Course Description
These lessons will introduce classification modeling and its problem space within supervised machine learning. This will include learning and recognizing the difference between classification and prediction, and how classification modeling fits in the larger field of machine learning. You will learn how to approach classification problems, and you will be introduced to the typical classification model pipeline.
Once you understand the classification model pipeline, you will learn how to begin the initial steps of the data collection. Finally, you will learn practical tips on how to manage data collection and information on preprocessing for a classification model.
What You'll Learn
- Distinguish classification from prediction and how classification fits within the larger field of machine learning
- Recognize basic classification concepts within supervised machine learning
- Apply the typical classification model pipeline to approach classification problems
- Begin the initial steps of data collection for a classification model
- Manage data collection and preprocess data for a classification model
Key Takeaways
- Classification modeling sits within the problem space of supervised machine learning.
- Classification and prediction are distinct, and the course teaches how to tell them apart.
- A typical classification model follows a pipeline, and understanding it helps you approach classification problems.
- Data collection and preprocessing are important initial steps in building a classification model.
- The course covers practical tips for managing data collection for classification.
Frequently Asked Questions
What does this course cover?
It introduces classification modeling and its problem space within supervised machine learning, including the difference between classification and prediction, how classification fits in the larger field of machine learning, the typical classification model pipeline, and the initial steps of data collection and preprocessing.
What lessons are included?
The lessons are Supervised Machine Learning, Basic Classification Concepts, Classification vs. Prediction, The Classification Model Pipeline, Data Collection, and Data Preprocessing.
What skills will I gain?
You will build skills in Data Classification, Machine Learning, Machine Learning Model Training, Machine Learning Model Monitoring and Evaluation, Statistical Classification, and Supervised Learning.
Does the course explain the difference between classification and prediction?
Yes. The course teaches how to learn and recognize the difference between classification and prediction and how classification modeling fits in the larger field of machine learning.
Does the course address data collection and preprocessing?
Yes. After covering the classification model pipeline, it teaches the initial steps of data collection, plus practical tips on managing data collection and information on preprocessing for a classification model.
