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Applied Machine Learning in Python

University of Michigan via Coursera

4 Reviews 397 students interested
  • Provider Coursera
  • Subject Machine Learning
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 4 weeks long
  • Learn more about MOOCs

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Overview

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This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

Taught by

Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero and V. G. Vinod Vydiswaran

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Reviews for Coursera's Applied Machine Learning in Python
3.5 Based on 4 reviews

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  • 1
Raivis J
3.0 8 months ago
Raivis completed this course, spending 14 hours a week on it and found the course difficulty to be medium.
This is an interesting course, but is somewhat lacking practical example to illustrate theoretical concepts. Topics like model evaluation and tuning parameters would have benefited if those are explained using real or semi-real life problem examples. Especially the quizzes needed more context as to why a particular situation might occur, and why that particular variable of interest is necessary. The feature selection / engineering aspect was just barely touched upon.
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Anonymous
2.0 2 years ago
Anonymous completed this course.
Slow prof. Make you go sleep.

Could hardly tell where he is going. There is not continuity to his explanations.

Almost makes me think and question if he know the stuff he is talking about.
Was this review helpful to you? Yes
Wichaiditsornpon@gmail.com W
4.0 a year ago
by Wichaiditsornpon@gmail.com completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
this course is well and cover a lot and very practice assignment but for each week cover too much of detail that make us hard to focus with it would better if this course split into something like 6-8 week, and a bit slow if you already know ml
Was this review helpful to you? Yes
Anonymous
5.0 2 years ago
Anonymous completed this course.
Interesting course, similar to Andrew Ng's machine learning course, but covers a slightly different spectrum of topics, and skips things like inner workings of gradient descent in order to have more of a focus on practical aspects of sklearn and python.
Was this review helpful to you? Yes
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