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Data Science - Regression, Classification and Clustering Models

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  • Description
  • Outcome
  • Certification
  • In this free online course Data Science - Regression, Classification and Clustering Models you will learn about three different types of models: regression models, classification models and clustering models. You will also learn how each of these models can be created in Azure ML, R and Python.

    The course begins by introducing you to regression models. You will learn about what regression modelling is and about the steps you can take to improve your models. The course teaches you about cross-validation and how it can help you with your data. You will learn about using Azure ML's built-in modules sweep parameters and permutation features.

    Next, you will learn about classification models. You can use many of same Azure ML built-in modules for classification models that you can use in regression modelling. You will also learn about the metrics for evaluating a classification model's performance, and about creating a support vector machine model and a two-class decision forest model.

    Finally, the course teaches you about unsupervised learning models. You will learn how different clustering method work and about how to evaluate cluster models. You will learn about cluster model's K-means and hierarchical clustering. You will learn about creating clustering models in Python and R.

    This free Alison course will be of great interest to learners who wish to expand their knowledge about data science and the use of regression, classification and clustering models.

    Perquisites: To complete this course successfully you need a basic knowledge of mathematics, including linear algebra. Additionally, some programming experience, ideally in either R or Python, is assumed and you will need to have completed the previous courses 'Introduction to Data Science', 'Data Science - Working with Data', and 'Data Science - Visualizing Data and Exploring Models'.

  • Having completed this course you will be able to:
    - Discuss the process of regression modelling and how to improve the model.
    - Describe how to refine a regression model with R.
    - Explain how to refine a regression model with Python.
    - Discuss the process of classification modelling and how to improve the model.
    - List the metrics for evaluating a classification models performance.
    - Describe how to create a support vector machine model and a decision forest model.
    - Discuss the process of creating unsupervised learning models.
    - Explain how to create hierarchical and k-means clustering models in R.
    - Explain how to create hierarchical and k-means clustering models in Python.

  • All Alison courses are free to study. To successfully complete a course you must score 80% or higher in each course assessments. Upon successful completion of a course, you can choose to make your achievement formal by purchasing an official Alison Diploma, Certificate or PDF.

    Having an official Alison document is a great way to share your success. Plus it’s:

    • Ideal for including in CVs, job applications and portfolios
    • An indication of your ability to learn and achieve high results
    • An incentive to continue to empower yourself through learning
    • A tangible way of supporting the Alison mission to empower people everywhere through education.

Modules List( 4 )
  • Data Science - Regression, Classification and Clus...
  • Data Science - Regression, Classification and Clustering Models - Course Resource Files
  • Module 1: Regression Modelling
    • Learning Outcomes
    • Regression Recap
    • Refining a Regression Model with R
    • Refining a Regression Model with Python
    • Lesson Summary
  • Module 2: Classification Modelling
    • Learning Outcomes
    • Understanding Classification
    • Preparing Data for Classification Using R
    • Preparing Data for Classification Using Python
    • Creating a Support Vector Machine Model and Decision Forest Model
    • Evaluating Classification Model
    • Lesson Summary
  • Module 3: Unsupervised Learning Models
    • Learning Outcomes
    • Unsupervised Models and Building K-Means Clustering
    • Exploring and Creating Clustering Models with R
    • Exploring and Creating Clustering Models with Python
    • Lesson Summary
  • END OF COURSE ASSESSMENT
  • Module 4: Data Science - Regression, Classification and Clustering Models Assessment
    • Data Science - Regression, Classification and Clustering Models Assessment
Topics List ( 5 )
Module 1: Regression Modelling
In this module you will be introduced to regression. You will learn about cross-validation and about using Azure ML's built-in modules sweep parameters, and permutation feature.
Topics List ( 7 )
Module 2: Classification Modelling
In this module you will learn about classification modelling. You can use many of same Azure ML built-in modules for classification models that you can use in regression modelling. You will learn about the metrics for evaluating a classification model's performance. You will learn about creating a Support vector machine model and a two-class decision forest model.
Topics List ( 5 )
Module 3: Unsupervised Learning Models
In this module you will learn about unsupervised learning models. You will learn about cluster models K-means and hierarchical clustering. You will learn about how each clustering method works and how to evaluate cluster models. You will learn about creating clustering models in Python and R.
Topics List ( 1 )
Module 4: Data Science - Regression, Classification and Clustering Models Assessment
You must score 80% or more to pass this assessment.
Course Features
  • Duration

    2-3 Hours

  • Publisher

    Channel 9

  • Video

    Yes

  • Audio

    Yes

  • Assessment

    Yes

  • Certification

    Yes

  • Price

    Free

  • Reward

    50 Pts

  • Responsive

    No

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