Course Description
In this course, you’ll learn the basics of machine learning. You’ll be introduced to machine learning and some of the related concepts and topics. You’ll understand the various types and categories of machine learning. This course will also cover how to implement machine learning models in Python by utilizing the scikit-learn package. Both supervised and unsupervised machine learning methods will be covered and demonstrated. All of the concepts and theories will be tied together through examples as well as the discussion of practical applications, challenges, and key considerations. By the end of this course, you will recognize multiple types of machine learning processes as well as their benefits and how they can apply to real-world scenarios.
What You'll Learn
- Define what machine learning is and how it is used across real-world scenarios
- Distinguish the various types and categories of machine learning models and algorithms
- Apply supervised machine learning methods, including linear and logistic regression, support vector machines, decision trees and random forests, and neural networks
- Apply unsupervised machine learning methods, including clustering, Gaussian mixture models, and manifold learning
- Implement machine learning models in Python using the scikit-learn package within JupyterLab
- Evaluate key considerations, challenges, and ethical considerations before and during machine learning projects
Key Takeaways
- Machine learning is introduced from the basics, covering related concepts and topics along with its types and categories.
- The course covers both supervised and unsupervised machine learning methods, demonstrating each with examples.
- Models are implemented in Python using the scikit-learn package, with work demonstrated in JupyterLab.
- Concepts are tied together through practical applications, challenges, and key considerations, including ethical considerations.
- By the end, learners can recognize multiple types of machine learning processes, their benefits, and how they apply to real-world scenarios.
Frequently Asked Questions
What will I learn in this Machine Learning course?
You will learn the basics of machine learning, including what it is and how it is used, the various types of machine learning models and algorithms, supervised machine learning, unsupervised machine learning, and key things to consider before and during machine learning projects and tasks.
Does this course cover both supervised and unsupervised machine learning?
Yes. Both supervised and unsupervised machine learning methods are covered and demonstrated, including topics such as classification vs. regression, linear and logistic regression, support vector machines, decision trees and random forests, neural networks, clustering, Gaussian mixture models, and manifold learning.
What tools or programming language does the course use?
The course covers how to implement machine learning models in Python by utilizing the scikit-learn package, and it includes an introduction to JupyterLab.
What topics beyond algorithms does the course address?
Alongside the algorithms, the course discusses practical applications, challenges, ethical considerations, the importance of data, model selection, and other key considerations.
What will I be able to do after completing this course?
By the end of the course, you will recognize multiple types of machine learning processes as well as their benefits and how they can apply to real-world scenarios.
