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Artificial Intelligence Foundations: Machine Learning (2018)

Artificial Intelligence Foundations: Machine Learning (2018)

1h 18mBeginner2018-03-29

Authors

Doug Rose

Doug Rose

Teaching Fortune 500s and professionals how to lead change

Course details

Machine learning is one of the liveliest areas in artificial intelligence. Machine learning algorithms allow computers to learn new things without being programmed. They use statistics as a way to better understand the massive amounts of data that we create every day. These newer algorithms help machines classify images, sounds, and videos. They can answer our questions, discover new drugs, and even write songs. In this course, we review the definition and types of machine learning: supervised, unsupervised, and reinforcement. Then you can see how to use popular algorithms such as decision trees, clustering, and regression analysis to see patterns in your massive data sets. Finally you can learn about some of the pitfalls when starting out with machine learning.


Topics include:
Describe how to work with data.
Apply machine learning principles.
Distinguish different types of machine learning.
Identify problems that use machine learning.
Create decision trees.
Explain how to select the best algorithm.

Skills covered

Machine LearningArtificial Intelligence FoundationsFoundationsArtificial Intelligence (AI)

Concepts

0. Introduction

  • 01 - Welcome

1. What Is Machine Learning

  • 02 - What it means to learn
  • 03 - Work with data
  • 04 - Apply machine learning
  • 05 - Different types of machine learning

2. Different Ways a Machine Learns

  • 06 - Supervised
  • 07 - Unsupervised
  • 08 - Semi-supervised
  • 09 - Reinforcement

3. Popular Machine Learning Algorithms

  • 10 - Problems that use machine learning
  • 11 - Decision trees
  • 12 - k-nearest neighbor
  • 13 - K-mean clustering
  • 14 - Regression
  • 15 - Naive Bayes

4. Applying Algorithms

  • 16 - Follow the data
  • 17 - Fit the data
  • 18 - Select the best algorithm

5. Common Challenges

  • 19 - Machine learning challenges

Conclusion

  • 20 - Next steps

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