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Machine Learning Foundations: A Case Study Approach

Machine Learning Foundations: A Case Study Approach

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Course Highlights

  • 15 Hours of E-learning content, tests etc

  • Earn a Certificate on successful course completion from The University of Washington

  • Get unlimited access to the course content

  • Graded Assignments with Peer Feedback | 30 % got a tangible career benefit from this course

  • 32 % Started a new career after completing this specialization | Shareable Specialization and Course Certificates

Mode of learning: Online self study

Course Overview

What are the course deliverables ?
  • Identify potential applications of machine learning in practice

  • Describe the core differences in analyses enabled by regression, classification, and clustering

  • Select the appropriate machine learning task for a potential application

  • Apply regression, classification, clustering, retrieval, recommender systems, and deep learning

  • Represent your data as features to serve as input to machine learning models

  • Assess the model quality in terms of relevant error metrics for each task

  • Utilize a dataset to fit a model to analyze new data

  • Build an end-to-end application that uses machine learning at its core

  • Implement these techniques in Python.

More about this course
  • In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

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