AWS Machine Learning Online Course (10 credits)


Price:
Sale price€199.50

Cloud machine learning services are a key part of the modern computing landscape, offering organizations the opportunity to utilize machine learning solutions. The biggest attraction to such services is that customers can start using and building machine learning systems without installing specific software or providing servers. Besides, accessing machine learning services via the cloud is efficient in terms of time and cost. Moreover, they make machine learning frameworks easy to implement, deploy and configure. AWS Machine Learning service supports various machine learning algorithms, including supervised learning, unsupervised learning, and deep learning. It supports both Python and R programming languages for writing codes. It offers developers and data scientists a variety of resources to help build their knowledge of machine learning in the AWS Cloud.

TechClass AWS Machine Learning online course will teach you how to get started with AWS Machine Learning and focus on standardized approaches to data analytics and machine learning implementation with Python programming language. By the end of this course, you will learn to build, train, deploy, automate, manage, and track enterprise-grade machine learning models from scratch in a simplified way using powerful AWS tools.

Learning outcomes

  • Get familiar with the considerations regarding machine learning implementation and the concept of Machine Learning as a Service (MLaaS)
  • Get familiar with AWS's different AI and Machine Learning services, including AWS SageMaker, AWS AI Services, etc.
  • Learn how to create a free-tier AWS account and to set up compute instances
  • Get familiar with Amazon S3 and learn how to use the primary functionalities of S3
  • Get familiar with the concept of AutoML as well as the SageMaker Autopilot for using AutoML capabilities
  • Gain hands-on experience with using SageMaker Autopilot and job profiles to explore trained models, choose the best model, get inference from the model, and deploy it to the cloud
  • Learn how to use SageMaker Autopilot for tuning models' hyperparameters
  • Get familiar with SageMaker Studio and learn how to create Notebooks in Studio and use the basic functionalities of the Studio
  • Learn how to use SageMaker Python SDK to facilitate training and deploying machine learning models on top of SageMaker
  • Get familiar with SageMaker Pipelines, its components, and use cases
  • Learn how to create simple pipelines using SageMaker Pipelines

Table of contents

Chapter 1: Beginning with This Course

  • 1.1. Our Approach in This Course
  • 1.2. About TechClass AI Department
  • 1.3. Your Expectations, Goals, and Knowledge

Chapter 2: Introduction

  • 2.1. Basics of Machine Learning
  • 2.2. Importance of Data
  • 2.3. Types of Machine Learning
  • 2.4. Machine Learning Pipeline
  • 2.5. Machine Learning Implementation
  • 2.6. Why Cloud-based Services?
  • 2.7. Amazon Web Services (AWS)

Chapter 3: AWS Machine Learning

  • 3.1. What is AWS Machine Learning Service?
  • 3.2. AWS AI Services
  • 3.3. Amazon SageMaker
  • 3.4. SageMaker Studio
  • 3.5. SageMaker Autopilot
  • 3.6. SageMaker Studio Notebooks
  • 3.7. SageMaker Pipelines
  • 3.8. Quiz

Chapter 4: Getting Started with AWS

  • 4.1. AWS Pricing
  • 4.2. Compute Instance Types
  • 4.3. AWS Free Tier Account
  • 4.4. Create a Free Tier Account
  • 4.5. A Tour of AWS Interface
  • 4.6. IAM Service
  • 4.7. Getting Started with AWS: Exercise

Chapter 5: Amazon S3

  • 5.1. Introduction to S3
  • 5.2. Set Up Amazon S3
  • 5.3. Create a Bucket
  • 5.4. Upload, Download, and Copy an Object
  • 5.5. Properties for an S3 Bucket
  • 5.6. Bucket Permissions
  • 5.7. Accessing a Bucket
  • 5.8. Storage Tier and Data Lifecycle
  • 5.9. Delete an Object or a Bucket
  • 5.10. Amazon S3: Exercise
  • 5.11. Amazon S3: Quiz

Chapter 6: SageMaker Autopilot

  • 6.1. Set up Amazon SageMaker Studio
  • 6.2. Getting Started with SageMaker Autopilot
  • 6.3. Create a SageMaker Autopilot Experiment
  • 6.4. Problem Types
  • 6.5. Model Support and Validation
  • 6.6. Model Deployment
  • 6.7. Models Generated by SageMaker Autopilot
  • 6.8. Notebooks Generated by SageMaker Autopilot
  • 6.9. Configure Inference Output
  • 6.10. SageMaker Autopilot Quotas

Chapter 7: SageMaker Autopilot: Practical Example

  • 7.1. Open SageMaker Studio
  • 7.2. Load the Dataset
  • 7.3. Create an Experiment
  • 7.4. Data Exploration Notebook
  • 7.5. Explore the Experiment
  • 7.6. Trials and Job Profiles
  • 7.7. Deploy the Best Model
  • 7.8. Predict with Your Model
  • 7.9. SageMaker Console: Training and Tuning Jobs
  • 7.10. SageMaker Console: Endpoints
  • 7.11. Project 1

Chapter 8: SageMaker Studio Notebooks

  • 8.1. Studio Notebooks vs. Instance-based Notebooks
  • 8.2. Create a Notebook in Studio
  • 8.3. SageMaker Python SDK
  • 8.4. Import Libraries
  • 8.5. Load Data
  • 8.6. Data Preprocessing
  • 8.7. Model Building with Scikit-learn
  • 8.8. Choose an Algorithm Implementation
  • 8.9. Upload Data on S3 Buckets
  • 8.10. Create a Training Job
  • 8.11. Start Training
  • 8.12. Deploy the Model
  • 8.13. Evaluate Model Performance
  • 8.14. Terminate the Resources
  • 8.15. Script Mode
  • 8.16. Project 2

Chapter 9: SageMaker Pipelines

  • 9.1. Machine Learning Workflow Challenges
  • 9.2. Why SageMaker Pipelines?
  • 9.3. SageMaker Pipelines Components
  • 9.4. Create a Project
  • 9.5. Clone Repositories
  • 9.6. Look over the Repositories
  • 9.7. Preprocess Script
  • 9.8. Evaluate Script
  • 9.9. Pipeline Script
  • 9.10. Pipeline Execution
  • 9.11. Approve the Model for Deployment
  • 9.12. Project 3

Chapter 10: Final Tasks

  • 10.1. Self-study Essay
  • 10.2. Congrats! You did it!

Brochure

Payment & Security

Payment methods

American Express Apple Pay Mastercard PayPal Visa

Your payment information is processed securely. We do not store credit card details nor have access to your credit card information.


Security

Customer Reviews

No reviews yet
0%
(0)
0%
(0)
0%
(0)
0%
(0)
0%
(0)

You may also like

Recently viewed