Building Your First Machine Learning Solution

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Mohammed Osman

3:08:23

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1. Course Overview

1. Course Overview.mp4

01:52

2. Getting Your Feet Ready to Run

1. Overview.mp4

01:16

2. What to Expect.mp4

01:13

3. On Machine Learning.mp4

04:18

4. What Is Different About Machine Learning-.mp4

02:40

5. Learning Types.mp4

07:39

6. Machine Learning Pipeline.mp4

06:58

7. Problem Definition.mp4

01:57

8. Introducing Google Collaboratory.mp4

04:14

9. Summary.mp4

00:48

3. Feeding Your Machine Learning Pipeline

1. Overview.mp4

01:01

2. Revisiting ML Pipeline.mp4

01:08

3. Understanding Data Sourcing.mp4

05:13

4. CSV Format.mp4

01:28

5. Understanding SciPy.mp4

05:03

6. Demo- Loading Data into Pandas.mp4

04:15

7. Summary.mp4

00:51

4. Understanding the Overall Data Trends

01. Overview.mp4

01:02

02. Revisiting ML Pipeline.mp4

01:36

03. Introducing Data Analysis.mp4

03:18

04. Univariant Numerical Analysis.mp4

09:10

05. Bivariant Numerical Analysis.mp4

05:45

06. Demo- Descriptive Stats - Part One.mp4

06:17

07. Demo- Descriptive Stats - Part Two.mp4

03:10

08. Data Visualization.mp4

07:23

09. Demo- Data Visualization - Part One.mp4

04:30

10. Demo- Data Visualization - Part Two.mp4

02:52

11. Summary.mp4

00:47

5. Making Your Data Ready for the ML Model

01. Overview.mp4

01:21

02. Revisting ML Pipeline.mp4

01:14

03. Data Scaling- The Problem.mp4

06:40

04. Data Scaling- The Solution.mp4

03:16

05. The Need for Data Segregation.mp4

04:19

06. Train Test Split.mp4

03:33

07. KFlod Cross Validation.mp4

03:32

08. Welcoming scikit-learn.mp4

02:27

09. Demo- Data Segregation Techniques.mp4

04:01

10. Summary.mp4

01:23

6. Implementing Your Regression Solution

01. Overview.mp4

01:05

02. Revisiting ML Pipeline.mp4

01:23

03. Scoping Your Focus.mp4

05:19

04. Introducing Derivatives.mp4

05:31

05. Linear Regression.mp4

03:43

06. Variance Bias Tradeoff.mp4

05:58

07. Other Regression Algorithms.mp4

02:30

08. Model Evaluation.mp4

03:16

09. Demo- Deploying and Testing the Model - Part 1.mp4

06:26

10. Demo- Deploying and Testing the Model - Part 2.mp4

06:10

11. Summary.mp4

02:04

7. What Is Next-

1. Overview.mp4

01:21

2. Handling Features.mp4

02:04

3. Model Improvement.mp4

01:23

4. Automated ML.mp4

04:09

5. Operationalization.mp4

01:48

6. Team Data Science Process.mp4

02:10

7. Summary.mp4

02:33

Description

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.

What You'll Learn?

Machine learning is perceived as a difficult, challenging, and math-intensive topic. In this course, Building Your First Machine Learning solution, you will discover the magic of machine learning and understand the theory behind it. First, you will learn what machine learning is, its types, its applications, why it is getting traction, and what its phases are. Next, you will discover how vital the data is for machine learning solutions, how to source it, analyze it, and pre-process it for subsequent machine learning steps. Finally, you will explore how to train your machine learning algorithms and evaluate them. Moreover, you will develop knowledge around recent trends in machine learning, such as AI as a Service. When you are finished with this course, you will have a firm understanding of machine learning with the ability to build a basic regression machine learning solution.

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Machine Learning

Mohammed Osman

Instructor's Courses

Mohammed Osman is a senior software engineer who started coding at the age of 13. Mohammed worked in various industries, including telecommunication, accounting, banking, health, and assurance. Mohammed's core skillset is a .NET ecosystem with a strong focus on C#, Azure, and Data Science. Mohammed also enjoys the soft-side of software engineering and leads scrum teams. Mohammed runs a blog with the message "Making your code smart and your career smarter." He shares tips and techniques to improve your code and valuable career pieces of advice in his blog.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.