Key concepts machine learning

About Course

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

Course Curriculum

01. Course Overview

01. Course Overview

01:56

02. Introducing Machine Learning Concept

02. Prerequisites and Course Outline

02:03
03. What Is Machine Learning

07:15
04. Rule-based Systems

03:56
05. Rule-based Systems vs. ML-based Systems

05:50
06. Traditional ML Models and Representation ML Models

06:10
07. Traditional ML Models vs. Deep Learning ML Models

02:09
08. The Machine Learning Mindset

05:03
09. Examples of AI in the Real World

04:57

03. Identifying Problems Solved Using Machine Learning

04. Applying Machine Learning to Complex Data

05. Formulating a Simple Machine Learning Solution

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Last Updated:February 16, 2022

Key concepts machine learning

About Course

Course Curriculum

01. Course Overview

01. Course Overview

02. Introducing Machine Learning Concept

02. Prerequisites and Course Outline

03. What Is Machine Learning

04. Rule-based Systems

05. Rule-based Systems vs. ML-based Systems

06. Traditional ML Models and Representation ML Models

07. Traditional ML Models vs. Deep Learning ML Models

08. The Machine Learning Mindset

09. Examples of AI in the Real World

03. Identifying Problems Solved Using Machine Learning

10. Choosing the Right Machine Learning Solution

11. Supervised vs. Unsupervised Learning

12. Specialized ML Problems-Recommendation Systems

13. Specialized ML Problems-Association Rules Learning

14. Specialized ML Problems-Reinforcement Learning

15. Identifying Characteristics of Good ML Problems

16. Framing a Machine Learning Solution

04. Applying Machine Learning to Complex Data

17. Applying ML to Text Image and Speech Data

18. Applying Machine Learning to Text Data

19. Preprocessing Text Data

20. Applying Machine Learning to Image Data

21. The Intuition Behind CNNs

22. Applying Machine Learning to Speech Data

05. Formulating a Simple Machine Learning Solution

23. Quick Overview of Linear Regression

24. The Machine Learning Workflow

25. Demo-Exploring and Understanding the Salary Dataset

25. Demo-Exploring and Understanding the Salary Dataset

26. Demo-Dealing with Outliers and Missing Values

27. Demo-Performing Simple Linear Regression

28. Summary and Further Study

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