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
Practical Machine Learning
This course is part of multiple programs.
Instructors: Jeff Leek, PhD +2 more
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3,267 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: Supervised Learning
- Category: Model Training
- Category: Regression Analysis
- Category: Predictive Modeling
- Category: Feature Engineering
- Category: Applied Machine Learning
- Category: Random Forest Algorithm
- Category: Predictive Analytics
- Category: Data Preprocessing
- Category: Classification And Regression Tree (CART)
- Category: Machine Learning
- Category: Model Evaluation
- Category: Machine Learning Methods
- Category: Machine Learning Algorithms
- Category: Machine Learning Software
Tools you'll learn
- Category: R Programming
- Category: Classification Algorithms
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 Courses1,756,161 learners
Offered by
Johns Hopkins University
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Showing 3 of 3267
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.
EG
Reviewed on Jul 27, 2016
I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.
MR
Reviewed on Aug 13, 2020
recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course