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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Learner Career Outcomes
37%
started a new career after completing these courses
37%
got a tangible career benefit from this course
12%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Flexible deadlines
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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 ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
Learner Career Outcomes
37%
started a new career after completing these courses
37%
got a tangible career benefit from this course
12%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your 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
TOP REVIEWS FROM PRACTICAL MACHINE LEARNING
by MRAug 13, 2020
recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course
by ADFeb 28, 2017
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.
by DHJun 17, 2018
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.
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.
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