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Machine Learning Foundations: A Case Study Approach   

ABOUT THIS COURSE

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?

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.

This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.

Learning Outcomes: By the end of this course, you will be able to:
-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.

Estimated Learning Time:  18 hours

SKILLS YOU WILL GAIN:

Machine Learning

Probability & Statistics

General Statistics

Algorithms

Deep Learning

Regression

Theoretical Computer Science

Applied Machine Learning

Computer Programming

Data Analysis

Machine Learning Algorithms

Python Programming

Statistical Programming

INSTRUCTORS

Course Instructor PhotoEmily Fox
Amazon Professor of Machine Learning
Statistics

Course Instructor PhotoCarlos Guestrin
Amazon Professor of Machine Learning
Computer Science and Engineering

 

  • Machine Learning Foundations: A Case Study Approach
  • Fee: $59.00
 

If you are thinking of taking more than one course, the subscription option may be more beneficial.
After payment, you will receive a separate email, usually within one business day, that will include instructions on how to access the course.


- Single course access (generally 30 days with option to extend if needed)
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