About this courseSkip About this course
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
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.
- Language: English
- Associated programs:
- MicroMasters® Program in Predictive Analytics using Python
What you'll learnSkip What you'll learn
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 QuestionsSkip Frequently Asked Questions
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.