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Course Introduction

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Introduction to Machine Learning on AWS
course-steps 4 certification 1 lab-steps 2
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Overview
DifficultyBeginner
Duration48m
Students523

Description

Overview

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)
  • 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 course includes:

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

Learning Objectives

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

Pre-requisites

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

Course Agenda

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 of 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 course, please contact Cloud Academy at support@cloudacademy.com.

Transcript

- [Instructor] 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 commonly 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.

About the Author

Students5749
Labs6
Courses41
Learning paths8

Jeremy is a Cloud Researcher and Trainer at Cloud Academy where he specializes in developing technical training documentation for security, AI, and machine learning for both AWS and GCP cloud platforms.

He has a strong background in development and coding, and has been hacking with various languages, frameworks, and systems for the past 20+ years.

In recent times, Jeremy has been focused on Cloud, Security, AI, Machine Learning, DevOps, Infrastructure as Code, and CICD.

Jeremy holds professional certifications for both AWS and GCP platforms.