Course Description
In this course, you will learn the basics of classification modeling using machine learning. You will examine the typical classification model pipeline and the applicable problems classification can be applied to. You will also learn how to collect, preprocess, and analyze the data needed to create a viable classification model.
The most common classification models will be explained and tradeoffs between models discussed, including practical tips for their implementation. This course also introduces neural networks and their applications within classification models. Common challenges will be discussed so that you can ensure the models you create are fair, trustworthy, and robust. Finally, you will have the chance to review several case studies of classification models so you can see how they work in detail. By the end of this course, you should be able to design, analyze, validate, and implement a classification model, as well as understand the implications of its use.
What You'll Learn
- Identify how and where classification models apply to real-world problems
- Compare classification algorithms, including their strengths, weaknesses, and applicable scenarios
- Choose the best classification algorithm for a specific problem given the tradeoffs between models
- Collect, preprocess, and analyze the data needed to build a viable classification model
- Analyze, validate, and implement the results of a classification model
- Examine case studies of classification models that have been implemented
Key Takeaways
- The course covers the typical classification model pipeline, from data collection and preprocessing to model building, feature engineering, and validation.
- It explains the most common classification models and discusses the tradeoffs between them along with practical implementation tips.
- Neural networks and their applications within classification models are introduced, including image classification and convolutional neural networks.
- Common challenges such as explainability, bias and fairness, and adversarial attacks are discussed to help build fair, trustworthy, and robust models.
- Case studies, including customer behavior, medicine and health care, and image classification, show how classification models work in detail.
Frequently Asked Questions
What will I be able to do after completing this course?
By the end of the course, you should be able to design, analyze, validate, and implement a classification model, as well as understand the implications of its use.
Which classification models does this course cover?
The course covers common classification models including regression, Naive Bayes, support vector machines, decision trees, random forest models, gradient boosting, k-nearest neighbours, multiclass models, and perceptrons and neural networks.
Does this course cover neural networks?
Yes. It introduces neural networks and their applications within classification models, including image classification and convolutional neural networks.
What challenges in classification modeling does the course address?
It discusses common challenges such as data collection problems, explainability, bias and fairness, adversarial attacks, and emerging challenges so you can ensure your models are fair, trustworthy, and robust.
Are there real-world examples in this course?
Yes. The course includes case studies of implemented classification models, covering customer behavior, medicine and health care, and image classification.
Professional Certifications and Continuing Education Units (CEUs)
Continuing Development Units (CDUs): 2.0
KnowledgeCity is an endorsed education approved provider for the International Institute of Business Analysis™ (IIBA®) for CCBA®, CBAP®. KnowledgeCity courses can assist learners with their continuing development units (CDUs).
