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 ARS SCINet/AI-COE
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Gain insight into a topic and learn the fundamentals.
3,268 reviews
8 hours to complete
Flexible schedule
Learn at your own pace
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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
- Applied Machine Learning
- Regression Analysis
- Predictive Analytics
- Classification And Regression Tree (CART)
- Random Forest Algorithm
- Supervised Learning
- Machine Learning Algorithms
- Machine Learning
- Model Training
- Machine Learning Methods
- Data Preprocessing
- Feature Engineering
- Machine Learning Software
- Model Evaluation
- Predictive Modeling
Tools you'll learn
Details to know
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Assessments
5 assignments
Taught in English
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Showing 3 of 3268
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.