The course is part of these learning paths
In this course, you'll learn about Machine Learning and where it fits within the wider Artificial Intelligence (AI) field. The course proceeds with a formal definition of Machine Learning and continues on with explanations for the various machine learning and training techniques. We review both Supervised and Unsupervised learning, showcasing the main differences between each type of learning method. We review both Classification and Regression models, showcasing the main differences between each type of training model.
We provide a basic review of several of the most popular and commonly used machine learning algorithms including:
- Linear Regression
- Logistic Regression
- K Nearest Neighbour (KNN)
- Decision Tree
- Random Forest
- Support Vector Machines (SVM)
- Naïve Bayes
Finally, we’ll provide a basic-level introduction to Deep Learning and Deep Neural Networks, as a more specialised form of Machine Learning.
The intended audience for this course includes:
- Beginners starting out to the field of Machine Learning
- Anyone interested in understanding how Machine Learning works
By completing this course, you will:
- Understand what Machine Learning is and what it offers
- Understand the benefits of using the Machine Learning
- Understand business use cases and scenarios that can benefit from using the Machine Learning
- Understand the different Machine Learning training techniques
- Understand the difference between Supervised and Unsupervised training
- Understand the difference between Classification and Regression
- Become familiar with several of the commonly used and popular Machine Learning algorithms discussed
- Understand the basic principles behind Deep Learning and Deep Neural Networks
The following prerequisites will be both useful and helpful for this course:
- A background in statistics or probability
- Familiarity and understanding of computer algorithms
- Basic understanding of data analytics
The agenda for the remainder of this course is as follows:
- We’ll discuss what Machine Learning is and when and why you might consider using it
- We’ll discuss benefits and business use cases that have been empowered by leveraging Machine Learning
- We’ll breakdown machine learning into supervised and unsupervised training models
- We’ll discuss the differences between classification and regression techniques
- We’ll examine a set of commonly used and popular machine learning algorithms
- Finally, we’ll take an introductory look at deep learning and the concept of deep neural networks.
If you have thoughts or suggestions for this course, please contact Cloud Academy at firstname.lastname@example.org.
Welcome back. Before we finish, let's do a quick review of what we have learned. We gained an understanding of what Machine Learning is and what it offers. We gained an understanding of the benefits of using Machine Learning. We learned about the different business use cases and scenarios that have benefited from using Machine Learning. We learned about the difference between Supervised and Unsupervised training. We learned about the difference between Classification and Regression. We reviewed several of the commonly used and popular Machine Learning algorithms, and finally, we reviewed the basic principles behind Deep Learning and Deep Neural Networks as a specialized form of Machine Learning.
Thank you for your participation. I do hope you have enjoyed this course on Machine Learning concepts. Feel free to send any feedback and or questions to email@example.com, or, alternatively, you can always get in touch with us here at Cloud Academy using the community forum with one of our Cloud experts who will reply to your question.
Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.
He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, Azure, GCP), Security, Kubernetes, and Machine Learning.
Jeremy holds professional certifications for AWS, Azure, GCP, Terraform, Kubernetes (CKA, CKAD, CKS).