Predictive Analytics using Machine Learning

Learn how to build predictive models using machine learning.

Predictive Analytics using Machine Learning

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After a course session ends, it will be archived.
Estimated 6 weeks
8–10 hours per week
Instructor-paced
Instructor-led on a course schedule

About this course

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This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the basis of cutting-edge analytics tools that are used for image classification, text and sentiment analysis, and more.

The course contains two case studies: forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions.

You will also learn:

  • Sampling techniques such as bagging and boosting, which improve robustness and overall predictive power, as well as random forests
  • Support vector machines by introducing you to the concept of optimising the separation between classes, before diving into support vector regression
  • Neural networks; their topology, the concepts of weights, biases, and kernels, and optimisation techniques

At a glance

  • Institution: EdinburghX
  • Subject: Data Analysis & Statistics
  • Level: Advanced
  • Prerequisites:

    You should be familiar with an undergraduate level, or have a background, in mathematics and statistics. Previous experience with a procedural programming language is beneficial (e.g. Python, C, Java, Visual Basic).

    Learners pursuing the MicroMastersprogramme are strongly recommended to complete PA1.1x Introduction to Predictive Analytics using Python and PA1.2x Successfully Evaluating Predictive Modelling and PA1.3x Statistical Predictive Modelling and Applications on the verified track prior to undertaking this course.

What you'll learn

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In this course, you will:

  • Understand the difference between machine learning and other statistical models
  • Practice building tree-based models, support vector machines and neural networks
  • Implement the theoretic models in machine learning-based software packages in Python
  • Apply machine learning models to business situations

Week 1: Decision trees
Week 2: Random forests and support vector machines
Week 3: Support vector machines
Week 4: Neural networks
Week 5: Neural network estimation and pitfalls
Week 6: Model comparison

About the instructors

Frequently Asked Questions

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What type of activities will I complete on the course?
This course foregrounds self-directed and active ways of learning: reading, coding in Python, knowledge check quizzes,and peer discussion. In addition, the course features videos that demonstrate relevant predictive analysis techniques and concepts.

What software will I be required to use?
All coding activities on thiscourse will be hosted onVocareum. You will be able to access this free software directlywithinthe edX platform. There is no requirement to purchase further software in order to complete this course.

What do I need to complete the course?
For successful completion of this course, you will need access to a computer ormobiledevice anda reliableinternet connection.

What is the University of Edinburgh Accessibility Guidance?

The University of Edinburgh is committed to providing online information and services accessible to all. Edx provide an accessibility statement which is available via the footer of all edx.org pages and includes an 'Accessibility Feedback' form which allows Learners to register feedback directly with the edx. Courses created by the University of Edinburgh contain an Accessibility Statement which addresses equality of access to information and servicesandis available via the 'Support' page.

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.