Practical Machine Learning

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 4.4 stars Average User Rating 4.4See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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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.

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 expand

1. Reading: Welcome to Practical Machine Learning
2. Reading: Syllabus
3. Reading: Pre-Course Survey
4. Video: Prediction motivation
5. Video: What is prediction?
6. Video: Relative importance of steps
7. Video: In and out of sample errors
8. Video: Prediction study design
9. Video: Types of errors
10. Video: Receiver Operating Characteristic
11. Video: Cross validation
12. 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 expand

1. Video: Caret package
2. Video: Data slicing
3. Video: Training options
4. Video: Plotting predictors
5. Video: Basic preprocessing
6. Video: Covariate creation
7. Video: Preprocessing with principal components analysis
8. Video: Predicting with Regression
9. 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 expand

1. Video: Predicting with trees
2. Video: Bagging
3. Video: Random Forests
4. Video: Boosting
5. 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 expand

1. Video: Regularized regression
2. Video: Combining predictors
3. Video: Forecasting
4. Video: Unsupervised Prediction
5. Reading: Course Project Instructions (READ FIRST)
6. Reading: Post-Course Survey

Graded: Quiz 4
Graded: Prediction Assignment Writeup
Graded: Course Project Prediction Quiz