Building Your First Machine Learning Solution

سرفصل های دوره

Machine learning is exciting, yet, it may sound more complicated than it is actually is. This course empowers you with the necessary theory and practice to become confident about how machine learning works by building a hands-on solution.

1. Course Overview

1. Course Overview

2. Getting Your Feet Ready to Run

1. Overview

2. What to Expect

3. On Machine Learning

4. What Is Different About Machine Learning-

5. Learning Types

6. Machine Learning Pipeline

7. Problem Definition

8. Introducing Google Collaboratory

9. Summary

3. Feeding Your Machine Learning Pipeline

1. Overview

2. Revisiting ML Pipeline

3. Understanding Data Sourcing

4. CSV Format

5. Understanding SciPy

6. Demo- Loading Data into Pandas

7. Summary

4. Understanding the Overall Data Trends

01. Overview

02. Revisiting ML Pipeline

03. Introducing Data Analysis

04. Univariant Numerical Analysis

05. Bivariant Numerical Analysis

06. Demo- Descriptive Stats - Part One

07. Demo- Descriptive Stats - Part Two

08. Data Visualization

09. Demo- Data Visualization - Part One

10. Demo- Data Visualization - Part Two

11. Summary

5. Making Your Data Ready for the ML Model

01. Overview

02. Revisting ML Pipeline

03. Data Scaling- The Problem

04. Data Scaling- The Solution

05. The Need for Data Segregation

06. Train Test Split

07. KFlod Cross Validation

08. Welcoming scikit-learn

09. Demo- Data Segregation Techniques

10. Summary

6. Implementing Your Regression Solution

01. Overview

02. Revisiting ML Pipeline

03. Scoping Your Focus

04. Introducing Derivatives

05. Linear Regression

06. Variance Bias Tradeoff

07. Other Regression Algorithms

08. Model Evaluation

09. Demo- Deploying and Testing the Model - Part 1

10. Demo- Deploying and Testing the Model - Part 2

11. Summary

7. What Is Next-

1. Overview

2. Handling Features

3. Model Improvement

4. Automated ML

5. Operationalization

6. Team Data Science Process

7. Summary

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تولید کننده: PluralSight