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Applied Machine Learning in Python

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.

Created by University of Michigan


What you’ll learn

From this learning opportunity you will gain confidence with the compentencies sought by companies these days. The most in demand technique in the learning opportunity that is frequently mentioned from companies is Data Analysis. The most relevant tool is Python.

Who will benefit?

Evaluating the description from this educational opportunity with nearly 10,000 data-related job descriptions, we discover that those in or pursuing Data Scientist roles have the most to gain.