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Course Description
Supervised machine learning is used in many every day applications, including image captioning, forecasting, and machine translation. But, because it’s so widely applicable, there are many different methods that can be used on different kinds of data to solve various styles of classification and prediction. These include regression, neural networks, and decision trees.
All machine learning heavily relies on data. In these lessons, we’ll consider some of the challenges in data collection and preprocessing that a practitioner should consider while building a machine-learning model. We will also review a number of examples of applications of supervised machine learning. You’ll also discover the reasons why supervised machine learning has been so successful, as well as some of the common challenges with using it. These lessons will discuss the most important applications of supervised machine learning including classification, prediction, and forecasting. You’ll also explore artificial neural networks, one of the most common methods used for supervised machine learning.
What You'll Learn
- Differentiate between data suitable for supervised machine learning and unsupervised machine learning
- Distinguish between styles of supervised machine learning such as classification and prediction
- Explain classification modeling
- Apply supervised learning to classification, prediction, and forecasting tasks
- Explore artificial neural networks as a common method for supervised machine learning
- Address challenges in data collection and preprocessing when building a machine-learning model
Key Takeaways
- Supervised machine learning is used in many everyday applications, including image captioning, forecasting, and machine translation.
- Different methods such as regression, neural networks, and decision trees can be applied to different kinds of data to solve various classification and prediction problems.
- All machine learning heavily relies on data, so practitioners must consider challenges in data collection and preprocessing when building a model.
- The most important applications of supervised machine learning include classification, prediction, and forecasting.
- Artificial neural networks are one of the most common methods used for supervised machine learning.
Frequently Asked Questions
What topics does this course cover?
The course covers supervised machine learning, classification, prediction, forecasting, artificial neural networks, and feature engineering.
What methods of supervised machine learning are discussed?
The lessons discuss methods including regression, neural networks, and decision trees, with artificial neural networks highlighted as one of the most common methods used for supervised machine learning.
What skills will I gain from this course?
You will build skills in automated machine learning, machine learning, machine learning algorithms, machine learning methods, machine learning model training, and supervised learning.
What are some real-world applications of supervised machine learning covered here?
The course reviews applications such as image captioning, forecasting, and machine translation, and focuses on classification, prediction, and forecasting.
Does the course address working with data?
Yes. Because all machine learning heavily relies on data, the lessons consider challenges in data collection and preprocessing that a practitioner should consider while building a machine-learning model.
