Course Summary
This course teaches practical skills in machine learning, with a focus on real-world applications. Students will learn how to prepare data, choose appropriate algorithms, and interpret results.Key Learning Points
- Emphasis on practical skills for machine learning
- Hands-on projects and real-world applications
- Flexible schedule and self-paced learning
Related Topics for further study
Learning Outcomes
- Ability to prepare data for machine learning algorithms
- Skill in choosing appropriate algorithms for a given problem
- Experience interpreting results and applying them to real-world scenarios
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge in Python
- Familiarity with statistics and linear algebra
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Project-based
- Online
Similar Courses
- Applied Data Science with Python
- Machine Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
Related Books
Description
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.
Knowledge
- 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
Outline
- Week 1: Prediction, Errors, and Cross Validation
- Prediction motivation
- What is prediction?
- Relative importance of steps
- In and out of sample errors
- Prediction study design
- Types of errors
- Receiver Operating Characteristic
- Cross validation
- What data should you use?
- Welcome to Practical Machine Learning
- A Note of Explanation
- Syllabus
- Pre-Course Survey
- Quiz 1
- Week 2: The Caret Package
- Caret package
- Data slicing
- Training options
- Plotting predictors
- Basic preprocessing
- Covariate creation
- Preprocessing with principal components analysis
- Predicting with Regression
- Predicting with Regression Multiple Covariates
- Quiz 2
- Week 3: Predicting with trees, Random Forests, & Model Based Predictions
- Predicting with trees
- Bagging
- Random Forests
- Boosting
- Model Based Prediction
- Quiz 3
- Week 4: Regularized Regression and Combining Predictors
- Regularized regression
- Combining predictors
- Forecasting
- Unsupervised Prediction
- Course Project Instructions (READ FIRST)
- Post-Course Survey
- Quiz 4
- Course Project Prediction Quiz
Summary of User Reviews
Learn practical machine learning techniques in this course. Students have found it easy to understand and implement. Many users appreciate the real-world examples provided in the course.Pros from User Reviews
- Easy to understand and implement
- Real-world examples provided
- Instructors are knowledgeable and engaging
- Hands-on projects help solidify understanding
Cons from User Reviews
- Some sections may be too basic for experienced learners
- Course content may not be up-to-date with the latest techniques
- Limited interactions with instructors and other students
Jeff Leek, PhD
4.5 Raiting
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