Practical Machine Learning
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assignment Level : Introductive
chat_bubble_outline Language : English
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Users' reviews
3.6
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134 reviews

Key information

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About the content

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.

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Syllabus

  • Week 1 - Week 1: Prediction, Errors, and Cross Validation
    This week will cover prediction, relative importance of steps, errors, and cross validation.
  • Week 2 - Week 2: The Caret Package
    This week will introduce the caret package, tools for creating features and preprocessing.
  • Week 3 - 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 - Week 4: Regularized Regression and Combining Predictors
    This week, we will cover regularized regression and combining predictors.
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Instructors

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

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Content designer

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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Platform

Coursera

Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California. 

Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.

Reviews
3.6 /5 Average
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Best review

A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.

Published on February 22, 2018
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on the February 22, 2018
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A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.

on the February 21, 2018
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Unsatisfactory and poor course in this specialisation. There are many important parts which are explained inaccurately. In many cases, the lecturer jumps from important points, or assumes students have detailed knowledge about the topic. You can find ambiguity in weekly questions. Very unsatisfied!

on the February 13, 2018
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I was rather disappointed with this course. I guess it fills the objective of getting you using the caret package and getting you started with some examples. However to understand what you are doing you should defintively go somewhere else. I definitively missed some swirl exercises and more flow diagrams in the slides. It felt for me as I was just copypasting some code from the slides. The course does clearly give some good literature and places to go for details.

on the February 12, 2018
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Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.

on the February 4, 2018
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The practical machine learning course is a booster for the data science aspirant.The concept taught by the Prof Jeff Leek is easily understandable. Thank you so much Sir.