Hello and welcome to this Cloud Academy course on Machine Learning Concepts. In this first lecture, we'll cover our course agenda, intended audience, learning objectives, and course prerequisites. Before we start, I would like to introduce myself. My name is Jeremy Cook. I'm one of the trainer's here at Cloud Academy, specializing in AWS. Feel free to connect with either myself, or the wider team here at Cloud Academy regarding anything about this course. You can email us at support@cloudacademy.com or alternatively, our online community forum is available for your feedback.

This training course begins with an introduction of Machine Learning and where it fits within the wider artificial intelligence field. The course proceeds with a formal definition of Machine Learning and continues on with explanations for the various machine learning and training techniques such as supervised and unsupervised training.

We then review several of the most popular machine learning algorithms. We'll then briefly discuss some reason as to where and when you might consider using machine learning within your own applications. The intended audience for this course includes: beginners to the field of machine learning. And 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 machine learning. Understand business use cases and scenarios that benefit from using machine learning. Understand the different machine learning training techniques. Understand the difference between supervised and unsupervised training. Understand the difference between classification and regression. You'll become familiar with several of the commonly used and popular machine learning algorithms. And finally to understand the basic principles behind deep learning and deep neural networks.

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 break down machine learning into supervised and unsupervised training models. We'll discuss the differences between classification and regression. We'll examine a set of common and popular machine learning algorithms. And finally, we'll take an introductory look at deep learning and the concept of 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. And a basic understanding of data analytics. Okay, the course introduction has now been completed. Go ahead and close this lecture. And we'll see you shortly in the next one where we'll begin discussing machine learning.