Experimental Design for Data Analysis

سرفصل های دوره

This course covers conceptual and practical aspects of building and evaluating machine learning models in a way that uses data judiciously, while also accounting for considerations such as ordering and relationships within data and other biases.

1. Course Overview

1. Course Overview

2. Designing an Experiment for Data Analysis

1. Module Overview

2. Prerequisites and Course Outline

3. Connecting the Dots with Data

4. Hypothesis Testing

5. T-tests

6. ANOVA

7. Designing a Machine Learning Experiment

8. Summary

3. Building and Training a Machine Learning Model

1. Module Overview

2. Getting Started with Azure ML Studio

3. Loading and Visualizing Data

4. Exploring Relationships in Data

5. Preprocessing and Preparing Data

6. Building and Training a Regression Model for Price Prediction

7. Building and Training a Regression Model in Python

8. Summary

4. Understanding and Overcoming Common Problems in Data Modeling

1. Module Overview

2. Overfitting and Techniques to Mitigate Overfitting

3. Accuracy, Precision, and Recall

4. The ROC Curve

5. Preparing and Processing Data

6. Building Training and Evaluating a Classification Model

7. Summary

5. Leveraging Different Validation Strategies in Data Modeling

01. Module Overview

02. Cross-validation in the ML Workflow

03. Singular Cross-validation

04. Cross-validation Using Azure ML Studio

05. K-fold Cross-validation and Variants

06. K-fold Cross-validation in scikit-learn

07. Repeated K-fold Cross-validation in scikit-learn

08. Stratified K-fold Cross-validation in scikit-learn

09. Group K-fold in scikit-learn

10. Summary

6. Tuning Hyperparameters Using Cross Validation Scores

1. Module Overview

2. Hyperparameter Tuning

3. Decision Trees

4. Hyperparameter Tuning a Decision Forest Classifier

5. Tuning and Scoring Multiple Models

6. Summary and Further Study

439,000 تومان

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تولید کننده: PluralSight