Introduction to the Course-About the Course
Course Introduction
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About Linden Lu
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Syllabus
Glossary
Online Education at Gies College of Business
Introduction to the Course-About Your Classmates
Updating Your Profile
Module 1: Introduction to Machine Learning-Module 1 Information
Module 1 Overview
Module 1 Introduction
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Module 1: Introduction to Machine Learning-Lesson 1.1: Introduction to Machine Learning
1.1 Introduction to Machine Learning
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Module 1: Introduction to Machine Learning-Lesson 1.2: Introduction to Data Preprocessing
1.2 Introduction to Data Preprocessing
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Module 1: Introduction to Machine Learning-Lesson 1.3: Introduction to Machine Learning Algorithms
1.3 Introduction to Machine Learning Algorithms
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Module 2: Fundamental Algorithms I-Module 2 Information
Module 2 Overview
Module 2 Introduction
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Module 2: Fundamental Algorithms I-Lesson 2.1: Introduction to Linear Regression
2.1 Introduction to Linear Regression
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Module 2: Fundamental Algorithms I-Lesson 2.2: Introduction to Logistic Regression
2.2 Introduction to Logistic Regression
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Module 2: Fundamental Algorithms I-Lesson 2.3: Introduction to Decision Tree
2.3 Introduction to Decision Tree
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Module 3: Fundamental Algorithms II-Module 3 Information
Module 3 Overview
Module 3 Introduction
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Module 3: Fundamental Algorithms II-Lesson 3.1: Introduction to K-nearest Neighbors
3.1 Introduction to K-nearest Neighbors
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Module 3: Fundamental Algorithms II-Lesson 3.2: Introduction to Support Vector Machine
3.2 Introduction to Support Vector Machine
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Module 3: Fundamental Algorithms II-Lesson 3.3: Introduction to Bagging and Random Forest
3.3 Introduction to Bagging and Random Forest
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Module 4: Model Evaluation-Module 4 Information
Module 4 Overview
Module 4 Introduction
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Module 4: Model Evaluation-Lesson 4.1: Regressive Evaluation Metrics
4.1 Regressive Evaluation Metrics
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Module 4: Model Evaluation-Lesson 4.2: Classification Evaluation Metrics I
4.2 Classification Evaluation Metrics I
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Module 4: Model Evaluation-Lesson 4.3: Classification Evaluation Metrics II
4.3 Classification Evaluation Metrics II
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Module 5: Model Optimization-Module 5 Information
Module 5 Overview
Module 5 Introduction
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Module 5: Model Optimization-Lesson 5.1: Feature Selection
5.1 Introduction to Feature Selection
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Module 5: Model Optimization-Lesson 5.2: Cross-Validation
5.2 Introduction to Cross-Validation
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Module 5: Model Optimization-Lesson 5.3: Model Selection
5.3 Introduction to Model Selection
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Module 6: Introduction to Text Analysis-Module 6 Information
Module 6 Overview
Module 6 Introduction
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Module 6: Introduction to Text Analysis-Lesson 6.1: Introduction to Text Analytics
6.1 Introduction to Text Analytics
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Module 6: Introduction to Text Analysis-Lesson 6.2: Text Classification
6.2 Introduction to Text Classification
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Module 6: Introduction to Text Analysis-Lesson 6.3: Advanced Topics and Sentiment Analysis
6.3 Introduction to Text Classification II
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Module 7: Introduction to Clustering-Module 7 Information
Module 7 Overview
Module 7 Introduction
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Module 7: Introduction to Clustering-Lesson 7.1: K-means
7.1 Introduction to K-means Clustering
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Module 7: Introduction to Clustering-Lesson 7.2: Case Study—Credit Card Data
7.2 K-means Case Study
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Module 7: Introduction to Clustering-Lesson 7.3: DBSCAN
7.3 Introduction to Density Based Clustering
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Module 8: Introduction to Time Series Data-Module 8 Information
Module 8 Overview
Module 8 Introduction
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Module 8: Introduction to Time Series Data-Lesson 8.1: Working With Dates and Times
8.1 Working With Dates and Times
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Module 8: Introduction to Time Series Data-Lesson 8.2: Analyzing Time Series Data
8.2 Analyzing Time Series Data
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Module 8: Introduction to Time Series Data-Module 8 Conlusion
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