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
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
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
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
Graded: Quiz 4
Graded: Prediction Assignment Writeup
Graded: Course Project Prediction Quiz
FAQs
How It Works
Coursework
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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
Great course. Ways to curb plagiarism & cheating needs to be revisited by your team.
good context
A good course, but the field is so large and so important. You'll really need the "hacker" mentality to get through this course. They DO NOT teach you even close to everything you'll need to complete the course. It's also very stats/math heavy which will make the theory difficult. This isn't why I only rated 4 stars. I did so because of the lack of Swirl and the feeling that I still don't feel like I understand the topic well enough to do anything in a business setting yet. I was hoping for more from the class.
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AC
I enjoyed working on this course. There is a lot good and