John Hopkins University - Practical Machine Learning
- Offered byCoursera
Practical Machine Learning at Coursera Overview
Duration | 4 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Practical Machine Learning at Coursera Highlights
- Earn a Certificate of completion from Johns Hopkins University on successful course completion
- Instructors - Roger D. Peng, Jeff Leek, and Brian Caffo
- Shareable Certificates
- Self-Paced Learning Option
Practical Machine Learning at Coursera Course details
- The course is desigend for those who want to learn the basic of machine learning.
- 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 at Coursera Curriculum
Week 1: Prediction, Errors, and Cross Validation - This week will cover prediction, relative importance of steps, 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?
Week 2: The Caret Package - This week will introduce the caret package, tools for creating features and preprocessing.
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
Week 3: Predicting with trees, Random Forests, & Model Based Predictions - This week we introduce a number of machine learning algorithms you can use to complete your course project.
Predicting with trees
Bagging
Random Forests
Boosting
Model Based Prediction
Week 4: Regularized Regression and Combining Predictors - This week, we will cover regularized regression and combining predictors.
Regularized regression
Combining predictors
Forecasting
Unsupervised Prediction
Practical Machine Learning at Coursera Admission Process
Important Dates
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