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
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8 hours to complete
Flexible schedule
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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
- Supervised Learning
- Model Training
- Regression Analysis
- Machine Learning
- Predictive Analytics
- Predictive Modeling
- Model Evaluation
- Machine Learning Algorithms
- Classification And Regression Tree (CART)
- Machine Learning Methods
- Data Preprocessing
- Machine Learning Software
- Feature Engineering
- Random Forest Algorithm
Tools you'll learn
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Assessments
5 assignments
Taught in English
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Status: Free TrialUniversity of Glasgow
Status: PreviewThe University of Chicago
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