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 UC Berkeley Learn2Launch
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3,267 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
- Classification And Regression Tree (CART)
- Model Training
- Feature Engineering
- Machine Learning Software
- Random Forest Algorithm
- Applied Machine Learning
- Predictive Modeling
- Machine Learning Algorithms
- Machine Learning Methods
- Regression Analysis
- Machine Learning
- Model Evaluation
- Data Preprocessing
- Predictive Analytics
- Supervised Learning
Tools you'll learn
Details to know
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Assessments
5 assignments
Taught in English
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Showing 3 of 3267
GA
Reviewed on Feb 21, 2018
A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.
AD
Reviewed on Feb 28, 2017
Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.
JP
Reviewed on Jun 24, 2017
Awesome course. Would recommend it, but only to those who have a bit of stats and R background. This definitely helped me get a solid enough understanding of using R for machine learning.
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