A Brief History of Modern AI and its Applications-Course Introduction
Course Introduction
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Course Prerequisites
A Brief History of Modern AI and its Applications-Introduction to Artificial Intelligence and Machine Learning
Introduction to Artificial Intelligence and Machine Learning
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Machine Learning and Deep Learning
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Machine Learning and Deep Learning - Part 1
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Machine Learning and Deep Learning - Part 2
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History of AI
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History of Machine Learning and Deep Learning
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A Brief History of Modern AI and its Applications-Modern AI: Applications and the Machine Learning Workflow
Modern AI
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Applications
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Machine Learning Workflow
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A Brief History of Modern AI and its Applications-End of the module review & evaluation
Review
Retrieving and Cleaning Data-Retrieving Data
Retrieving Data from CSV and JSON Files
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Retrieving Data from Databases, APIs, and the Cloud
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[Optional] Download Assets for Lab: Reading Data in Database Files - Part A
[Optional] Lab Solution: Reading Data Jupyter Notebook - Part A
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[Optional] Download Assets for Lab: Reading Data in Jupyter Notebook - Part B
[Optional]Lab Solution: Reading in Database Files - Part B
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Retrieving and Cleaning Data-Data Cleaning
Data Cleaning
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Handling Missing Values and Outliers
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Handling Missing Values and Outliers using Residuals
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Retrieving and Cleaning Data-End of the module review & evaluation
Summary/Review
Exploratory Data Analysis and Feature Engineering-Exploratory Data Analysis
Introduction to Exploratory Data Analysis (EDA)
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EDA with Visualization
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Grouping Data for EDA
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[Optional] Download Assets for Lab: Exploratory Data Analysis Lab
[Optional]Solution: EDA Notebook - Part 1
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[Optional]Solution: EDA Notebook - Part 2
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[Optional]Solution: EDA Notebook - Part 3
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[Optional]Solution: EDA Notebook - Part 4
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Exploratory Data Analysis and Feature Engineering-Feature Engineering and Variable Transformation
Feature Engineering and Variable Transformation - Background
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Variable Transformation
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Feature Encoding
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Feature Scaling
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Common Variable Transformations in Python
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[Optional] Download Assets for Lab: Feature Engineering Demo
[Optional] Solution: Feature Engineering Lab - Part 1
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[Optional] Solution: Feature Engineering Lab - Part 2
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[Optional] Solution: Feature Engineering Lab - Part 3
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Exploratory Data Analysis and Feature Engineering-End of module review and evaluation
Summary/Review
Inferential Statistics and Hypothesis Testing-Estimation and Inference, and Hypothesis Testing
Estimation and Inference - Introduction
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Estimation and Inference - Example
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Estimation and Inference - Parametric vs. Non-Parametric
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Estimation and Inference - Commonly Used Distributions
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Frequentist vs. Bayesian Statistics
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Inferential Statistics and Hypothesis Testing-Hypothesis Testing
Introduction to Hypothesis
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Hypothesis Testing Example
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Bayesian Interpretation of Hypothesis Testing Example
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Type 1 vs Type 2 Error
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Type 1 vs Type 2 Error: Examples
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Hypothesis Testing Terminology
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Significance Level and P-Values
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Significance Level and P-Values and the F Statistic
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[Optional] Download Assets for Lab: Hypothesis Testing Demo
[Optional] Hypothesis Testing Demo - Part 1
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[Optional] Hypothesis Testing Demo - Part 2
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Correlation vs Causation
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Inferential Statistics and Hypothesis Testing-End of module review & evaluation
Summary/Review
(Optional) HONORS Project-Final Project
Project Overview