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Course Description
Supervised machine learning can be used to model language and predict whether or not an email is spam. Modelling language is a particularly challenging domain. In these lessons, we’ll discuss how to convert language into usable data using particularities of language. We’ll also explore why every language is different and requires a unique set of rules.
Businesses of all types can use customer segmentation to better understand their market needs and to build new products that address the unique behaviors and needs of customer groups. In these lessons, we will discuss the use of unsupervised learning to create a customer segmentation. We’ll also cover practical considerations of customer segmentations.
Finally, we will review neural-network based image classification. Because of the proliferation of image data, machine learning methods to classify images have gained popularity. Convolutional neural networks, a specific type of neural network created to address some of the difficulties in working with images, can provide an effective method to solve some of the most challenging problems in modern machine learning.
What You'll Learn
- Convert language into usable data and apply supervised learning to detect whether an email is spam
- Build customer segmentations using unsupervised learning to understand market needs and customer groups
- Classify images using neural networks, including convolutional neural networks built to address image-data challenges
- Identify the difficulties in working with different types of data
- Explain the advantages of specific pre-processing and modelling methods
- Define the business case for various styles of machine learning
Key Takeaways
- Supervised machine learning can be used to model language and predict whether an email is spam.
- Modelling language is a challenging domain because every language is different and requires a unique set of rules.
- Businesses of all types can use unsupervised learning to create customer segmentations that reveal market needs and inform new products addressing the behaviors and needs of customer groups.
- Convolutional neural networks are a specific type of neural network created to address some of the difficulties in working with images.
- The proliferation of image data has increased the popularity of machine learning methods to classify images.
Frequently Asked Questions
What topics does this course cover?
The course covers three real-world applications of machine learning across three lessons: Spam Detection, Customer Segmentation, and Image Classification. It addresses converting language into usable data for supervised spam detection, using unsupervised learning to create customer segmentations, and neural-network based image classification including convolutional neural networks.
What will I be able to do after taking this course?
You will be able to identify the difficulties in working with different types of data, explain the advantages of specific pre-processing and modelling methods, and define the business case for various styles of machine learning.
Who can benefit from the customer segmentation portion of this course?
Businesses of all types can benefit, as the course discusses how customer segmentation helps them better understand their market needs and build new products that address the unique behaviors and needs of customer groups.
What skills does this course focus on?
The course focuses on applications of artificial intelligence, automated machine learning, machine learning, machine learning algorithms, machine learning methods, and supervised learning.
Why is modelling language described as challenging in this course?
The course explains that modelling language is a particularly challenging domain because every language is different and requires a unique set of rules when converting language into usable data.
