A Beginner's Guide to Artificial Intelligence and Machine Learning

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A Beginner's Guide to Artificial Intelligence and Machine Learning (Size: 357.5 MB)
  01-introduction.mkv 47.9 MB
  02-project.mkv 31.8 MB
  03-what_is_ai_ml.mkv 14.6 MB
  04-how_to_learn_machine_learning.mkv 21.5 MB
  05-worlds_simplest_model.mkv 21.1 MB
  06-build_a_face_emotion_classifier.mkv 46.5 MB
  07-how_to_improve_your_model.mkv 19.3 MB
  08-better_face_emotion_classifier.mkv 44.3 MB
  09-practice_dissecting_ml_problems.mkv 23.5 MB
  10-taxonomy_of_ml_topics.mkv 25.2 MB
  11-practice_dissecting_ai_products.mkv 7.6 MB
  12-next_steps.mkv 51.4 MB
  Bonus Resources.txt 307.2 B
  Get Bonus Downloads Here.url 204.8 B
  skillshare.a.beginners.guide.to.artificial.intelligence.and.machine.learning-skilledhares-sample.mkv 2.8 MB

Description


A Beginner's Guide to Artificial Intelligence and Machine Learning



Duration: 1h 5m | 12 Lessons | Video: 1280x720, 44kHz | 358 MB
Genre: eLearning | Language: English | Level: Beginner
What really is “artificial intelligence”? “Machine learning”? Let’s cut through the meaningless buzzwords, and talk the real talk.

This class covers must-know topics for AI product managers, ML practitioners, and anyone in between that’s curious about these emerging fields. We’ll cover many topics and takeaways:

Build a Face Emotion Classifier, to practice using ML concepts.
What artificial intelligence is and how it relates to machine learning
How to learn machine learning, with plenty of practice
Practice breaking down AI products into ML problems. Great for understanding ML news.
Practice breaking down ML problems. Great as interview practice.
Understand taxonomies of ML problems
ML concepts like linear regression, bias-variance, classification, regression, featurization etc.
This class involves minimal amounts of code but does not require any technical knowledge. Regardless of your background, you’ll walk away with the fundamentals for discussing, learning, and practicing machine learning.

Interested in more machine learning? Try my Computer Vision 101 (Applied ML) classes.

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