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.
Offered By
Johns Hopkins UniversityAbout this Course
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Shareable Certificate
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Approx. 9 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
What you will learn
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Skills you will gain
- Random Forest
- Machine Learning (ML) Algorithms
- Machine Learning
- R Programming
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 9 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Offered by
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 - 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
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)
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)
3 hours to complete
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
3 hours to complete
4 videos (Total 33 min), 2 readings, 3 quizzes
Reviews
- 5 stars66.44%
- 4 stars22.36%
- 3 stars6.93%
- 2 stars2.49%
- 1 star1.74%
TOP REVIEWS FROM PRACTICAL MACHINE LEARNING
by NKFeb 18, 2016
Some of the terms used here vary from the terms used in the industry. For example recall, precision etc. Overall this is a very good course with provides basics of machine learning.
by EGJul 27, 2016
I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.
by ASAug 30, 2017
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.
by PLSep 19, 2018
Excelent course, it's a little bit short considering the breadth of the topic, but covers the most important algorithms and never abandon it's focus on methodology.
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