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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. There are several types of machine learning, including supervised machine learning and unsupervised machine learning, and they can be used to solve very different kinds of problems.
In these lessons, you’ll learn about the history of machine learning and the difference between machine learning and other methods for problem solving. We will discuss the reasons why machine learning has been so successful in solving some of the most difficult problems like machine translation and image recognition. We’ll review the importance of data collection and common best practices in data collection including issues such as missing data, anomalous data, and falsified data. Finally, we’ll discuss the typical machine learning pipeline and the common stages that a business would go through in order to create and deploy a machine learning model.
What You'll Learn
- Distinguish between supervised and unsupervised machine learning
- Explain the historical impact of machine learning and why it has succeeded at hard problems like machine translation and image recognition
- Describe the kinds of problems machine learning can be applied to, such as image classification, spam detection, customer segmentation, and prediction
- Demonstrate the advantage of using machine learning compared to other problem-solving methods
- Apply best practices in data collection, including handling missing, anomalous, and falsified data
- Outline the typical machine learning pipeline and the common stages a business goes through to create and deploy a model
Key Takeaways
- Machine learning is a branch of data science used in many contexts, including classifying images, detecting spam, labelling images, segmenting customers, and making predictions.
- There are several types of machine learning, including supervised and unsupervised machine learning, which are suited to solving different kinds of problems.
- Machine learning has been especially successful at solving difficult problems such as machine translation and image recognition.
- Data collection is important, and common best practices address issues such as missing data, anomalous data, and falsified data.
- Creating and deploying a machine learning model follows a typical pipeline with common stages a business goes through.
Frequently Asked Questions
What topics does this course cover?
The course covers an overview of machine learning, the types of machine learning (including supervised and unsupervised), data collection, and the machine learning pipeline. It also discusses the history of machine learning and the difference between machine learning and other problem-solving methods.
What is the difference between supervised and unsupervised machine learning in this course?
The course teaches you to distinguish between unsupervised and supervised machine learning, explaining that they can be used to solve very different kinds of problems.
What kinds of problems can machine learning solve according to this course?
The course describes problems such as classifying images, detecting spam, labelling images, segmenting customers, and making predictions, and notes machine learning's success with difficult problems like machine translation and image recognition.
Does the course address data quality?
Yes. It reviews the importance of data collection and common best practices, including issues such as missing data, anomalous data, and falsified data.
What skills will I gain from this course?
The course develops skills in machine learning, machine learning algorithms, machine learning methods, machine learning model training, automated machine learning, and decision tree learning.
