Intro to Machine Learning in AWS for Beginners

Name: Intro to Machine Learning in AWS for Beginners
Rating: 4.2 (498 reviews)

Build, Train, Test Your First ML Model in SageMaker. Learn AWS Marketplace, Canvas, GroundTruth, SageMaker Studio.

Created byProf. Ryan Ahmed, PhD, MBA | 1M+ Students | #1 Best Selling AI Courses

Last updated 4/2024

English

English [Auto],

What you'll learn

Understand the difference between Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS) and Deep Learning (DL)
Build, Train, and Test a Simple Machine Learning Model Using AWS SageMaker Canvas
Leverage a Yolo V3 Object Detection Algorithm available on the AWS Marketplace
Navigate Through the AWS Management Console
Write Your First Code in SageMaker Studio

Course content

4 sections • 13 lectures • 1h 46m total length

What is AWS? & Benefits of Cloud Computing8:42
Explore Amazon Web Services, the world's top cloud platform, offering compute, storage, databases, analytics, IoT, and AI and ML services with pay-as-you-go pricing for agile, scalable deployment.
AWS SignUp & AWS Management Console Tour10:07
Sign up for aws, verify email, and navigate the management console to explore services like SageMaker and s3, plus free tier and support options.

Introduction to SageMaker11:48
Explore Amazon SageMaker basics, including SageMaker Canvas for no-code modeling, writing your first code in a Jupyter Notebook, and training, deploying, and labeling data.
AWS SageMaker Demo Part 1 – Notebook Instance12:05
Master Amazon SageMaker basics for training and deploying models using S3 data and diverse training options. Start a notebook instance to run training jobs and prepare for deployment.
AWS SageMaker Demo Part 2 – Write Your First Code & Terminate Instances10:01
Learn to write your first code in AWS SageMaker notebooks, run Python 3 cells in Jupyter, and terminate the notebook instance after the hands-on demo.
AWS SageMaker Demo Part 3 – AWS Marketplace Tutorial and Demo7:45
Explore AWS SageMaker marketplace model packages with a pre-trained YOLO v3 detector, upload images, and view bounding boxes with inference confidence scores.
AWS SageMaker Demo Part 4 – SageMaker Studio13:46
Explore Amazon SageMaker Studio, a web-based interface to build, train, and deploy AI models, manage notebooks, data, and experiments in one place.

SageMaker Canvas 1018:24
Explore Amazon SageMaker Canvas to build, train, and test machine learning models without writing code; import data, run regression, generate predictions, and export to SageMaker Studio.
Upload your data to S36:08
Create an S3 bucket, upload your CSV data, and link it to Amazon SageMaker Canvas to build machine learning models without coding.
Train & Evaluate Your Model in SageMaker Canvas7:03
Train and evaluate a regression model in SageMaker Canvas using a Celsius to Fahrenheit dataset uploaded to S3, then predict values with no code.

Final Capstone Project Overview1:56
Predict salaries from experience by training a simple model in Amazon SageMaker Canvas, upload salary data to S3, plot the distribution, and test the model with three years of experience.
Final Capstone Project Solution7:32
Upload salary data to S3, import into stage maker canvas, and build a salary regression model using years of experience to predict salary, with metrics like r squared and rmse.
Shutdown Canvas and Cleanup Resources0:48
Log out to shut down Amazon SageMaker Canvas and stop session billing. It takes a few minutes to start a new session. Close the tab and relaunch SageMaker Canvas later.

Requirements

Basic knowledge in AWS
Basic knowledge in machine learning

Description

Machine Learning is the future one of the top tech fields to be in right now!

ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology.

The field is exploding with opportunities and career prospects.

This introductory course is for absolute beginners, students will learn:

Key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) and CloudWatch,
The benefits of cloud computing and what’s included in the AWS Free Tier Package
How to setup a brand-new account in AWS and navigate through the AWS Management Console
The fundamentals of Machine Learning and understand the difference between Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS) and Deep Learning (DL)
List the key components to build any machine learning models including data, model, and compute
Learn the fundamentals of Amazon SageMaker, SageMaker Components, training options offered by SageMaker including built-in algorithms, AWS Marketplace, and customized ML algorithms
Cover AWS SageMaker Studio and learn the difference between AWS SageMaker JumpStart, SageMaker Autopilot and SageMaker Data Wrangler
Learn how to write our first code in the cloud using Jupyter Notebooks.
We will then have a tutorial covering AWS Marketplace object detection algorithms such as Yolo V3
Learn how to train our first machine learning model using the brand-new AWS SageMaker Canvas without writing any code!

Who this course is for:

Absolute Beginners who want to break into machine learning in AWS
Beginners Data Scientists wanting to advance their careers by Learning AWS and Machine Learning
Tech enthusiasts who are passionate and new to Machine Learning and want to gain basic knowledge in AWS & Machine Learning

Intro to Machine Learning in AWS for Beginners

What you'll learn

Explore related topics

Course content

Introduction to AWS, SignUp & Management Console Tour2 lectures • 19min

Machine Learning in AWS & SageMaker5 lectures • 55min

Build, Train and Test your First Machine Learning Model!3 lectures • 22min

Final Capstone Project3 lectures • 10min

Requirements

Description

Who this course is for: