Practical Machine Learning course Coursera
Practical Machine Learning (Coursera)

Practical Machine Learning (Coursera)

Practical Machine Learning is a free online MOOC Course, Offered by Johns Hopkins University via Coursera. This course is part of multiple programs.

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

  • Data Science: Statistics and Machine Learning Specialization
  • Data Science Specialization

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Overview

One of the most common tasks performed by data scientists and data analysts is prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.

The Practical Machine Learning course by Coursera will also introduce a range of model-based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

WHAT YOU WILL LEARN

  • Use the basic components of building and applying prediction functions
  • Understand concepts such as training and tests sets, overfitting, and error rates
  • Describe machine learning methods such as regression or classification trees
  • Explain the complete process of building prediction functions

Syllabus

Week 1: Prediction, Errors, and Cross-Validation

This week will cover prediction, the relative importance of steps, errors, and cross-validation.

Week 2: The Caret Package

This week will introduce the caret package, tools for creating features, and preprocessing.

Week 3: Predicting with trees, Random Forests, & Model-Based Predictions

This week we introduce a number of machine learning algorithms you can use to complete your course project.

Week 4: Regularized Regression and Combining Predictors

This week, we will cover regularized regression and combining predictors.

Teacher

  • Jeff Leek

Additional information

Course Delivery

Online

Course Efforts

8 Hours

Course Enrollment

Free

Course Language

English

Course Length

4 Weeks

Course Level

Mixed

Course Provider

Course School

Course Subtitles

Arabic, English, French, German, Italian, Korean, Portuguese, Russian, Spanish

Flexible Learning

Yes

Verified Certificate

Paid

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