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Course Description
Machine learning is one of the most exciting branches of data science. It can be used in a wide variety of contexts to solve many different problems, including classifying images, detecting spam, labelling images, segmenting customers, and making predictions. From neural networks to forecasting to dimension reduction, there are many methods of machine learning that are typically in a practitioner’s toolkit.
This course provides an overview of machine learning and its application to many different business problems. We’ll explain the types of machine learning algorithms and what problems they can be applied to, including supervised machine learning, semi-supervised machine learning, and unsupervised machine learning. You will learn how to assess the effectiveness of a machine-learning model and best practices to make decisions between data-collection methods, preprocessing steps, and algorithms. You’ll also discover what critical factors to consider to make a project successful. Ethical considerations, including privacy, bias, and fairness, will also be addressed. You’ll also explore several real-world examples of machine learning applications.
What You'll Learn
- Identify the data needed to build an effective machine-learning model
- Assess the quality and effectiveness of a machine-learning model using validation and quality metrics
- Choose the right model and data for a given problem across supervised, unsupervised, and semi-supervised approaches
- Explain the trade-offs between models, methods, and data-collection strategies
- Apply machine learning to real-world problems such as spam detection, customer segmentation, and image classification
- Describe ethical principles in machine learning, including privacy, bias, and fairness
Key Takeaways
- Machine learning can be applied to many business problems, including classifying images, detecting spam, labelling images, segmenting customers, and making predictions.
- The course covers three categories of algorithms: supervised, semi-supervised, and unsupervised machine learning.
- Building a successful project requires decisions about data-collection methods, preprocessing steps, and algorithm selection, along with critical success factors.
- Methods covered range from neural networks and forecasting to dimension reduction, clustering, feature engineering, bagging, and boosting.
- Ethical considerations such as privacy, bias, and fairness are an essential part of applied machine learning.
Frequently Asked Questions
What topics does this course cover?
It provides an overview of machine learning and its application to business problems, covering the types of algorithms (supervised, semi-supervised, and unsupervised), methods such as neural networks, forecasting, and dimension reduction, how to assess a model's effectiveness, how to choose between data-collection methods, preprocessing steps, and algorithms, and ethical considerations including privacy, bias, and fairness.
What machine-learning methods will I learn about?
The course covers methods including artificial neural networks, classification, prediction, forecasting, feature engineering, clustering, dimension reduction, bagging, and boosting, as well as the use of pre-trained models.
What real-world applications are included?
The course explores several real-world examples, including spam detection, customer segmentation, and image classification.
Does this course address ethics in machine learning?
Yes. Ethical considerations, including privacy, bias, and fairness, are addressed, and the course teaches you to describe ethical principles in machine learning.
How do I evaluate whether a machine-learning model is good?
The course teaches you to assess the quality of a machine-learning model, covering model validation, model quality metrics, common challenges, and explainability.
