このコースについて
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100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

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英語

字幕:英語

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

英語

字幕:英語

シラバス - 本コースの学習内容

1
1時間で修了

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.

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2件のビデオ (合計9分), 4 readings, 1 quiz
4件の学習用教材
Syllabus10 分
About the Discussion Forums10 分
Updating Your Profile10 分
Social Media10 分
1の練習問題
Orientation Quiz10 分
9時間で修了

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.

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4件のビデオ (合計47分), 3 readings, 2 quizzes
4件のビデオ
Introduction to k-nn12 分
3件の学習用教材
Module 1 Overview10 分
Lesson 1-1 Readings10 分
Lesson 1-2 Readings10 分
1の練習問題
Module 1 Graded Quiz20 分
2
9時間で修了

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.

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5件のビデオ (合計52分), 4 readings, 2 quizzes
5件のビデオ
Introduction to Decision Trees15 分
Introduction to Support Vector Machine13 分
4件の学習用教材
Module 2 Overview10 分
Lesson 2-1 Readings10 分
Lesson 2-3 Readings10 分
Lesson 2-4 Readings10 分
1の練習問題
Module 2 Graded Quiz20 分
3
8時間で修了

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.

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5件のビデオ (合計40分), 3 readings, 2 quizzes
5件のビデオ
Introduction to Boosting9 分
Introduction to ML Pipelines8 分
3件の学習用教材
Module 3 Overview10 分
Lesson 3-1 Readings10 分
Lesson 3-2 Readings10 分
1の練習問題
Module 3 Graded Quiz20 分
4
9時間で修了

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.

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5件のビデオ (合計48分), 4 readings, 2 quizzes
5件のビデオ
Introduction to Model-Selection16 分
Introduction to Regularization8 分
4件の学習用教材
Module 4 Overview10 分
Lesson 4-1 Readings10 分
Lesson 4-2 Readings10 分
Lesson 4-3 Readings10 分
1の練習問題
Module 4 Graded Quiz20 分

講師

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Robert Brunner

Professor
Accountancy

修士号の取得を目指しましょう

この コース は イリノイ大学アーバナ・シャンペーン校(University of Illinois at Urbana-Champaign) の100%オンラインの Master of Science in Accountancy (iMSA) の一部です。 プログラムのすべてで認定されれば、それらのコースが学位学習に加算されます。

イリノイ大学アーバナ・シャンペーン校(University of Illinois at Urbana-Champaign)について

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. ...

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