One of the most common tasks performed by data scientists and data analysts are 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 course 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.
Practical Machine Learning
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Practical Machine Learning
This course is part of multiple programs.
Instructors: Jeff Leek, PhD +2 more
158,199 already enrolled
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3,269 reviews
8 hours to complete
Flexible schedule
Learn at your own pace
89%
Most learners liked this course
What you'll 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
Skills you'll gain
- Category: Predictive Modeling
- Category: Model Evaluation
- Category: Machine Learning
- Category: Supervised Learning
- Category: Machine Learning Methods
- Category: Machine Learning Software
- Category: Model Training
- Category: Data Preprocessing
- Category: Feature Engineering
- Category: Machine Learning Algorithms
- Category: Applied Machine Learning
- Category: Classification And Regression Tree (CART)
- Category: Predictive Analytics
- Category: Regression Analysis
- Category: Random Forest Algorithm
Tools you'll learn
- Category: Classification Algorithms
- Category: R Programming
Details to know
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Assessments
5 assignments
Taught in English
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Instructors
Instructor ratings
(139 ratings)Jeff Leek, PhD
Johns Hopkins University
32 Courses1,764,842 learners
Offered by
Johns Hopkins University
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Showing 3 of 3269
AC
Reviewed on May 31, 2021
A well descriptive experience for this subject; really steps into how to handle information and how to extract info from them. You need to be prepared with Regression Models, it's the base of it.
FF
Reviewed on Jul 8, 2016
Great primer for machine learning with ample additional resources for those who are interested. I feel this course gave me a solid basis to delve deeper into the topic.
UY
Reviewed on Nov 16, 2016
Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.