Applied Machine Learning in Python

Learn how to apply Machine Learning concepts fast using the Scikit-Learn library. The course provides clear explanations and challenging assignments. It is a perfect balance between theory and implementation.

Created by: Kevyn Collins-Thompson

Produced in 2016

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What you will learn

  • Methods and techniques of applied machine learning
  • Everyday application of machine learning, in credit cards fraud detection or showing ads
  • How to use Scikit-learn Python library
  • Understand data dimensionality and clustering algorithms
  • Classify different types of fruit using a k-nearest neighbor classifier
  • Optimize ML model's performance with evaluation and selection methods
  • Implement Naive Bayes Classifiers and Random Forests algorithm
  • Create predictive models to forecast outcomes in the future
  • Much, Much more!

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Course Description

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.

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Pros

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Cons

    • Practical application of Machine Learning with Scikit-Learn.
    • Good mixture of lectures, reading and practice.
    • Insights into key Machine Learning techniques and tools.
    • Well-organized content and stimulating assignments.
    • Covers numerous topics in a short time.
    • Lacks real world examples.

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Instructor Details

Kevyn Collins-Thompson

Kevyn Collins-Thompson is an Associate Professor of Information and Computer Science in the School of Information at the University of Michigan. He works on developing algorithms and systems for effectively connecting people with information, especially for educational goals. This involves bringing together methods from applied machine learning, human-computer interaction (HCI), and natural language processing. He also has more than a decade of industry experience as a software engineer, manager, and researcher.

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Reviews

4.9

109 total reviews

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