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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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
- Data Preprocessing
- Model Training
- Random Forest Algorithm
- Machine Learning Methods
- Machine Learning
- Applied Machine Learning
- Regression Analysis
- Machine Learning Algorithms
- Predictive Modeling
- Supervised Learning
- Classification And Regression Tree (CART)
- Predictive Analytics
- Feature Engineering
- Machine Learning Software
- Model Evaluation
Tools you'll learn
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Assessments
5 assignments
Taught in English
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There are 4 modules in this course
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(139 ratings)Offered by
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University of Glasgow
The University of Chicago
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Showing 3 of 3267
HP
Reviewed on Jan 15, 2017
It was like opening up a door to a whole new world. I have discovered new tools that I will thoroughly enjoy to use for the exploration of data and for predictions. Thanks Team Coursera !
NK
Reviewed on Feb 18, 2016
Some of the terms used here vary from the terms used in the industry. For example recall, precision etc. Overall this is a very good course with provides basics of 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.
