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
Offered By
Johns Hopkins University
About this Course
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Learner Career Outcomes
38%
started a new career after completing these courses
38%
got a tangible career benefit from this course
12%
got a pay increase or promotion
What you will learn
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Use the basic components of building and applying prediction functions
Skills you will gain
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
Learner Career Outcomes
38%
started a new career after completing these courses
38%
got a tangible career benefit from this course
12%
got a pay increase or promotion
100% online
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Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 14 hours to complete
English
Subtitles: English, Korean
Syllabus - What you will learn from this course
2 hours to complete
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
2 hours to complete
9 videos (Total 73 min), 4 readings, 1 quiz
9 videos
What is prediction?8m
Relative importance of steps9m
In and out of sample errors6m
Prediction study design9m
Types of errors10m
Receiver Operating Characteristic5m
Cross validation8m
What data should you use?6m
4 readings
Welcome to Practical Machine Learning10m
A Note of Explanation2m
Syllabus10m
Pre-Course Survey10m
1 practice exercise
Quiz 110m
2 hours to complete
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
2 hours to complete
9 videos (Total 96 min)
9 videos
Data slicing5m
Training options7m
Plotting predictors10m
Basic preprocessing10m
Covariate creation17m
Preprocessing with principal components analysis14m
Predicting with Regression12m
Predicting with Regression Multiple Covariates11m
1 practice exercise
Quiz 210m
1 hour to complete
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.
1 hour to complete
5 videos (Total 48 min)
1 practice exercise
Quiz 310m
4 hours to complete
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4 hours to complete
4 videos (Total 33 min), 2 readings, 3 quizzes
2 readings
Course Project Instructions (READ FIRST)10m
Post-Course Survey10m
2 practice exercises
Quiz 410m
Course Project Prediction Quiz40m
4.5
519 ReviewsTop reviews from Practical Machine Learning
Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.
Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.
About 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.
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