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 Upwardly Global
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Gain insight into a topic and learn the fundamentals.
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
- Machine Learning Methods
- Data Preprocessing
- Model Training
- Machine Learning Algorithms
- Predictive Modeling
- Machine Learning
- Applied Machine Learning
- Predictive Analytics
- Supervised Learning
- Random Forest Algorithm
- Model Evaluation
- Classification And Regression Tree (CART)
- Machine Learning Software
- Feature Engineering
- Regression Analysis
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
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.
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.
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.
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