Collect the Dataset-Overview
Follow a Machine Learning Workflow Course Introduction
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CAIP Specialization Introduction
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Collect the Dataset Module Introduction
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Overview
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Collect the Dataset-Data Collection
Machine Learning Datasets
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Data Structure Terminology
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Data Quality Issues
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Data Sources
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Open Datasets
Guidelines for Selecting a Machine Learning Dataset
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ETL and Machine Learning Pipelines
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Guidelines for Loading a Dataset
Analyze the Dataset-Overview
Analyze the Dataset Module Introduction
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Overview
Analyze the Dataset-Statistical Analysis
Dataset Content and Format
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Guidelines for Exploring the Structure of a Dataset
Distributions
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Descriptive Statistical Analysis
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Central Tendency
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Variability and Range
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Variance and Standard Deviation
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Skewness
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Kurtosis
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Statistical Moments
Correlation Coefficient
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Guidelines for Analyzing a Dataset
Analyze the Dataset-Visual Analysis
Visualizations
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Histogram
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Box Plot
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Scatterplot
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Maps
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Guidelines for Using Visualizations to Analyze Data
Prepare the Dataset-Overview
Prepare the Dataset Module Introduction
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Overview
Prepare the Dataset-Data Preparation
Data Preparation
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Data Types
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Operations You Can Perform on Different Types of Data
Continuous vs. Discrete Variables
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Data Encoding
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Dimensionality Reduction
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Missing and Duplicate Values
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Normalization and Standardization
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Summarization
Holdout Method
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Guidelines for Preparing Training and Testing Data
Set Up and Train a Model-Overview
Set Up and Train a Model Module Introduction
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Overview
Set Up and Train a Model-Set Up a Machine Learning Model
Design of Experiments
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Hypothesis Testing
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p-value and Confidence Interval
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Machine Learning Algorithms
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Guidelines for Setting Up a Machine Learning Model
Set Up and Train a Model-Train the Model
Iterative Tuning
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Bias and Generalizations
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Cross-Validation
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Feature Transformation
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The Bias–Variance Tradeoff
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Parameters
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Regularization
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Training Efficiency
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Guidelines for Training and Tuning the Model
Finalize the Model-Overview
Finalize the Model Module Introduction
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Overview
Finalize the Model-Model Finalization
Know Your Audience
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Use Visualization to Present Your Findings
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Put Together a Machine Learning Presentation
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Communicate Your Findings Clearly
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Put a Model into Production
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Pipeline Automation
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Testing and Maintenance
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Consumer-Oriented Applications
Guidelines for Incorporating Machine Learning into a Long-Term Solution