Machine Learning Foundations: A Case Study Approach

Machine Learning Foundations: A Case Study Approach

This course covers the fundamental machine learning concepts and how to use them to solve real-world problems. You will learn to predict house prices, analyze user reviews, find specific documents, recommend products, and search for images. By the end of the course, you will be able to apply machine learning techniques in various fields.

The course is designed for business professionals who want to understand how machine learning can improve their businesses. It does not require any prior knowledge of machine learning, and all of the code is provided in Python.

The course is divided into three parts:

  • Part 1: introduces the core concepts of machine learning (ML), such as supervised learning, unsupervised learning, and reinforcement learning.
  • Part 2: covers the different types of ML models, including linear regression, logistic regression, decision trees, and neural networks.
  • Part 3: discusses the different ways to evaluate the performance of machine learning models.

The course also includes several hands-on exercises, during which you will apply the concepts you have learned to real-world data sets. This course is for you if you are interested in learning more about machine learning and how to apply it to your business.

๐Ÿ†“ Free to Audit
๐Ÿ•’ Approx. 18 Hours
๐Ÿ“ˆ Intermediate Level
๐Ÿงพ Paid Certificate Available
๐ŸŽ“ Offered by The University of Washington via Coursera

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