About this course: 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.
Created by: Johns Hopkins University
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Basic Info | Course 8 of 10 in the Data Science Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
9 videos, 3 readings
- Reading: Welcome to Practical Machine Learning
- Reading: Syllabus
- Reading: Pre-Course Survey
- Video: Prediction motivation
- Video: What is prediction?
- Video: Relative importance of steps
- Video: In and out of sample errors
- Video: Prediction study design
- Video: Types of errors
- Video: Receiver Operating Characteristic
- Video: Cross validation
- Video: What data should you use?
Graded: Quiz 1
WEEK 2
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
9 videos
- Video: Caret package
- Video: Data slicing
- Video: Training options
- Video: Plotting predictors
- Video: Basic preprocessing
- Video: Covariate creation
- Video: Preprocessing with principal components analysis
- Video: Predicting with Regression
- Video: Predicting with Regression Multiple Covariates
Graded: Quiz 2
WEEK 3
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.
5 videos
- Video: Predicting with trees
- Video: Bagging
- Video: Random Forests
- Video: Boosting
- Video: Model Based Prediction
Graded: Quiz 3
WEEK 4
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4 videos, 2 readings
- Video: Regularized regression
- Video: Combining predictors
- Video: Forecasting
- Video: Unsupervised Prediction
- Reading: Course Project Instructions (READ FIRST)
- Reading: Post-Course Survey
Graded: Quiz 4
Graded: Prediction Assignment Writeup
Graded: Course Project Prediction Quiz
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Creators
Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Very interesting course
Some problems with current and old versions of packages and problems with using other packages on different operating systems. Though that did also help foster an independent research style which will help me in the future.
A very good starter course on Machine Learning in R with great links to various resources that students and delve deeper into the various topics.
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RK
A great introduction to machine learning and it does a good job building on the material from the previous classes.