by Fred Schenkelberg Leave a Comment

Machine Learning Methods

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Course Content

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Course Introduction

Module 1: Introductory Concepts 8 Lessons

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M1-1 AI and Machine Learning

M1-2 Problems and Algorithms

M1-3 Computer Programming

M1-5 Mathematical Notation

M1-6 Training and Testing

M1-7 Confusion Matrix

M1-8 ROC and Regression Performance

Module 2: Statistical Methods 4 Lessons

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M2-1 Basic Statistics

M2-2 Probability

M2-3 Inferential Statistics

M2-4 Naïve Bayes

Module 3: Data Preprocessing 8 Lessons

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M3-1 Real Datasets

M3-2 Data Cleaning

M3-3 Missing Data

M3-4 Outlier Investigation

M3-5 Transformation

M3-6 Integration

M3-7 Data Reduction

M3-8 Principal Component Analysis

Module 4: Cluster Analysis 4 Lessons

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M4-1 Clustering Theory

M4-3 Hierarchical Clustering

M4-4 Density Based Clustering

Module 5: Regression Modeling 7 Lessons

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M5-1 Density Based Clustering Correlation

M5-2 Simple Linear Regression

M5-3 Multiple Regression

M5-4 Stepwise Regression

M5-5 Logistic Regression

M5-6 Spline Functions

M5-7 Regression Splines (MARS)

Module 6: Decision Tree Theory 5 Lessons

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M6-1 Classification Trees

M6-2 Purity Measures

M6-3 Numerical Features

M6-4 Pruning and Missing Data

M6-5 Regression Trees

Module 7: Decision Tree Algorithms 8 Lessons

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M7-1 Introducing CART

M7-2 Applying CART

M7-4 Introducing Random Forest

M7-5 Applying Random Forest

M7-6 Gradient Boosting

M7-7 Introducing TreeNet

M7-8 Applying TreeNet

Course Conclusion

About Fred Schenkelberg

I am the reliability expert at FMS Reliability, a reliability engineering and management consulting firm I founded in 2004. I left Hewlett Packard (HP)’s Reliability Team, where I helped create a culture of reliability across the corporation, to assist other organizations.

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