課程信息
4.5
4 個評分
100% 在線

100% 在線

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
完成時間(小時)

完成時間大約為38 小時

建議:6 hours/week...
可選語言

英語(English)

字幕:英語(English)...
100% 在線

100% 在線

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
完成時間(小時)

完成時間大約為38 小時

建議:6 hours/week...
可選語言

英語(English)

字幕:英語(English)...

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
完成時間為 1 小時

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....
Reading
2 個視頻(共 9 分鐘), 4 個閱讀材料, 1 個測驗
Video2 個視頻
Meet Professor Brunner4分鐘
Reading4 個閱讀材料
Syllabus10分鐘
About the Discussion Forums10分鐘
Updating Your Profile10分鐘
Social Media10分鐘
Quiz1 個練習
Orientation Quiz10分鐘
完成時間(小時)
完成時間為 9 小時

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....
Reading
4 個視頻(共 47 分鐘), 3 個閱讀材料, 2 個測驗
Video4 個視頻
Introduction to Machine Learning14分鐘
Introduction to Linear Regression14分鐘
Introduction to k-nn12分鐘
Reading3 個閱讀材料
Module 1 Overview10分鐘
Lesson 1-1 Readings10分鐘
Lesson 1-2 Readings10分鐘
Quiz1 個練習
Module 1 Graded Quiz20分鐘
2
完成時間(小時)
完成時間為 9 小時

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....
Reading
5 個視頻(共 52 分鐘), 4 個閱讀材料, 2 個測驗
Video5 個視頻
Introduction to Fundamental Algorithms3分鐘
Introduction to Logistics Regression14分鐘
Introduction to Decision Trees15分鐘
Introduction to Support Vector Machine13分鐘
Reading4 個閱讀材料
Module 2 Overview10分鐘
Lesson 2-1 Readings10分鐘
Lesson 2-3 Readings10分鐘
Lesson 2-4 Readings10分鐘
Quiz1 個練習
Module 2 Graded Quiz20分鐘
3
完成時間(小時)
完成時間為 8 小時

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. ...
Reading
5 個視頻(共 40 分鐘), 3 個閱讀材料, 2 個測驗
Video5 個視頻
Introduction to Modeling Success6分鐘
Introduction to Bagging11分鐘
Introduction to Boosting9分鐘
Introduction to ML Pipelines8分鐘
Reading3 個閱讀材料
Module 3 Overview10分鐘
Lesson 3-1 Readings10分鐘
Lesson 3-2 Readings10分鐘
Quiz1 個練習
Module 3 Graded Quiz20分鐘
4
完成時間(小時)
完成時間為 9 小時

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....
Reading
5 個視頻(共 48 分鐘), 4 個閱讀材料, 2 個測驗
Video5 個視頻
Introduction to Overfitting4分鐘
Introduction to Cross-Validation13分鐘
Introduction to Model-Selection16分鐘
Introduction to Regularization8分鐘
Reading4 個閱讀材料
Module 4 Overview10分鐘
Lesson 4-1 Readings10分鐘
Lesson 4-2 Readings10分鐘
Lesson 4-3 Readings10分鐘
Quiz1 個練習
Module 4 Graded Quiz20分鐘

講師

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Robert Brunner

Professor
Accountancy
Graduation Cap

Start working towards your Master's degree

This 課程 is part of the 100% online Master of Science in Accountancy (iMSA) from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

關於 University of Illinois at Urbana-Champaign

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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