Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure

سرفصل های دوره

In this course, you'll learn how to prepare, clean up, and engineer new features from the data with Azure Machine Learning, so the dataset can be represented in a form that's easy for the learning algorithm to learn the patterns.

1. Course Overview

1. Course Overview

2. Getting Started with Azure Machine Learning

1. Introduction

2. What Is Machine Learning

3. Introduction to Azure Machine Learning

4. Azure Machine Learning Experiment Workflow

5. Prerequisites

6. Demo - Creating an Azure Machine Learning Studio Workspace

7. Demo - Creating an Azure Machine Learning Service Workspace

8. Demo - Exploring the Dataset

9. Summary

3. Differentiating Data, Features, Targets, and Models

1. Introduction

2. Moving from Raw Data to Features

3. 6 Characteristics of a Good Feature

4. Define Target for ML Problems

5. Demo - Exploring Datasets for Different Problems

6. How Algorithms Learn Models

7. Demo - Modifying the Metadata of Datasets

8. Summary

4. Preparing Input Data for Machine Learning Models

01. Introduction

02. Data Preprocessing Methods

03. Demo - Exploratory Data Analysis

04. Demo - Data Cleaning (Erroneous Data)

05. Demo - Data Cleaning (Outliers)

06. Demo - Data Cleaning (Duplicate Rows)

07. Demo - Data Transformation

08. Demo - Reducing Data (Record Sampling)

09. Demo - Reducing Data (Attribute Sampling)

10. Demo - Discretizing Data

11. Entropy-based Discretization

12. Demo - Entropy-based Discretization

13. Summary

5. Handling Missing Data

01. Introduction

02. Reasons Why Data Is Missing

03. Demo - Listwise Deletion

04. Problems in Deleting Rows

05. Demo - Using Indicator Variables

06. Replace with Mean, Median, and Mode

07. Disadvantages of Single Imputation Methods

08. Demo - Replace with MICE

09. How MICE Works

10. Summary

6. Role of Feature Engineering in Machine Learning

1. Introduction

2. Why Feature Engineering

3. Role of Feature Engineering in Model Complexity

4. Build Better Models with Feature Engineering

5. Feature Engineering Numeric Variables

6. Feature Engineering Categorical Variables

7. Demo - One-hot Encoding Categorical Variables

8. Demo - Learning with Counts Categorical Variables

9. Summary

7. Split a Data Set into Training and Testing Subsets

1. Introduction

2. Demo - Training and Testing on Same Data

3. Demo - Split Data into Training and Test Set

4. Splitting Data for Model Tuning

5. Demo - Cross-validation

6. Demo - Model Selection

7. Leave-one-out Cross Validation

8. Summary

8. Identify Data-level Issues In Machine Learning Models

1. Introduction

2. Imbalanced Dataset for Classification Problems

3. Demo - SMOTE

4. Data Scale Issues in Distance-based Models

5. Multicollinearity Problem in Regression Models

6. Outliers in Regression Models

7. Problem with High-dimensional Datasets

8. Summary

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تولید کننده: PluralSight