Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community.
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Data Analytics Foundations for Accountancy II
von
883 bereits angemeldet!
University of Illinois at Urbana-Champaign
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
11 Stunde zum Abschließen
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 (Gesamt 9 min), 4 Lektüren, 1 Quiz
4 Lektüren
Syllabus10m
About the Discussion Forums10m
Updating Your Profile10m
Social Media10m
1 praktische Übung
Orientation Quiz10m
9 Stunden zum Abschließen
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 (Gesamt 47 min), 3 Lektüren, 2 Quiz
4 Videos
Introduction to Machine Learning14m
Introduction to Linear Regression14m
Introduction to k-nn12m
3 Lektüren
Module 1 Overview10m
Lesson 1-1 Readings10m
Lesson 1-2 Readings10m
1 praktische Übung
Module 1 Graded Quiz20m
Woche
29 Stunden zum Abschließen
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 (Gesamt 52 min), 4 Lektüren, 2 Quiz
5 Videos
Introduction to Fundamental Algorithms3m
Introduction to Logistics Regression14m
Introduction to Decision Trees15m
Introduction to Support Vector Machine13m
4 Lektüren
Module 2 Overview10m
Lesson 2-1 Readings10m
Lesson 2-3 Readings10m
Lesson 2-4 Readings10m
1 praktische Übung
Module 2 Graded Quiz20m
Woche
38 Stunden zum Abschließen
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 (Gesamt 40 min), 3 Lektüren, 2 Quiz
5 Videos
Introduction to Modeling Success6m
Introduction to Bagging11m
Introduction to Boosting9m
Introduction to ML Pipelines8m
3 Lektüren
Module 3 Overview10m
Lesson 3-1 Readings10m
Lesson 3-2 Readings10m
1 praktische Übung
Module 3 Graded Quiz20m
Woche
49 Stunden zum Abschließen
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 (Gesamt 48 min), 4 Lektüren, 2 Quiz
5 Videos
Introduction to Overfitting4m
Introduction to Cross-Validation13m
Introduction to Model-Selection16m
Introduction to Regularization8m
4 Lektüren
Module 4 Overview10m
Lesson 4-1 Readings10m
Lesson 4-2 Readings10m
Lesson 4-3 Readings10m
1 praktische Übung
Module 4 Graded Quiz20m
Dozent
Robert Brunner
ProfessorAccountancy
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