University of Michigan

Applied Machine Learning in Python

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Course

Gain insight into a topic and learn the fundamentals

4.6

(8,482 reviews)

|

92%

Intermediate level
Some related experience required
31 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

What you'll learn

  • Describe how machine learning is different than descriptive statistics

  • Create and evaluate data clusters

  • Explain different approaches for creating predictive models

  • Build features that meet analysis needs

Details to know

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Assessments

4 quizzes

Taught in English

Course

Gain insight into a topic and learn the fundamentals

4.6

(8,482 reviews)

|

92%

Intermediate level
Some related experience required
31 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

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Build your subject-matter expertise

This course is part of the Applied Data Science with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.

What's included

7 videos4 readings1 quiz1 programming assignment1 ungraded lab

This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.

What's included

13 videos2 readings1 quiz1 programming assignment2 ungraded labs

This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.

What's included

8 videos2 readings1 quiz1 programming assignment1 ungraded lab

This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.

What's included

10 videos13 readings1 quiz1 programming assignment2 ungraded labs

Instructor

Instructor ratings
4.4 (863 ratings)
Kevyn Collins-Thompson
University of Michigan
3 Courses298,266 learners

Offered by

Recommended if you're interested in Data Analysis

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Taking this course by University of Michigan may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.

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Learner reviews

Showing 3 of 8482

4.6

8,482 reviews

  • 5 stars

    71.64%

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    21%

  • 3 stars

    4.91%

  • 2 stars

    1.17%

  • 1 star

    1.26%

MB
5

Reviewed on Jun 18, 2017

SA
4

Reviewed on Aug 14, 2019

AS
5

Reviewed on Nov 26, 2020

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