Simplified: Intro to Machine Learning

Learn what machine learning is all about in this beginner-level entry course

Ratings 3.85 / 5.00
Simplified: Intro to Machine Learning

What You Will Learn!

  • Machine Learning
  • Applications of Machine Learning
  • Basics of Python in the Context of Data Science
  • Linear Regression
  • Logistic Regression
  • Neural Networks
  • Debugging a Faulty Algorithm
  • Regularization

Description

This is the first of a series of courses dedicated to teaching students with an understanding of basic computer science concepts and little to no pre-existing knowledge of machine learning.  Specifically, "Machine Learning Simplified" targets individuals who can't afford an expensive machine learning course and do not have the extensive pre-requisites the majority of courses require.  Why learn machine learning?  Artificial intelligence has already established itself as the future of modern society.  Experts predict that up to 20 million jobs will be lost to AI by 2030.  Therefore, to stay competitive in the constantly changing labor force, it's critical to keep up with new technology.  Machine learning, one of the biggest sectors of artificial intelligence, has shown to be a promising, newly emerging field in the tech industry.  After completing this course and the rest of the courses in the series, you will have an in-depth understanding of machine learning. This course has a strong emphasis on ensuring that you understand each topic conceptually before proceeding to the technical aspect. The graphics in each video are also created with an end goal of helping you grasp each concept at a basic level. If you have any questions after going through the course, you can always reach out to me.

Who Should Attend!

  • High school students with limited computer science knowledge
  • Individuals interested in machine learning but don't have the extensive pre-requisites other courses require
  • Students who can't afford the expensive machine learning certification courses

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Tags

  • Machine Learning

Subscribers

4991

Lectures

29

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