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
Access provided by New York State Department of Labor
155,921 already enrolled
Gain insight into a topic and learn the fundamentals.
4.5
(3,255 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
- Classification And Regression Tree (CART)
- Data Collection
- Machine Learning
- Random Forest Algorithm
- Regression Analysis
- Feature Engineering
- Statistical Machine Learning
- Predictive Modeling
- Data Processing
- Supervised Learning
- Applied Machine Learning
- Machine Learning Algorithms
- R Programming
- Decision Tree Learning
Details to know
Shareable certificate
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Assessments
5 assignments
Taught in English
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This course is available as part of
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- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this course
This week will cover prediction, relative importance of steps, errors, and cross validation.
What's included
9 videos4 readings1 assignment
This week will introduce the caret package, tools for creating features and preprocessing.
What's included
9 videos1 assignment
This week we introduce a number of machine learning algorithms you can use to complete your course project.
What's included
5 videos1 assignment
This week, we will cover regularized regression and combining predictors.
What's included
4 videos2 readings2 assignments1 peer review
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Instructors
Instructor ratings
4.5 (135 ratings)Offered by
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4.5
3,255 reviews
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Showing 3 of 3255
UY
4
Reviewed on Nov 17, 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.
DH
5
Reviewed on Jun 18, 2018
Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.
EG
4
Reviewed on Jul 28, 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.
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