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.
This course is part of the Data Science Specialization
Practical Machine Learning
Offered By
About this Course
65,416 recent views
Learners taking this Course are
- Risk Managers
- Data Scientists
- Biostatisticians
- Data Analysts
- Biologists
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
Syllabus - What you will learn from this course
Week
12 hours to complete
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
9 videos (Total 73 min), 3 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
3 readings
Welcome to Practical Machine Learning10m
Syllabus10m
Pre-Course Survey10m
1 practice exercise
Quiz 110m
Week
22 hours to complete
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
9 videos (Total 96 min), 1 quiz
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
Week
31 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.
5 videos (Total 48 min), 1 quiz
1 practice exercise
Quiz 310m
Week
44 hours to complete
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
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
492 Reviews40%
started a new career after completing these courses
39%
got a tangible career benefit from this course
13%
got a pay increase or promotion
Top 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.
Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.
Instructors
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
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.
About the Data Science Specialization
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.