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Machine Learning for Accounting with Python

Курс от University of Illinois Urbana-Champaign
Средний≈ 64.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems. Accounting Data Analytics with Python is a prerequisite for this course. This course is running on the same platform (Jupyter Notebook) as that of the prerequisite course. While Accounting Data Analytics with Python covers data understanding and data preparation in the data analytics process, this course covers the next two steps in the process, modeling and model evaluation. Upon completion of the two courses, students should be able to complete an entire data analytics process with Python.

Навыки, которые вы освоите

Model EvaluationClassification AlgorithmsScikit Learn (Machine Learning Library)Applied Machine LearningPandas (Python Package)Time Series Analysis and ForecastingUnsupervised LearningRegression AnalysisModel OptimizationData PreprocessingFeature EngineeringMachine Learning AlgorithmsMachine LearningSupervised LearningText MiningJupyterPredictive ModelingModel TrainingUnstructured DataPython Programming

Программа курса

8 модулей · 100 учебных материалов

01Course Orientation and Module 1: Introduction to Machine Learning21 материалов

About the Course

Course IntroductionВидеоAbout Linden LuВидеоSyllabusЧтениеGlossaryЧтение

Учитесь у экспертов

Linden Lu

Instructor of Accountancy

Machine Learning for Accounting with Python
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 64.3 ч

8 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
About the Discussion ForumsОбсуждение
Online Education at Gies College of BusinessЧтение
Demographics SurveyPLUGIN

About Your Classmates

Getting to Know Your ClassmatesОбсуждениеUpdating Your ProfileЧтение

Module 1 Information

Module 1 OverviewЧтениеMake Connections to TopicОбсуждениеModule 1 IntroductionВидео

Lesson 1.1: Introduction to Machine Learning

1.1 Introduction to Machine LearningВидеоIntroduction to Machine LearningЛабораторная

Lesson 1.2: Introduction to Data Preprocessing

1.2 Introduction to Data PreprocessingВидеоIntroduction to Data PreprocessingЛабораторная

Lesson 1.3: Introduction to Machine Learning Algorithms

1.3 Introduction to Machine Learning AlgorithmsВидеоIntroduction to Machine Learning AlgorithmsЛабораторная

Module 1 Conclusion

Module 1 QuizЗаданиеModule 1 Programming AssignmentЛабораторнаяModule 1 Programming Assignment ScoreПрограммирование
02Module 2: Fundamental Algorithms I11 материалов

Module 2 Information

Module 2 OverviewЧтениеModule 2 IntroductionВидео

Lesson 2.1: Introduction to Linear Regression

2.1 Introduction to Linear RegressionВидеоIntroduction to Linear RegressionЛабораторная

Lesson 2.2: Introduction to Logistic Regression

2.2 Introduction to Logistic RegressionВидеоIntroduction to Logistic RegressionЛабораторная

Lesson 2.3: Introduction to Decision Tree

2.3 Introduction to Decision TreeВидеоIntroduction to Decision TreeЛабораторная

Module 2 Conclusion

Module 2 QuizЗаданиеModule 2 Programming AssignmentЛабораторнаяModule 2 Programming Assignment ScoreПрограммирование
03Module 3: Fundamental Algorithms II11 материалов

Module 3 Information

Module 3 OverviewЧтениеModule 3 IntroductionВидео

Lesson 3.1: Introduction to K-nearest Neighbors

3.1 Introduction to K-nearest NeighborsВидеоIntroduction to K-nearest NeighborsЛабораторная

Lesson 3.2: Introduction to Support Vector Machine

3.2 Introduction to Support Vector MachineВидеоIntroduction to Support Vector MachineЛабораторная

Lesson 3.3: Introduction to Bagging and Random Forest

3.3 Introduction to Bagging and Random ForestВидеоIntroduction to Bagging and Random ForestЛабораторная

Module 3 Conclusion

Module 3 QuizЗаданиеModule 3 Programming AssignmentЛабораторнаяModule 3 Programming Assignment ScoreПрограммирование
04Module 4: Model Evaluation11 материалов

Module 4 Information

Module 4 Overview ЧтениеModule 4 IntroductionВидео

Lesson 4.1: Regressive Evaluation Metrics

4.1 Regressive Evaluation MetricsВидеоRegressive Evaluation MetricsЛабораторная

Lesson 4.2: Classification Evaluation Metrics I

4.2 Classification Evaluation Metrics IВидеоClassification Evaluation Metrics IЛабораторная

Lesson 4.3: Classification Evaluation Metrics II

4.3 Classification Evaluation Metrics IIВидеоClassification Evaluation Metrics IIЛабораторная

Module 4 Conclusion

Module 4 QuizЗаданиеModule 4 Programming AssignmentЛабораторнаяModule 4 Programming Assignment ScoreПрограммирование
05Module 5: Model Optimization11 материалов

Module 5 Information

Module 5 OverviewЧтениеModule 5 IntroductionВидео

Lesson 5.1: Feature Selection

5.1 Introduction to Feature SelectionВидеоIntroduction to Feature SelectionЛабораторная

Lesson 5.2: Cross-Validation

5.2 Introduction to Cross-ValidationВидеоIntroduction to Cross-ValidationЛабораторная

Lesson 5.3: Model Selection

5.3 Introduction to Model SelectionВидеоIntroduction to Model SelectionЛабораторная

Module 5 Conclusion

Module 5 QuizЗаданиеModule 5 Programming AssignmentЛабораторнаяModule 5 Programming Assignment ScoreПрограммирование
06Module 6: Introduction to Text Analysis11 материалов

Module 6 Information

Module 6 OverviewЧтениеModule 6 IntroductionВидео

Lesson 6.1: Introduction to Text Analytics

6.1 Introduction to Text AnalyticsВидеоIntroduction to Text AnalyticsЛабораторная

Lesson 6.2: Text Classification

6.2 Introduction to Text ClassificationВидеоIntroduction to Text ClassificationЛабораторная

Lesson 6.3: Advanced Topics and Sentiment Analysis

6.3 Introduction to Text Classification IIВидеоIntroduction to Text Classification IIЛабораторная

Module 6 Conclusion

Module 6 QuizЗаданиеModule 6 Programming AssignmentЛабораторнаяModule 6 Programming Assignment ScoreПрограммирование
07Module 7: Introduction to Clustering11 материалов

Module 7 Information

Module 7 OverviewЧтениеModule 7 IntroductionВидео

Lesson 7.1: K-means

7.1 Introduction to K-means ClusteringВидеоIntroduction to K-means ClusteringЛабораторная

Lesson 7.2: Case Study—Credit Card Data

7.2 K-means Case StudyВидеоK-means Case StudyЛабораторная

Lesson 7.3: DBSCAN

7.3 Introduction to Density Based ClusteringВидеоIntroduction to Density Based ClusteringЛабораторная

Module 7 Conclusion

Module 7 QuizЗаданиеModule 7 Programming AssignmentЛабораторнаяModule 7 Programming Assignment ScoreПрограммирование
08Module 8: Introduction to Time Series Data13 материалов

Module 8 Information

Module 8 OverviewЧтениеModule 8 IntroductionВидео

Lesson 8.1: Working With Dates and Times

8.1 Working With Dates and TimesВидеоWorking With Dates and TimesЛабораторная

Lesson 8.2: Analyzing Time Series Data

8.2 Analyzing Time Series DataВидеоAnalyzing Time Series DataЛабораторная

Module 8 Conlusion

Module 8 QuizЗаданиеModule 8 Programming AssignmentЛабораторнаяModule 8 Programming Assignment ScoreПрограммированиеCongratulations on completing the course!ЧтениеGet Your Course CertificateЧтениеLearn on Your TermsВидео
Course-End SurveyPLUGIN