课程信息
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100% 在线

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完成时间大约为40 小时

建议:6 hours/week...

英语(English)

字幕:英语(English)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为40 小时

建议:6 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 1 小时

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 个视频 (总计 9 分钟), 4 个阅读材料, 1 个测验
4 个阅读材料
Syllabus10分钟
About the Discussion Forums10分钟
Updating Your Profile10分钟
Social Media10分钟
1 个练习
Orientation Quiz10分钟
完成时间为 9 小时

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 个视频 (总计 47 分钟), 3 个阅读材料, 2 个测验
4 个视频
Introduction to k-nn12分钟
3 个阅读材料
Module 1 Overview10分钟
Lesson 1-1 Readings10分钟
Lesson 1-2 Readings10分钟
1 个练习
Module 1 Graded Quiz20分钟
2
完成时间为 9 小时

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 个视频 (总计 52 分钟), 4 个阅读材料, 2 个测验
5 个视频
Introduction to Decision Trees15分钟
Introduction to Support Vector Machine13分钟
4 个阅读材料
Module 2 Overview10分钟
Lesson 2-1 Readings10分钟
Lesson 2-3 Readings10分钟
Lesson 2-4 Readings10分钟
1 个练习
Module 2 Graded Quiz20分钟
3
完成时间为 8 小时

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 个视频 (总计 40 分钟), 3 个阅读材料, 2 个测验
5 个视频
Introduction to Boosting9分钟
Introduction to ML Pipelines8分钟
3 个阅读材料
Module 3 Overview10分钟
Lesson 3-1 Readings10分钟
Lesson 3-2 Readings10分钟
1 个练习
Module 3 Graded Quiz20分钟
4
完成时间为 9 小时

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 个视频 (总计 48 分钟), 4 个阅读材料, 2 个测验
5 个视频
Introduction to Model-Selection16分钟
Introduction to Regularization8分钟
4 个阅读材料
Module 4 Overview10分钟
Lesson 4-1 Readings10分钟
Lesson 4-2 Readings10分钟
Lesson 4-3 Readings10分钟
1 个练习
Module 4 Graded Quiz20分钟

讲师

Avatar

Robert Brunner

Professor
Accountancy

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此 课程 隶属于 伊利诺伊大学香槟分校 提供的 100% 在线 Master of Science in Accountancy (iMSA)。如果您被录取参加全部课程,您的课程将计入您的学位学习进程。

关于 伊利诺伊大学香槟分校

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