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

  • Jeff Leek, PhD
    Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics
    Bloomberg School of Public Health

  • Roger D. Peng, PhD
    Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics
    Bloomberg School of Public Health

  • Brian Caffo, PhD
    Taught by:  Brian Caffo, PhD, Professor, Biostatistics
    Bloomberg School of Public Health
Basic Info
Course 8 of 10 in the Data Science Specialization.
How To PassPass all graded assignments to complete the course.
User Ratings
Average User Rating 4.4See what learners said
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation. 
9 videos3 readings
  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: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing. 
9 videos
  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: 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
  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: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.  
4 videos2 readings
  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
How It Works
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
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.
Learn more about this course

No comments:

Post a Comment