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Johns Hopkins University

Practical Machine Learning

Johns Hopkins University via Coursera


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.


  • Week 1: Prediction, Errors, and Cross Validation
    • This week will cover prediction, relative importance of steps, errors, and cross validation.
  • Week 2: The Caret Package
    • This week will introduce the caret package, tools for creating features and preprocessing.
  • 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.
  • Week 4: Regularized Regression and Combining Predictors
    • This week, we will cover regularized regression and combining predictors.

Taught by

Jeff Leek


3.4 rating, based on 27 Class Central reviews

4.5 rating at Coursera based on 3217 ratings

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  • Anonymous

    Anonymous completed this course.

    This course sucks. This is about machine learning. not about students learning. students don't learn anything with this course. apart from typing a one-liner code and pressing return. This was supposed to be the last course of their data analytics specialization...
  • Juan José D'Ambrosio

    Juan José D'Ambrosio completed this course, spending 5 hours a week on it and found the course difficulty to be medium.

    The name says everything, is just practical, none of the topics is treated in deep. And assumes that you have made almost all the other courses in the specialization.
    There is a pronounced down in the quality of the weeks, the week 1 is good enough, and the week 4 just sucks. And the professors seems being hurried up in the videos.
    However, can help as a very short introduction to a more in deep course.
  • Brandt Pence

    Brandt Pence completed this course, spending 3 hours a week on it and found the course difficulty to be easy.

    This is the second-to-last course in the Data Science specialization from Johns Hopkins, and the final of three courses covering actual data analysis techniques (preceded by Statistical Inference and Regression Models). This was one of the better courses...
  • Jason Michael Cherry completed this course, spending 2 hours a week on it and found the course difficulty to be medium.

    Of all the JHU Data Science specialization courses I've had, this was by far the most enjoyable. I really liked how the class was more in the style of 'here's some techniques, now do whatever you want on the project.' Prior courses are, and understandably...
  • Anonymous

    Anonymous is taking this course right now.

    This course, and the entire specialization is very poorly constructed and taught. You don't really understand the concepts behind the code you are writing, and how to go through a data science project. Its probably the worst course and specialization for data science out there!

    The ONLY reason why you might want to take this is if you already know ML and statistics extremely well, and just want to brush up on your skills. The R programming course was helpful for me for this purpose because I had already used R a few years ago and only wanted a refresher. Otherwise, don't expect to learn much and you would rather get confused rather than learn anything valuable.
  • L K B

    L K B completed this course, spending 7 hours a week on it and found the course difficulty to be hard.

    This is good introduction to ML. The course demonstrate the practical application of ML, but due to short duration, it does not explain concepts in depth and it does glance over more complex parameters.
    If you like to learn how to programme ML in R, have good experience with statistics and programming, and are happy with doing additional studies, I would recommend this course. For more in-depth knowledge I recommend Andrew Ng courese.
  • Ziem Beyuore
    Machine learning is a very useful and problem solving program in the our current world and it can be apply to all filed of life be it health, marketing, statistical data collection.
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    Prashant Bhalsingh

    Prashant Bhalsingh is taking this course right now.

    compare to some other course for deep learning i have taken this course really sucks.Does not give any understanding of deep network . Trainer seems to be quite dull in explaining concepts...
  • Anonymous

    Anonymous completed this course.

    I found this course very valuable. It isn't realistic to expect to become an expert in machine learning in the 4-5 days you might spend on studying the materials and, if you do, you'll be disappointed. However, it is a pretty good practical introductory...
  • Profile image for Loki Riya
    Loki Riya

    Loki Riya is taking this course right now, spending 20 hours a week on it and found the course difficulty to be easy.

    This course is very useful one for me.Thanks for giving such a opportunity to me.Please try to teach us more advanced topics like this in future.
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    Alex Ivanov

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    Radomir Nowacki

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