Fundamentals of Machine Learning with AWS
Are you curious about Machine Learning and don’t know where to begin? You’ve come to the right place. In this course, we will teach you the concepts behind Machine Learning (ML) and Artificial Intelligence (AI). We’ll cover a brief history of ML and AI and then dive into the concepts, taxonomy and vocabulary so that you’ll be able to continue your education, familiar with the lexicon that used in data analysis. We will frame these concepts in a pragmatic manner by reviewing using Amazon’s rich and powerful ML and AI platform built on the highly scalability and available Amazon Web Services cloud platform.
Are you wondering when it’s appropriate to use Machine Learning? To address this question, we will review specific usage scenarios for Machine Learning so that you can not only recognize situations where Machine Learning makes sense, but also be inspired to create your own Machine Learning models for both professional and personal use.
This course will also provide a high-level overview of the common algorithms and models used with Machine Learning, including those provided out of the box by AWS. By understanding the family of models, you’ll be better equipped to determine appropriate model to use for your business scenarios.
Although it will be helpful to have some prior experience with technology and AWS, it isn’t required to take this course. You also do not have to have background in software development, data science or data analytics. This course makes Machine Learning accessible to nearly anyone.
What you'll learn-and how you can apply it
- Understand key Machine Learning concepts and taxonomy
- Walk away with an understanding of the AI and ML services offered by AWS
- Learn about the different types of Machine Learning
- Review the typical workflows used with Machine Learning
- Receive a high level understanding of the families of Machine Learning algorithms
- Receive a basic understanding of the most popular algorithms as well as the business cases that they can address
- Discover business scenarios where Machine Learning can be applied
This training course is for you because...
- You have a passion for the latest advancements in Machine Learning
- You want to learn the fundamentals of Machine Learning including concepts, taxonomy, workflow and tools
- You are new to Machine Learning and AWS and wish to ensure you are current on the latest developments with ML and AI on AWS
- Ideally, you have some experience with AWS, but it’s not required.
- For an introduction to cloud computing & AWS:Amazon Web Services (AWS) LiveLessons, 2nd Edition
About your instructor
Asli Bilgin is an award-winning cloud computing executive who has over two decades of experience working for companies such as Dell, Microsoft and Amazon. Her firm, Nokta Consulting, specializes in IT transformation and modernization leveraging disruptive technologies such as cloud computing, machine learning and blockchain. At Amazon, Asli created, launched and ran the global Software as a Service program At Microsoft, she led the cloud and web strategy for 80 countries in the Middle East & Africa, based out of Dubai. Asli is a passionate advocate for the impact technology can make on people’s lives.. Asli was the architect behind the LEGO and Microsoft partnership effort for WomenBuild, a program to promote compute science as an art and science, specifically for girls and women.
The timeframes are only estimates and may vary according to how the class is progressing
Segment 1: High Level Overview of AI & ML (20 mins)
- What is Artificial Intelligence?
- What is Machine Learning?
- What is Deep Learning?
- Evolution of Artificial Intelligence, Machine Learning and Deep Learning
Segment 2: Which Use Cases Can Machine Learning Address? (30 mins)
- Personal Productivity
- Product Management
Segment 3: How does Machine Learning Work? (20 mins)
- Workflow- Build, Train Deploy
- Supervised Learning
- Unsupervised Learning
- Break – 10 min
Segment 4: How does Deep Learning Work? (15 mins)
- High Level Overview
- Key Concepts and Taxonomy
- Neural Networks
Segment 5: Amazon Artificial Intelligence and Machine Learning Overview (30 mins)
- High Level Overview of AWS
- AWS ML & AI: Platform Services
- AWS ML & AI: Application Services
- AWS ML & AI: Foundational Services
Segment 6: Machine Learning Concepts and Taxonomy (40 mins)
- The Big Picture
- What is Input Data?
- What are Features?
- What is a Target?
- What are Observations?
- What is Labeled Data?
- What is Unlabeled Data?
- What is Ground Truth?
- What are Hyperparameters?
- Break – 10 mins
Segment 7: How to Use Machine Learning? (20 mins)
- Preparing Data
- Model Training
- Refining Models
- Conducting Predictions
Segment 8: Selecting the Appropriate Data (20 mins)
- What is the Best Kind of Data?
- Academic Sources for Data
- Commercial Sources for Data
- Data Sources Supported by AWS
Segment 9: Machine Learning Concepts and Taxonomy (25 mins)
- Machine Learning Algorithm Families
- Common Algorithms
- Use Cases for Popular Algorithms
- Built in AWS Algorithms Provided by AWS
- Break – 10 min
Segment 10: Predictions (15 mins)
- What are Predictions or Inferences?
- How do Predictions Work?
- What are the Types of Predictions?
Segment 11: A Closer Look at AWS ML Services (15 mins)
- Amazon SageMaker High Level Overview
- Key Components of Amazon SageMaker
- How to Get Started with Amazon SageMaker
Segment 12: Call to Action & Conclusion (15 mins)
- Next Steps