Applied Machine Learning in Python, Certificate | Part time online | Coursera | United States
2 days
Duration
Free
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Tuition fee
Anytime
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About

This  Applied Machine Learning in Python course is offered by Coursera in partnership with University of Michigan will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.

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Overview

The Applied Machine Learning in Python course is offered by Coursera in partnership with University of Michigan 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.

What Will You Learn

  • Describe how machine learning is different than descriptive statistics
  • Create and evaluate data clusters
  • Explain different approaches for creating predictive models
  • Build features that meet analysis needs

Programme Structure

Courses included:

  • Machine Learning - SciKit Learn
  • Supervised Machine Learning 
  • Evaluation
  • Supervised Machine Learning

Key information

Duration

  • Part-time
    • 2 days

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

  • Intermediate level
  • Some related experience required

Tuition Fee

To always see correct tuition fees
  • International

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 2 days.
  • National

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 2 days.

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Funding

Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.

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