (despite its name). The other two, however, can be used for either classification or regression tasks. Thus, this module will dive deeper into the concept of machine classification, where algorithms learn from existing, labeled data to classify new, unseen data into specific categories; and, the concept of machine regression, where algorithms learn a model from data to make predictions for new, unseen data. While these algorithms all differ in their mathematical underpinnings, they are often used for classifying numerical, text, and image data or performing regression in a variety of domains. This module will also review different techniques for quantifying the performance of a classification and regression algorithms and how to deal with imbalanced training data....
5 videos (Total 52 min), 4 readings, 2 quizzes
5개의 동영상
Introduction to Fundamental Algorithms3m
Introduction to Logistics Regression14m
Introduction to Decision Trees15m
Introduction to Support Vector Machine13m
4개의 읽기 자료
Module 2 Overview10m
Lesson 2-1 Readings10m
Lesson 2-3 Readings10m
Lesson 2-4 Readings10m
1개 연습문제
Module 2 Graded Quiz20m
3
완료하는 데 8시간 필요

Module 3: Practical Concepts in Machine Learning

This module introduces several important and practical concepts in machine learning. First, you will learn about the challenges inherent in applying data analytics (and machine learning in particular) to real world data sets. This also introduces several methodologies that you may encounter in the future that dictate how to approach, tackle, and deploy data analytic solutions. Next, you will learn about a powerful technique to combine the predictions from many weak learners to make a better prediction via a process known as ensemble learning. Specifically, this module will introduce two of the most popular ensemble learning techniques: bagging and boosting and demonstrate how to employ them in a Python data analytics script. Finally, the concept of a machine learning pipeline is introduced, which encapsulates the process of creating, deploying, and reusing machine learning models. ...
5 videos (Total 40 min), 3 readings, 2 quizzes
5개의 동영상
Introduction to Modeling Success6m
Introduction to Bagging11m
Introduction to Boosting9m
Introduction to ML Pipelines8m
3개의 읽기 자료
Module 3 Overview10m
Lesson 3-1 Readings10m
Lesson 3-2 Readings10m
1개 연습문제
Module 3 Graded Quiz20m
4
완료하는 데 9시간 필요

Module 4: Overfitting & Regularization

This module introduces the concept of regularization, problems it can cause in machine learning analyses, and techniques to overcome it. First, the basic concept of overfitting is presented along with ways to identify its occurrence. Next, the technique of cross-validation is introduced, which can mitigate the likelihood that overfitting can occur. Next, the use of cross-validation to identify the optimal parameters for a machine learning algorithm trained on a given data set is presented. Finally, the concept of regularization, where an additional penalty term is applied when determining the best machine learning model parameters, is introduced and demonstrated for different regression and classification algorithms....
5 videos (Total 48 min), 4 readings, 2 quizzes
5개의 동영상
Introduction to Overfitting4m
Introduction to Cross-Validation13m
Introduction to Model-Selection16m
Introduction to Regularization8m
4개의 읽기 자료
Module 4 Overview10m
Lesson 4-1 Readings10m
Lesson 4-2 Readings10m
Lesson 4-3 Readings10m
1개 연습문제
Module 4 Graded Quiz20m

강사

Avatar

Robert Brunner

Professor
Accountancy

Start working towards your Master's degree

이 강좌은(는) 일리노이대학교 어버너-섐페인캠퍼스의 100% 온라인 Master of Science in Accountancy (iMSA) 중 일부입니다. 전체 프로그램을 수료하면 귀하의 강좌가 학위 취득에 반영됩니다.

일리노이대학교 어버너-섐페인캠퍼스 정보

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. ...

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.