Unsupervised Learning

Beginner
6m 29s
7,224
4.6/5

In this lesson, you'll learn about Machine Learning and where it fits within the wider Artificial Intelligence (AI) field. The lesson 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)
  • K-Means
  • 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.

Intended Audience

The intended audience for this lesson includes:

  • Beginners starting out to the field of Machine Learning
  • Anyone interested in understanding how Machine Learning works

Learning Objectives

By completing this lesson, 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

Pre-requisites

The following prerequisites will be both useful and helpful for this lesson:

  • A background in statistics or probability
  • Familiarity and understanding of computer algorithms
  • Basic understanding of data analytics

Lesson Agenda

The agenda for the remainder of this lesson 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.

Feedback

If you have thoughts or suggestions for this lesson, please contact Cloud Academy at support@cloudacademy.com.

About the Author
Avatar
Jeremy Cook, opens in a new tab
Content Lead Architect
Students
162,238
Labs
84
Courses
106
Learning paths
214

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).

Covered Topics