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Start Your Free Trial Today >>Elevate Your Machine Learning Expertise: Ensemble Models and Automated Pipelines on AWS
Are you ready to push the boundaries of your Machine Learning knowledge and skills? Machine Learning Level 3 is your next step towards mastering advanced techniques and automating real-world solutions with AWS. This course is designed for those who want to dive deep into ensemble modeling and build robust Machine Learning pipelines using Amazon Web Services.
Course Overview:
In this course, youāll explore the powerful world of ensemble models and learn how to deploy these models using AWS services such as SageMaker, Lambda, and S3. From setting up your AWS environment to automating predictions, this course provides a comprehensive guide to modern Machine Learning practices. Whether youāre aiming to enhance your professional toolkit or optimize real-world applications, this course will equip you with the expertise to achieve your goals.
What You Will Learn:
1. AWS Setup and Management:
Start by setting up your AWS Free Tier Account and configuring billing alarms to monitor costs effectively.
Download the complete ML Series Curriculum to guide your learning journey.
2. Building Ensemble Models with Amazon SageMaker Canvas:
Set up your SageMaker domain and get an overview of SageMaker Canvas, Amazonās no-code Machine Learning platform.
Learn how to import datasets, build, analyze, and predict with ensemble models tailored for both regression and classification tasks.
3. Advanced Ensemble Techniques with SageMaker Studio Classic:
Delve into the intricacies of SageMaker Studio Classic to build and analyze ensemble models for regression and classification.
Understand the importance of S3 in managing datasets and predictions in your Machine Learning workflows.
4. Automating Machine Learning Pipelines with AWS Lambda:
Gain hands-on experience in setting up a full-fledged ML pipeline using AWS Lambda, from data loading and model training to inference and automation.
Address real-world challenges like handling missing libraries and managing environment variables effectively.
Automate your ML processes with Lambda triggers and destinations, culminating in a fully automated Machine Learning solution.
5. Real-World Application and Optimization:
Learn to evaluate and optimize your models, ensuring they are ready for deployment in real-world scenarios.
By the end of this course, youāll be able to predict and classify data with high accuracy using ensemble models, and seamlessly integrate these models into automated pipelines.
Why Choose Machine Learning Level 3?
Advanced Knowledge: Dive deep into ensemble modeling and AWS-driven automation, expanding your Machine Learning expertise.
Practical Applications: Engage with real-world case studies and hands-on projects, ensuring that you can apply your skills immediately.
Expert Guidance: Benefit from the insights and experiences of industry leaders who are at the forefront of Machine Learning and AI.
Flexible Learning: Whether youāre enhancing your current skills or pivoting your career, this course is structured to fit your pace and learning style.
Community and Support: Join a thriving community of learners and receive support from experts who are dedicated to your success.
Build Your Future in Machine Learning:
This course is more than just a learning experienceāitās a transformative journey that will empower you to tackle complex Machine Learning challenges with confidence. From mastering ensemble models to automating pipelines, Machine Learning Level 3 is your gateway to becoming a leader in the field.
Enroll today and take your Machine Learning expertise to the next level with AWS!
$35
/ MonthAccelerate your Career and boost your Earning Potential with our Expert Instructors & Community! What Youāll Unlock: - š 40+ Courses (over 200 hours!) - š 17 Specialized Career Paths - š Quizzes and Practice Activities - š Certificates for Courses & Career Paths - š Prizes for Learning - š§Ŗ Weekly Live Labs (plus recordings!) - šÆ Monthly Missions to practice even more - š¼ Monthly Career Booster Events - š¬ Full access to the SDS Community - ā” Monthly Speed Networking - š„ Monthly Resume Clinics - š„ Group Mentorship Program "In just a few months of learning at SDS, I landed a Data Analyst job!" ā Sanaz Afshar, California
$157
/ MonthAccelerate your Career and boost your Earning Potential with our Expert Instructors & Community! What Youāll Unlock: - š 40+ Courses (over 200 hours!) - š 17 Specialized Career Paths - š Quizzes and Practice Activities - š Certificates for Courses & Career Paths - š Prizes for Learning - š§Ŗ Weekly Live Labs (plus recordings!) - šÆ Monthly Missions to practice even more - š¼ Monthly Career Booster Events - š¬ Full access to the SDS Community - ā” Monthly Speed Networking - š„ Monthly Resume Clinics - š„ Group Mentorship Program Plus, Pro Plan perks just for you: - š A Personalized Career Path built around your goals - š§āš« 1-on-1 Mentoring Sessions every month - āļø Personalized Resume Reviews "In just a few months of learning at SDS, I landed a Data Analyst job!" ā Sanaz Afshar, California
Course content
Disclaimer
Set Up Your SageMaker Domain
06:23
Overview of SageMaker Canvas
04:38
Import the Datasets in Canvas
06:53
Ensemble Models: Stacking
07:49
Build Your Ensemble Model for Regression
05:59
Analyze Your Ensemble Model for Regression
04:59
Predict (Batch Prediction and Single Prediction) with Your Ensemble Model for Regression
04:47
Build Your Ensemble Model for Classification
07:48
Analyze Your Ensemble Model for Classification
04:34
Predict (Batch Prediction and Single Prediction) with Your Ensemble Model for Classification
05:54
Section Cleanup
07:50
Introduction to Our ML Pipeline on AWS Lambda
04:56
Intro to IAM
02:03
IAM Policies
06:58
IAM Roles
02:33
Create an IAM Role for AWS Lambda
05:11
What is S3? (Skip If You Already Watched the Previous Section on SageMaker Studio Classic)
03:50
Create the S3 Data Bucket and S3 Model Bucket
07:42
AWS Lambda
04:23
Create the ML Pipeline Lambda Function
04:59
The Missing Libraries Problem
03:52
The Missing Libraries Solution: Lambda Layers
07:34
Define the Environment Variables
08:28
Load the Dataset From the S3 Data Bucket
09:59
Split the Dataset Into the Training Set and the Test Set
05:54
Extract the Features and Target from the Training Set or Test Set
06:44
Train a Random Forest Model on the Training Set
05:47
Save the Random Forest Model in the S3 Model Bucket
08:23
Evaluate the Random Forest Model on the Test Set
04:22
Store All the Features of a New Given Wine in a List
06:59
Predict the Quality of That New Wine Using the Pre-trained Model Saved in Our S3 Model Bucket
09:29
Configure the Lambda Handler for Training and Deploy the Code
09:24
Intermediate Demo (Manual Test)
02:17
Configure the Lambda Handler for Inference and Make a New Prediction
05:11
Automate the ML Pipeline with the Lambda Triggers and Destinations
09:25
Final Demo (Automation Test)
06:39
Final Cleanup
07:34
Final Cost
02:56