What is Machine Learning-Course Overview
Specialization Overview
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Instructor Introduction
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Course Overiew
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About the Course
Module 1 Slides
What is Machine Learning-Introduction to Machine Learning
Module 1 Introduction & Objectives
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Introduction to Machine Learning
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Data Terminology
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What is a Model?
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Types of Machine Learning
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What ML Can and Cannot Do
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What is Machine Learning-Review
Module Wrap-up
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The Modeling Process-Building a Model
Introduction and Objectives
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Building a Model
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Feature Selection
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Algorithm Selection
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The Modeling Process-Model Selection
Bias-Variance Tradeoff
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Test and Validation Sets
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Cross Validation
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The Modeling Process-Review
Module Wrap-up
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Evaluating & Interpreting Models-Metrics in ML Projects
Introduction and Objectives
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Outcomes vs Outputs
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Model Output Metrics
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Evaluating & Interpreting Models-Regression and Classification Metrics
Regression Error Metrics
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Classification Error Metrics: Confusion Matrix
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Classification Error Metrics: ROC and PR Curves
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Evaluating & Interpreting Models-Review
Troubleshooting Model Performance
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Module Wrap-up
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Linear Models-Linear Regression & Regularization
Introduction and Objectives
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Linear Regression
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Regularization
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Linear Models-Logistic Regression
Logistic Regression
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Softmax Regression
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Linear Models-Review
Module Wrap-up
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Trees, Ensemble Models and Clustering-Tree and Ensemble Models
Introduction and Objectives
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Tree Models
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Ensemble Models
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Random Forest
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Trees, Ensemble Models and Clustering-Clustering
Clustering
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K-Means Clustering
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Trees, Ensemble Models and Clustering-Review
Module Wrap-up
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Deep Learning & Course Project-Neural Networks
Introduction and Objectives
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Introduction to Deep Learning
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Artificial Neurons
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From Neurons to Neural Networks
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Training Neural Networks
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Deep Learning & Course Project-Applications of Deep Learning
Computer Vision
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Natural Language Processing
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Deep Learning & Course Project-Review
Module Wrap-up
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Deep Learning & Course Project-Course Wrap Up
Course Project Modeling Options
Course Wrap-up
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